Browse Topic: Drive cycles

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Construction and Verification of Munich EV Driving Cycle Considering Road Slope2026-99-08697/30/2026
The driving cycle is the basic model of certification of vehicle fuel consumption and emissions, or calibration of powertrains. Standard regulatory driving cycles, such as WLTC, in general assume flat roads during their generation and fail to take into account the strong effect that road gradients have on vehicle operation and driving energy consumption. Such a shortcoming, then, leads to gross mismatches in adaptability when used for urban environments with typical hilly topography. To solve this problem, in this paper, we proposed a method for building driving cycles that consider the impact of slope with actual driving data. Initially, high-precision onboard data collectors were used to generate a total sum of 21, 350 km of driving data from the Munich area, thus creating a diversified driving data set with details such as vehicle speed, slope, and environmental information. Subsequently, joint probability distributions of “speed-acceleration” and “slope-slope change rate” are proposed by using the Micro-trip Method, and a novel chi-squared test algorithm is used to obtain a higher fidelity of urban driving cycle representative of typical conditions. Results of the driving cycle results show that the driving cycle built was close to the actual kinematics, indicating a deviation of less than 5%, and can capture the average uphill characteristic of 1.7%, which is quite well represented. Finally, in fact, validation of whole vehicle environmental chamber tests further demonstrates that the energy consumption prediction error of the developed driving cycle is just 2.2%, much lower than 19.1% error of WLTC. It highlights the importance of considering slope parameters in improving the accuracy of energy consumption calibration for an EV operating on complex slope terrains. Furthermore, it underscores that converting the real-world driving data into lab-based driving cycles can reduce the cost and time of actual road tests for Chinese companies going to the overseas markets, thereby offering support for the international marketing strategy of a global database.
Tian, Lichen, Jiang, Ping, Gao, Wang, Liang, Yongkai, Ma, Kunqi, Yu, Hanzhengnan
Co-optimization of Energy Efficiency and Drivability for Dual-Motor Electric Commercial Vehicles: A Topology-Aware Hierarchical Approach02-19-04-00267/29/2026
Dual-motor architectures provide additional operating degrees of freedom for electric commercial vehicles (ECVs), but the integration of automated manual transmissions (AMTs) introduces torque discontinuities during gear-related mode transitions. Existing energy management strategies usually focus on steady-state efficiency optimization, while the mechanical feasibility of mode transitions is often considered separately or neglected. To address this issue, this study proposes a topology-aware hierarchical control framework for dual-motor ECVs. The framework combines an offline global efficiency map with an online transition-feasibility arbitration mechanism. In the offline layer, the energy-oriented operating mode and torque split are extracted over the vehicle-speed and wheel-torque domain. In the online layer, a topology-based transition matrix is used to identify mechanically singular mode transitions, and potentially torque-interrupting commands are re-routed through feasible bridge modes. The proposed method embeds powertrain topology constraints into the real-time implementation of an offline optimal map, thereby complementing conventional global optimization methods with transition-feasibility arbitration. Simulation results under the CHTC driving cycle show that the proposed strategy improves torque continuity during mode transitions while retaining most of the energy-saving benefit of the unconstrained efficiency-oriented strategy. Compared with the rule-based strategy, the proposed method reduces SOC-equivalent energy consumption by 10.7%, and recovers 65.5% of the DP-achievable energy-saving potential. Hardware-in-the-Loop (HIL) results further demonstrate that the proposed online arbitration logic can be executed within the controller sampling period.
Song, Dafeng, Chen, Lexin, Zeng, Xiaohua, Ni, Lixin
Fuel Savings from Predictive Charge Control Based on Short-Trip Duration in Series Hybrid Electric Vehicles14-15-03-00157/10/2026
Series hybrid electric vehicles (HEVs) employ an electric motor for propulsion, while the internal combustion engine operates solely as a generator under energy-efficient speed and load conditions. Owing to this architecture, series HEVs can achieve high fuel efficiency with a relatively simple control structure. However, conventional energy management systems (EMSs) often prioritize battery state-of-charge (SOC) stabilization, which can lead to frequent engine start–stop operations and unnecessary fuel consumption, particularly in short-trip driving. This study aims to enhance energy management performance in series HEVs by optimizing engine power generation timing based on predicted short-trip duration. A computationally efficient, rule-based prediction model is developed using real-world driving data, in which short-trip duration is estimated from vehicle speed and acceleration. Due to its low computational load, the proposed model is suitable for implementation in an onboard electronic control unit (ECU). The proposed control strategy initiates engine power generation when the battery SOC is low and the predicted trip duration is long, and suppresses generation when the SOC is sufficiently high or the predicted trip is short. A detailed vehicle model incorporating an engine, generator, electric motor, inverter, and battery is developed in Modelica to evaluate the proposed strategy. Simulation results demonstrate that the proposed EMS significantly reduces the frequency of engine start–stop events, leading to fuel economy improvements of 3.6% under the WLTC (excluding the extra-high phase) and 13.4% in a real-world urban–rural driving cycle, compared with a commercialized baseline vehicle. These results confirm the effectiveness and practical applicability of the proposed EMS for passenger vehicle applications.
Mizushima, Norifumi, Sato, Akira, Kuboyama, Tatsuya, Moriyoshi, Yasuo
Multi-Scale Modelling of Localized Battery Degradation in Heavy-Duty Series Hybrids: From Electrochemical Impedance Spectroscopy to Powertrain and Battery Pack Performance2026-01-07477/1/2026
Hybrid electric vehicles rely heavily on battery pack power capability, which is often compromised by non-uniform aging and thermal gradients. Conventional battery models typically use bulk state-of-health metrics, failing to capture localized degradation that leads to current imbalances and reduced pack utility. This paper presents a multi-scale modelling framework that integrates Electrochemical Impedance Spectroscopy data into a fractional-order equivalent circuit model to simulate localized degradation in Lithium Iron Phosphate cells. Results show that the terminal voltage of LFP cells can be accurately modelled using the proposed fractional-order equivalent circuit with a discrete transfer-function implementation, maintaining root-mean-square errors below 20 mV across most state-of-health and state-of-charge conditions. The validated cell model is then extended to a degradation-aware battery pack representation. The battery pack in this work utilizes a 200-kWh, 800 V architecture consisting of five modules connected in parallel, each module composed of 13 parallel strings of 250 series cells, evaluated under multiple degradation scenarios. By integrating this pack model into a Class-8 series hybrid powertrain simulation, this study quantifies how cell-to-cell heterogeneity impacts vehicle performance under the VECTO regional delivery drive cycle. At the vehicle level, these battery constraints influence engine duty cycles and battery pack stress metrics. When localized degradation reaches up to 40% in one module while the remaining modules degrade up to 20% to 30%, such inhomogeneous degradation reduces the minimum pack terminal voltage by approximately 27% and increases peak discharge current by more than 30%, resulting in more rapid degradation. These battery-level limitations translate into higher fuel consumption by up to 6% in a charge-sustaining scenario.
Safavi, Seyed Reza, Homayouni, Hooman, Shoa, Tina, Wang, Jason, McTaggart-Cowan, Gordon
Synergistic Integration of Advanced Combustion and Air-Path Technologies to Maximize Internal Combustion Engines Efficiency in Hybrid Applications2026-01-07337/1/2026
This paper assesses the efficiency limits of light-duty vehicle propulsion systems based on reciprocating internal combustion engines (ICE) in the current state of the art and in the next five-year horizon, considering their combination with technologies such as electric turbocharging and hybridization, while excluding plug-in hybrid configurations so that fuel remains the primary onboard energy source. A systematic methodology is applied to evaluate the influence of key variables—heat transfer, air–fuel ratio, and compression ratio—on engine performance, integrating these variations into a simulation model to capture their interactions and effects. The resulting parametric study enables the generation of new engine maps that exploit synergies between parameters and enhance the prediction of engine behaviour across different operating conditions, forming the basis for assessing potential advancements in hybrid powertrain architectures. These maps are then used to define performance expectations for hybrid vehicles, identifying optimal parameter combinations to guide future technology development and improve efficiency in hybrid powertrain design. The proposed powertrain architectures are integrated into a representative vehicle model, considering two vehicle typologies: a compact passenger car and a sport utility vehicle (SUV). To quantify the potential fuel-consumption benefits, an intelligent energy-management algorithm is implemented to supervise and optimize system operation over a WLTC driving cycle. The results indicate that the proposed configurations can achieve fuel-consumption reductions exceeding 20%, demonstrating the effectiveness of both the powertrain designs and the control strategies. Overall, the findings highlight the significant efficiency potential of advanced ICE-based propulsion systems when combined with near-term technologies such as electric boosting and hybridization, confirming the viability of these improvements and providing a robust basis for future hybrid vehicle development focused on maximizing energy efficiency in transportation.
Pla, Benjamin, Dolz, Vicente, Serrano, Jose R., Gómez-Vilanova, Alejandro, Oliva, Fermin, Cardenas, Maria, Ariztegui, Javier
Representative Driving Cycles for Light Commercial Vehicles Based on Their Usage Profiles2026-01-07517/1/2026
This study describes a methodology for synthesizing representative driving cycles for light commercial vehicles. The focus is on taking the usage profiles of these vehicles into account in the driving cycle synthesis. In this methodology, representative routes are simulated using the example of light commercial vehicles in the craft sector. The results of these simulations are representative speed distributions and representative altitude variations. These results are then used as target values for the actual driving cycle synthesis. Furthermore, measurement runs are carried out with a light commercial vehicle to create a database of real-world driving data. The measurement runs include different urban, rural, and motorway sections and cover a total distance of approximately 510 km. Routes with flatter and more challenging altitude profiles are driven. During the measurement runs, the speed signal and the altitude signal are measured. These signals are then processed and cut into short segments, each consisting of a speed and altitude profile. This is followed by the actual driving cycle synthesis, in which those segments with suitable altitude variations are assembled according to the representative speed distributions. This paper examines three different usage profiles of light commercial vehicles in three different cities in Germany. These three cities are Ulm (large city), Stuttgart (metropolitan region), and Munderkingen (small town in rural region). The results of these three analyses show considerable differences both in the calculated speed distributions and in the calculated altitude variations. For example, the usage profile in the rural region of Munderkingen shows significantly higher rural proportions than the usage profile in the Stuttgart metropolitan region. Furthermore, the analysis in Stuttgart shows a much higher altitude variation than the usage profile in Ulm. These differences highlight the need to take vehicle usage profiles into account in the driving cycle synthesis.
Heilmann, Oliver, Grabow, Andreas, Cortès, Sven, Schlick, Michael, Stoll, Tobias, Kulzer, André Casal
Improved Thermal Management for Operating a Battery Electric City Bus under Real Driving Conditions2026-01-07587/1/2026
The goal of reducing global CO2 emissions requires actions especially for the transportation sector. To achieve the goal, electric traction motors are frequently implemented in passenger vehicles, as well as in commercial vehicles like heavy-duty trucks or buses. Particularly electric city buses have the potential to reduce the local emissions in urban areas and provide local exhaust-emission-free mobility. While their number of registrations rises, research focusses on the improvement of the overall system in order to increase energy efficiency. High importance is gained by the thermal management of the whole system. This research investigates a simulative approach to improve the thermal management and therefore the energy efficiency of an electric city bus. The different thermal components of an electric city bus like drive system, battery system and heating, ventilation and air conditioning system (HVAC system) are modelled. Their thermal behavior has been validated in previous research. Based on the validated model, this study proposes an improved thermal management that, state-dependent, combines the thermal circuits of the single components to reduce the overall energy demand. Cooling or heating is provided by the HVAC system. Furthermore, the simulation utilizes real driving cycles of a city bus in the Hamburg area. Measurement data from an entire year are examined by a cluster analysis that results in typical application profiles for urban bus traffic. These profiles are used as basis for further research. An operating strategy for the thermal management of an electric city bus under real driving conditions is developed using the simulation model. Results are presented, which show that the overall energy demand decreases due to an improved, application profile-dependent thermal management system.
Schäfer, Henrik, Hellberg, Tobias, Meywerk, Martin
Heavy-Duty Nonroad Material Handler Electrification Part 1: Real-World Drive Cycle Development02-19-03-00156/10/2026
Knowing a detailed operating cycle is critical for developing and testing equipment. Operating cycles can be separated by two clear distinctions: (1) regulatory or non-regulatory and (2) application at the engine-only or full machine level. The Environmental Protection Agency’s (EPA) Nonroad Transient Cycle (NRTC) may be a good representation of engine use in many types of equipment, but there is a gap in standardized and validated drive cycles specifically for nonroad material handlers. Lacking a standardized drive cycle makes it difficult to accurately benchmark machine performance and validate new powertrain technologies. The objective of this investigation is to illustrate the development of a custom drive cycle augmented with real-world customer use data that serves multiple purposes: (1) understand the range of operation and utilization that formulated inputs for electrified architecture analysis and (2) develop a repetitive and consistent maneuver to establish baseline energy consumption enabling equivalent comparison to future electrified prototype builds. This article presents a solution specifically for a 23-ton nonroad material handler in which material handling, machine transport, and extended idle were homologated to form representative short cycles defined by machine velocity and hydraulic cylinder position. The most intensive material handling short cycles had a load factor of 40% and an average fuel rate of 16 L/h. Combined with a visual aid, the short cycles exhibited low variability, having less than 5% root mean square (RMS) error in lift and reach position with respect to the average. The machine’s performance on these short cycles at the Advanced Power Systems Research Center (APSRC) was compared to results from two real-world customer locations operating the instrumented test machine in a cyclical manner, and for similar ground conditions were found to be comparable in fuel consumption.
Czarnecki, Alexander, Goodenough, Bryant, Worm, Jeremy, Robinette, Darrell, LaTendresse, Phil, Westman, John
Engine Thermal Management Strategies for Emissions Reduction during Real Driving Tests2026-37-00366/9/2026
Thermal management in internal combustion engines (ICEs) strongly affects fuel consumption and pollutant emissions, especially during engine warm-up. Particularly, the oil temperature is strictly related to the organic efficiency of the vehicle: in the early phase of a driving cycle, the low temperature produces a high-viscous oil, which increases friction losses and increases fuel consumption, with respect to full thermal regimated oil. Usually, the oil and coolant thermal behaviours are interconnected, thanks to a coolant/oil heat exchanger in the engine. In this study, a prototyped electrical coolant pump has been applied and integrated in a small SUV vehicle, replacing the original mechanical unit. An off-board experimental campaign allowed a complete hydraulic characterization of the cooling system, including thermostat operation, and led to a physically based correlation between flow rates and pressure drops in each branch. Based on these results, the pump was designed and prototyped, enabling advanced flow management strategies on board. On-road Real Driving Emissions (RDE) tests were carried out using different pump control logics. Four different control strategies have been proposed in order to reduce the warm up time of the engine and the oil. Results show that the warm-up time reduction produces also a decrease in CO, NO, THC, CH₄, and PN emissions by 15–65%, particularly during cold-start conditions. The innovation proposed can be also combined to other technological options, to further improve the thermal behaviour of the engine and increase the temperature of the oil in the early phase of a common driving cycle. Electrification also reduces parasitic losses and facilitates integration with hybrid powertrains, confirming thermal management as an effective transitional technology for improving ICE efficiency and environmental performance under real driving conditions.
Di Battista, Davide, Di Bartolomeo, Marco, Cipollone, Roberto
Heavy-Duty Nonroad Material Handler Electrification Part 2: Energy Efficiency and Performance Evaluation02-19-03-00166/2/2026
Decarbonization efforts achieved through electrification in nonroad mobile machinery can realize a reduction in fuel consumption of more than 20%, thanks to concepts familiar to light-duty passenger vehicles. This case study compares the results of a hybrid-electric material handler to its conventional counterpart, utilizing machine-specific drive cycles presented in part one of this paper series. The hybrid prototype features an extended-range electric vehicle (EREV) powertrain that demonstrated substantial energy efficiency improvements. Specifically, there was a reduction in equivalent fuel consumption of 75% when operating in electric-only mode, and 33% when maintaining the battery by charging with an on-board generator. Together, the efficiency improvements can be extrapolated over a low-intensity, 8-h shift characterized by significant idle time and highly dynamic engine load for a 47% reduction in net energy consumption. Key technologies that led to this improvement included engine downsizing and decoupling, regenerative braking, and an electrohydraulic pump unit with advanced controls. This study explains details of the powertrain architecture and subsystems that were implemented on a demonstration vehicle, control strategies used to meet project goals, and an analysis of energy consumption from testing on a closed course. Also included in this study is a discourse on comparison metrics that can be used for quantifying the energy consumption differences between hybrid-electric and conventional diesel powertrains in nonroad mobile machinery.
Czarnecki, Alexander, Goodenough, Bryant, Worm, Jeremy, Robinette, Darrell, LaTendresse, Phil, Westman, John, Subert, David, Heath, Matthew, Kiefer, Dylan, Black, Andrew
Harbinger extends its range of medium-duty EVs26TOFHP06_126/1/2026
Harbinger revealed its HC Series Cab during Work Truck Week 2026 in Indianapolis, Indiana. The HC Series is a new line of medium-duty, low cab forward vehicles available as both an electric and plug-in hybrid model. Harbinger states that the HC Series' hybrid powertrain uses a range-extended hybrid platform with a gasoline engine to recharge the truck's batteries, extending range up to 500 miles (805 km), depending on upfit configuration and drive cycle.
Wolfe, Matt
Real-Time Observations of the Effect of Fuel Dilution in an Engine Transient Friction Rig. Along with Real-Time Observations of Fuel Entering and Leaving Internal Combustion Engine Oil, over Both Standard Engine, ICE and Plug-In Hybrid, PHEV Dynamic Drive Cycles2026-01-03514/7/2026
Hybrid electric vehicles (HEVs) with an increasing level of electrification, are becoming a major part of the global energy transition. To achieve lower engine tailpipe exhaust emissions and improve total fuel consumption, typically the HEV control system expertly and frequently switches between the internal combustion engine and electric motor drive, with multiple stops and restarts of the internal combustion engine (ICE). As a consequential result of this switching, are typically slower or even incomplete engine warm-up times, depending on the engine speed, load pattern and run time of the vehicle drive cycle. Along with the speed and load transient control, the engine stop and start processes are also challenging to control, with respect to cold start fuel and combustion by-products entering the oil. Consequently, contamination enters the engine oil but may not completely leave. These effects are highly transient over the drive cycle. Contaminants and in particular, fuel dilution, will affect the engine oil viscosity. To demonstrate this whilst yielding insights, a precisely controlled engine test cell, running the cold start Worldwide Harmonized Light Duty Transient Cycle (WLTC) for both, a non-hybridized ICE only vehicle and a HEV in charge sustaining mode operation is described. This also has on-line viscosity sensing and oil sampling. Typical data is shared along with engine oil comparisons. For complimentary insights, the impact of the fuel dilution on engine friction was investigated using a novel, precise, fully transient engine friction test rig, which measures gasoline direct injection high pressure fuel-pump friction and engine oil viscosity accurately. The cycle is based on measured data from vehicles tested on a chassis dynamometer. On-line friction data, with oil comparisons is used to show real-time data of the effect of fuel dilution on the frictional energy required, thus CO2 over the full WLTC.
Butcher, Richard, Bradley, Nathan, Thedering, Dennis
In-Use Life Digital Twin (Redundant Storage) and Swappable Use Case for Battery Durability2026-01-03974/7/2026
As regulatory frameworks for zero-emission vehicles (ZEVs) and battery electric vehicles (BEVs) continue to evolve, there is growing emphasis on monitoring battery durability and usage throughout the vehicle lifecycle. These regulations increasingly specify the use of data monitors and tracking mechanisms to assess battery health and performance. In addition, regulations require anti tampering mechanisms especially for monitors that have external write access. Historically, regulations focused primarily on vehicle warranty; however, with the introduction of battery durability monitors, clarity is needed for the new battery durability monitors. More specifically if the battery durability monitors track with the lifetime of the vehicle or if they follow the lifetime of the battery. Furthermore, current regulations provide no guidance on high-voltage (HV) traction battery service strategies or methods to protect monitors from tampering by external customers. This paper will classify battery durability tracking parameters (DIDs) according to whether they align to the lifetime of the vehicle or the battery itself. Building on this classification, a service strategy is proposed that considers typical vehicle architectures: when the battery management Electrical Computer Unit (ECU) is fully integrated with or separated from the high voltage traction (HV) battery. The outlined service strategy not only supports regulatory compliance, but also enhances data integrity by mitigating the risk of tampering with monitored parameters through a Digital Twin framework. More specifically, the Digital Twin framework introduces redundant storage of critical information in multiple storage locations such as ECUs and then a mechanism for correlating that critical information to determine a mismatch. This approach anticipates future requirements for tamper-proofing and ensures secure, reliable tracking of battery durability metrics through redundant ECU storage.
Laskowsky, Patricia, Bunnell, Justin, Zettel, Andrew, Albarran, Josue
Electrified Propulsion System Balancing for Light Duty Full Size Truck and Sport Utility Vehicles: A 2026 North American Market Perspective2026-01-04124/7/2026
Achieving the stringent EPA CAFE 2032 standards for light-duty full-size trucks and sport-utility vehicles (SUVs) in North American poses significant challenges. While Battery Electric Vehicles (BEVs) offer a clear path to zero tailpipe emissions, their widespread adoption in this segment faces hurdles including range anxiety, payload/towing capabilities, and traditional truck/SUV use cases. This paper investigates a balanced approach, focusing on optimizing propulsion system design with appropriate hardware content, can effectively meet future fuel economy and emissions standards. This investigation examines advanced BEVs and hybrid electric vehicle architectures, including full hybrids (HEVs), and plug-in hybrids (PHEVs) tailored for full-size trucks and SUVs. Considerations include the optimal sizing of internal combustion engines, electric motors, and battery packs to deliver robust performance while maximizing energy efficiency. This paper analyzes the integration of technologies such as electrified transmissions, electric motor configurations, and battery size. Trade-offs between electric motor capabilities, battery pack sizing and emissions reduction across different hybridization levels are investigated. The AMESim Hybrid Optimization Tool (HOT) is used to evaluate multiple propulsion system configurations and drive cycles for energy efficiency. Vehicle selection was performed by evaluating the payload/towing capabilities, propulsion system architecture, and vehicle model year. Drive cycles were selected from truck standards and real-world driving scenarios. The objective is to demonstrate a balanced approach for electrification pathways that satisfy consumer preferences for capability and range, ensuring CAFE 2032 compliance through a diversified powertrain portfolio.
Babcock, Dillon, Robinette, Darrell
Thermal Management of Battery Modules under Fast Charging2026-01-03994/7/2026
Battery modules operate under diverse and complex conditions, such as driving cycles and fast charging. In these scenarios, effective thermal management is critical to ensuring safety and extending the battery's lifespan. Fast-charging scenarios present a particular challenge due to the complex current control strategies that strongly influence cell temperature distribution, making thermal uniformity a key concern. Existing studies focus more on drive cycles, but not sufficient for fast charging. This study presents a coupled electrochemical-thermal simulation framework based on the DCIR (Direct Current Internal Resistance) model to examine heat generation and temperature responses during fast charging. The model incorporates heat conduction pathways and the structural layout of the module, enabling the evaluation of thermal mismatch risks and the optimization of module design and thermal management strategies. The findings offer practical insights for battery thermal management and the development of advanced control strategies.
Xiao, Fangzhi, Chen, Guijie, Ma, Shihu, Hu, Xiao, Song, Shujun, Wakale, Anil Bhaurao
Passive Soot Regeneration Observations on a GPF Equipped Vehicle for PM Exhaust Emission Control2026-01-03664/7/2026
An on-road study has been conducted where a modern vehicle with a 3L turbocharged, PFDI gasoline engine was upfitted with appropriately sized uncoated GPFs for soot capture in a dual-bank exhaust line. The tested GPFs, whether clean or pre-loaded, were weighed to track their soot-load trends between representative real-world driving routes, where sensor data and exhaust temperature data was recorded. Thus, characterization of the passive soot regeneration process in the uncoated GPF was linked to elevated temperatures and vehicle drive cycles speeds.
Craig, Angus, Warkins, Jason
Ambient and Initial Temperature Effects on Battery Electric Vehicle (BEV) Range and Energy Consumption Rate Modeled in FASTSim2026-01-02774/7/2026
Ambient and initial temperatures significantly impact the energy consumption rate (ECR) of battery electric vehicles (BEVs) due to auxiliary loads and the temperature dependence of battery efficiency. This study introduces a streamlined, physics-based thermal modeling approach within the FASTSim tool that bridges the gap between oversimplified constant-load models and computationally expensive high-fidelity simulations. By employing a lumped thermal mass framework, the model captures fundamental energy balances and critical non-linear energy penalties while maintaining the computational efficiency required for expansive sensitivity studies. The simulations evaluated a compact BEV hatchback with a resistive heater over city (UDDS) and highway (HWFET) test cycles. Compared to a 22°C initial and ambient temperature baseline, a -7°C initial/ambient temperature resulted in a 221% increase in the ECR for the city cycle and a 100% increase for the highway cycle. Conversely, a 45°C initial / 40°C ambient temperature resulted in a 40% increase for UDDS and an 18% increase for HWFET. These results demonstrate that while cold conditions impose the most severe energy penalties due to resistive heating, the impact is consistently more pronounced in city driving where auxiliary loads represent a larger proportion of total energy. This lightweight yet robust framework enables researchers to rapidly quantify BEV thermal sensitivity across diverse climates without the need for high-overhead simulation environments.
Baker, Chad, Steuteville, Robin, Holden, Jake, Gonder, Jeffrey, Carow, Kyle
Improving Winter Driving Range of EVs with a Novel Desiccant Device: Ceramic Humidity Regulator (CHR)2026-01-01174/7/2026
With rapid growth of Electric Vehicles (EVs) in the market, challenges such as driving range, charging infrastructure, and reducing charging time needs to be addressed. Unlike traditional Internal combustion vehicles, EVs have limited heating sources and primarily uses electricity from the running battery, which reduces driving range. Additionally, during winter operation, it is necessary to prevent window fogging to ensure better visibility, which requires introducing cold outside air into the cabin. This significantly increases the energy consumption for heating and the driving range can be reduced to half of the normal range. This study introduces the Ceramic Humidity Regulator (CHR), a compact and energy-efficient device developed to address driving range improvement. The CHR uses a desiccant system to dehumidify the cabin, which can prevent window fogging without introducing cold outside air, thereby reducing heating energy consumption. CHR is based on desiccant dehumidification technology. Unlike conventional desiccant rotors, it features an integrated structure that combines the desiccant material with a honeycomb-type Positive Temperature Coefficient (PTC) heater. This enables highly efficient direct heating regeneration and a compact design optimized for EVs installation. Previously, the heating power reduction achieved by CHR was measured, and the extended driving range was estimated based on those results. In contrast, this study conducted a complete driving test from full to empty battery charge in a cold laboratory environment. The test was performed using the CLTC (China Light-Duty Vehicle Test Cycle) driving mode. Using an EV equipped with a CHR prototype, tests were conducted with CHR turned ON and OFF respectively. A 13% improvement in winter driving range was actually observed, confirming the real-world benefits of the concept. In conclusion, this study demonstrates that CHR is a promising solution for extending EVs driving range under winter conditions while improving energy efficiency and passenger comfort.
Sakai, Naoki, Takahiko, Nakatani, Shinoda, Narimasa, Ihara, Yukio, Wakida, Norihiro, Kato, Kyohei, Anoop, Reghunathan-Nair
Development of a Virtual Test Rig to Evaluate Thermal Management of a Battery-Electric Vehicle under Battery Heating-Cooling Modes2026-01-04164/7/2026
A battery-electric vehicle (BEV) has multiple powertrain components (battery, inverter, e-motor), a thermal management system (compressor, heat exchanger, cabin heating, ventilation, and air-conditioning), and a vehicle body, among others. Vehicle testing is time-consuming, and changing powertrain components during the testing and design process is costly. Simulation models (aka virtual or simulation test rig) have been widely used for efficient vehicle design. This work presents a systematic approach to developing a virtual test rig to evaluate the thermal performance of battery-electric vehicles. A Tesla Model Y is tested in a chassis dynamometer, and the measured vehicle performance data are used as boundary conditions for the complete vehicle model. The detailed lithium-ion battery (LIB) pack model, including its cooling system, was developed and calibrated using various transient driving cycle data. The HVAC model uses a simplified controller to maintain the cabin temperature at 25 °C in both battery heating and cooling modes. The predicted thermal and electrical performance of the BEV is well validated by test data. Then, the complete vehicle model is used to compare the thermal performances of the BEV under cabin heating and cooling modes for various transient driving cycles. The simulated results show that using an external cabin air circulation model can reduce the battery energy consumption and dissipated heat by 9.9% and 2.4%, respectively. This calibrated virtual test rig can be used to evaluate a new HVAC system.
Sok, Ratnak, Kusaka, Jin
Neural Surrogates and Reinforcement Learning for Heavy-Duty Diesel Torque Control2026-01-01994/7/2026
Accurate torque-trace reproduction on regulatory drive cycles is central to heavy-duty diesel certification and development testing. Conventional controllers such as Proportional Integral Derivative (PID or PI) can be enhanced with gain scheduling and feedforward (FF) maps to satisfy requirements but require extensive calibration and are sensitive to nonlinearities and delay. This paper evaluates a data-driven control framework comprising a recurrent neural surrogate of engine torque (specifically an LSTM – long short-term memory) trained on engine/dynamometer data and a reinforcement learning (RL) policy trained using this surrogate (“world model”) to track requested torque while regularizing control effort. The RL policy (specifically TD3 – twin delayed deep deterministic) is benchmarked against tuned PID and PID+FF baselines on the Environmental Protection Agency’s Heavy Duty Federal Test Procedure (HD-FTP) segments using EPA regression criteria (slope, |intercept|, R2) and tracking metrics (mean absolute error - MAE, root mean square error - RMSE). TD3 reduced mean absolute error (MAE) by 58% (from 91.34 to 38.69 N·m) and root mean square error (RMSE) by 54% (from 123.5 to 56.73 N·m), improved regression to slope = .9978, |intercept| = 7.13 N·m, R2 = .9844, and cut the 95th-percentile absolute error by 60% compared to the PID+FF controller (the next best performing controller – in all categories). Results show the RL controller improves responsiveness and accuracy relative to autotuned PID+FF on the surrogate model, while reducing manual calibration effort. The approach is modular and engine-agnostic (retrain surrogate and policy) and is amenable to multi-objective extensions that incorporate emissions proxies in the reward.
Cook, James, Puzinauskas, Paulius, Bittle, Joshua, Hall, Spencer
On-Road Vehicle Testing and Characterization2026-01-01954/7/2026
Vehicle testing for fuel economy and emissions is typically performed indoors over standard dynamometer drive schedules to minimize variability and maximize repeatability of the results. In contrast, during on-road operation, operational parameters such as vehicle speed and acceleration and environmental factors such as temperature and wind will change unpredictably. These factors influence vehicle fuel economy and emissions, making on-road operation much more variable than dynamometer results. However, even though on-road conditions may be unpredictable, the on-road operational data can still be used to characterize vehicle performance. This paper describes the development of an on-road vehicle test methodology, with a focus on accounting for on-road factors with a high degree of accuracy while requiring only an achievable and reasonable amount of data. To develop this methodology, a 2016 Honda Civic was instrumented and driven multiple times over a route covering urban, rural, and freeway segments. Vehicle operational data, environmental conditions, fuel consumption, and emissions were recorded. The route was divided into segments and drive cycle parameters were calculated for each segment. Simple empirical equations were developed for this vehicle correlating fuel consumption with drive cycle parameters and the environmental conditions. The empirical models were compared to fuel consumption data from dynamometer tests with good results. Criteria pollutants (CO, NOx, and THC) were also measured and compared to dynamometer data. Finally, the amount of testing and data required to adequately characterize vehicle performance is discussed.
Moskalik, Andrew, Barba, Daniel
Energy Efficiency Analysis of U-Haul Truck and Trailer Systems: Impact of Tongue Weight and Load Variations2026-01-04664/7/2026
The demand for improved energy efficiency in real-world vehicle operations continues to grow with technology enhancement. When transporting large cargo loads with passenger pickup trucks and rental trailers, the interaction between vehicle payload, towing configuration, and fuel consumption becomes a key factor in overall system efficiency. Understanding how towing configurations and trailer loading influence fuel consumption and vehicle performance is critical for both consumer guidance and vehicle system design. This study investigates the energy efficiency of U-Haul truck and trailer systems, with a particular focus on the influence of trailer tongue weight. U-Haul truck and trailer simulation models were developed using AVL Vehicle Simulation Model (VSM) software, with an F-350 engine brake-specific fuel consumption (BSFC) map integrated to represent realistic engine performance. Two configurations with equal payload were evaluated: (1) a U-Haul truck alone, and (2) a U-Haul truck towing a trailer. Within these configurations, multiple scenarios were analyzed, including variations in payload levels and tongue weight distributions. Driving cycles were selected to capture common moving conditions such as urban stop-and-go traffic and extended highway operation. Simulation outputs quantified the interactions among vehicle dynamics, powertrain load, and fuel consumption. Results show clear differences in energy consumption between standalone and towing configurations, with tongue weight distribution exerting a significant influence on both efficiency and stability. The findings provide practical insights into the energy trade-offs between independent vehicle operation and towing scenarios. Moreover, the study highlights the importance of load distribution and driving cycle considerations in optimizing fuel consumption, offering a framework that can be extended to rental, commercial, and consumer towing applications where energy efficiency and vehicle performance are important.
Wang, Gang, Kathadi, Mohammad, Yang, William, Chen, Yan
An Analysis of the Energy Requirement for a Synthesized City Drive Cycle for Electric Two-wheelers2026-01-50102/12/2026
Range estimation for electric vehicles based on standard drive cycles generally underestimates energy consumption and fails to accurately represent the actual driving characteristics. This paper aims to develop a representative driving cycle for electric two-wheelers that emulate the real-world driving scenario in Lucknow, India. The micro-trip-based random selection scheme is used to form the drive cycle. The onboard Global Positioning System (GPS) module is used to log vehicle speed data for every second, and nine assessment parameters were used to analyze the candidate drive cycles. The total duration of the developed drive cycle is 1800 s, and the length is 17.45 km. Traffic attributes of the developed drive cycle are compared with the India drive cycle (IDC), Delhi motorcycle drive cycle (DMDC), and Edinburgh motorcycle drive cycle (EMDC). A comparison of the estimated energy requirement of the developed drive cycle with IDC indicates that the estimated actual energy requirement (Wh/km) is 46.9% higher than estimated with IDC. Comparative analysis reveals the significant differences that emphasize the need for developing city-specific cycles.
Vashist, Devendra, Pandey, Bhaskar, Malik, Varun
Drive Cycle Based Energy Audit & Range Estimation for 4X2 Electric Truck2026-26-01631/16/2026
Transportation sector in India accounts for 12% of total energy consumption. Demand of energy consumption is being met by the imported crude oil, which makes transportation sector more vulnerable to fluctuating international crude oil prices. India is mindful of its commitment in 2016 Paris climate agreement to reduce GHG emissions intensity of its GDP by 40% by 2030 as compared to 2005 levels. To fast track the decarbonization of transportation sector, commercial vehicle manufacturers have been exploring other viable options such as battery electric vehicles (BEVs) as a part of their fleet. As on today, BEV has its own challenges such as range anxiety & high total cost of ownership. Range anxiety can be certainly addressed by optimum sizing of electric powertrain, reduction in specific energy consumption (SEC) & use of effective regeneration strategies. Higher SEC can be more effectively addressed by doing vehicle energy audit thereby estimating the energy losses occurring at each powertrain component of an electric vehicle. The work illustrated in this paper involves drive cycle-based energy audit & range estimation for 4X2 rigid electric truck using simulation approach. It involves strenuous exercise of simulation specific input data generation by doing rigorous component level tests for battery, motor, tires & auxiliaries. Duty cycle data was acquired for 3000 km & condensed cycle of 32 minutes was formed which represents real world usage pattern. Data recorded in component and vehicle tests was used to build robust simulation model in GT-DRIVE. Simulated SEC was validated within 4% with on road trails. 73.5% of battery discharge energy was used to overcome rolling resistance loss, aerodynamic drag loss, electromechanical conversion loss, auxiliary losses, braking losses & differential losses. Effective power at wheels observed to be 26.5% of total battery discharge energy. Sensitivity analysis for RAR, RRC, coasting & braking regeneration limits was carried out and effect of each parameter on final SEC was studied and optimum set of parameter combination was suggested to the OEM. Outcome of this project has also laid down the sophisticated methodology to carry out energy audit of any electric vehicle, which in turn will help to bring simulation predictions much closer to the real-world scenarios.
Gijare, Sumant, Karthick, K., Juttu, Simhachalam, Thipse, Sukrut S., A, Jothikumar, J, Frederick Royston, SR, Subasree, G, Harini, M, Senthil Kumar
India WLTP Vs MIDC: Comparative Analytical Study on BEV Range & Energy Consumption for Proposing iWLTP 4-Phase2026-26-02441/16/2026
In India, Currently Continuous FULL MIDC (Modified Indian Driving Cycle) is used to declare the Range & Energy consumption of BEV (Battery Electric Vehicle). AISC (Automotive Industry Standards Committee) is looking to implement Worldwide Harmonized Light-Duty Test Procedure (WLTP) in India. AISC released AIS 175 for WLTP implementation from Apr 2027. The objective of WLTP is to standardize the test procedure globally for evaluating Emission/FE/Range of Light Duty Vehicles. But the effect of AIS 175 regulation on Battery Electric Vehicles Range Declaration is very less. The Range is almost same as Full MIDC declared Range. The On-road Range BEV is always lesser than the Declared Range of vehicles because of ambient conditions. Usually, the Full MIDC declared Range will be 20% ~26% higher than actual On Road Range. The Range of BEV as per India WLTP 3-Phase was observed 18% ~ 24% higher than actual On-road range of vehicles. There is only 2% difference observed between Full MIDC Range test & India WLTP 3-Phase PER test results. The Energy Consumption of WLTP 3-Phase test is 7% better than MIDC Energy Consumption test (AIS 039). To develop and declare the range of BEV’s near to on-road range better WLTP test cycle is required. So, we created New India WLTP 4-Phase test cycle with 4Phases: Low, Medium, High1, High2 (reference from Europe WLTP, instead of Extra-High phase, High phase repeated, just like NEDC to MIDC). The Range observed in India WLTP 4-Phase is only 10% ~ 16% higher than on-road Range of vehicle. The difference between India WLTP 3-Phase vs India WLTP 4-Phase for Range and Energy consumption was 6% Implementing India WLTP 4-Phase test cycle will provide very realistic range estimation results for Indian driving conditions compare to India WLTP 3-Phase test cycle.
Shiva Kumar, Mucharla, Tentu, Kavya
Parameterizing External Factors Influencing E-Motor Consumption2026-26-01751/16/2026
In recent times, a standard driving cycle is an excellent way to measure the electric range of EVs. This process is standardized and repeatable; however, it has some drawbacks, such as low active functions being tested in a controlled environment. This sometimes causes huge variations in the range between driving cycles and actual on-road tests. This problem of variation can be solved by on-road testing and testing a vehicle for customer-based velocity cycles. On-road measurement may be high on active functions while testing, which may give an exact idea of real-world consumption, but the repeatability of these test procedures is low due to excessive randomness. The repeatability of these cycles is low due to external factors acting on the vehicle during on-road testing, such as ambient temperature, driver behavior, traffic, terrain, altitude, and load conditions. No two measurements can have the same consumption, even if they are done on the same road with the same vehicle, due to the influence of the above-mentioned external factors. The current paper will portray a machine learning-based methodology to parameterize the external factors affecting e-motor consumption. By parameterizing these factors, on-road test results are normalized and further used for comparative studies. The paper also takes us through the process of data collection for this study, the parameterization process of external factors using ML models, for different driving scenarios and ambient temperature ranges. The ML models are developed in a MATLAB environment and can be reproduced in any other tool. Merits and demerits of each ML model are discussed along with ways and means to mitigate each external factor, which will make the testing procedure more robust and reliable. Thus, it helps in making automobiles more energy efficient.
Kelkar, Kshitij, Kanakannavar, Rohit
Scalable and Modular Data-Driven Hybrid Chassis Dynamometer Framework for EV Powertrain Evaluation2026-26-03631/16/2026
In its conventional form, dynamometers typically provide a fixed architecture for measuring torque, speed, and power, with their scope primarily centered on these parameters and only limited emphasis on capturing aggregated real-time performance factors such as battery load and energy flow across the diverse range of emerging electric vehicle (EV) powertrain architectures. The objective of this work is to develop a valid, appropriate, scalable modular test framework that combines a real-time virtual twin of a compact physical dynamometer with world leading real-time mechanical and energy parameters/attributes useful for its virtual validation, as well as the evaluation of other unknown parameters that respectively span iterations of hybrid and electric vehicle configurations, ultimately allowing the assessment of multiple chassis without having to modify the physical testing facility's test bench. This integration enables a blended approach, using a live data source for now, providing a point of calibration and validation for the virtual model(s), as well as using the virtual model capability to determine other unknown/measurable characteristics about the physical model. So, this test framework makes that system's capability, representing an enhancement to its ability, with a virtual twin merging them together to enable virtual evaluation of multiple configurations of the chassis without changing the physical test stand. So, this combined real-world and virtual framework offers a scalable and flexible testing and modelling platform for both early performance characterisation, as well as life cycle-based energy evaluations. In responding to the identified gaps, this work introduces an innovative hybrid chassis dynamometer framework that applies to a real-world test bench in tandem with a concurrently simulated virtual model, offering early-stage validation and optimisation potential using the shift-left development proposition. The result is a reusable and forward-thinking platform supporting efficient EV development by forecasting and drawing informed insights into energy flow, battery performance, and lifecycle behaviour from ahead of typical physical testing boundaries.
Kumar, Akhilesh, V, Yashvati
Efficiency Improvement of an Electric Vehicle e-Transaxle through Gear, Bearing, and Lubricant Optimization Strategies2026-26-00531/16/2026
Improving transaxle efficiency is vital for enhancing the overall performance and energy economy of electric vehicles. This study presents a systematic approach to minimizing power losses in a single-speed, two-stage reduction e-transaxle (standalone) by implementing a series of component-level design optimizations. The investigation begins with the replacement of conventional transmission oil with a next-generation low-viscosity transmission fluid. By adopting a lower-viscosity lubricant, the internal fluid resistance is reduced, leading to lower churning losses and improved efficiency across a wide range of operating conditions. Following this, attention is directed toward refining the gear macro-geometry to create a gear set with reduced power losses. This involves adjustments to parameters such as module, helix angle, pressure angle, and tooth count, along with the introduction of a positive profile shift. These modifications improve the contact pattern, lower sliding friction, and achieve a more uniform distribution of forces along the gear flanks. As a result, load-related gear losses are significantly diminished. In the final stage of optimization, high-performance, low-friction bearing designs are incorporated to further reduce mechanical drag and enhance overall drivetrain efficiency. For bearing loss optimization strategies, energy-efficient ball bearing designs is examined and engineered to lower internal friction and increase operational lifespan. Energy Efficient bearing prove significant improvement in efficiency and further reducing mechanical losses within the drivetrain. Taken together, the findings highlight that careful selection of lubricants, refinement of gear geometry, and adoption of advanced bearing solutions can deliver notable gains in transaxle efficiency. These outcomes reinforce the potential of such targeted interventions as practical means for boosting drivetrain performance in electric vehicle applications.
Agrawal, Devesh, Bhardwaj, Abhishek, Bhandari, Kiran Kamlakar
Drive Cycle Type Identification Using Data Driven Methodologies2026-26-06491/16/2026
Identifying the type of drive cycle is crucial for analyzing customer usage, optimizing vehicle performance and emission control. Methods that rely on geographical location for drive cycle identification are limited by varying driving conditions at the same location (e.g. heavy traffic during peak hours vs. free-flowing traffic at night). This paper proposes a methodology to identify the type of drive cycle (city, interurban, highway or hybrid) using drive characteristics derived from vehicle data rather than geographical location. Real-world vehicle data from testing trucks is taken, whose drive profiles are already known. Initially, multiple characteristic features of the drive cycle are identified from literature surveys and domain experience. These features, which can be extracted from basic signal data, include gear shifts, time spent in different driving modes (acceleration, cruise, standstill), velocity distributions, and an 'aggressiveness factor' representing overall driving style. Using ML based feature selection techniques, the most important features are selected for this cause. With these finalized parameters, a data-driven classification model is developed. This model is trained, validated, and tested using the identified real-world vehicle data. It classifies drive cycles into four major types: city, interurban, highway, and hybrid with a high degree of accuracy. This classification enables accurate identification of drive cycles, addressing the limitations of location-based methods. The developed model is employed to determine the type of drive cycle driven by customers, thereby aiding in the analysis of the influence of drive cycles on vehicle performance and emissions.
Reddy, Mallangi Prashanth, Gorain, Raju, Ganguly, Gourav
Clutch Thermal Adequacy Assessment in Repeat Restart Launch Conditions on Gradient2026-26-04341/16/2026
The clutch is a mechanical device that connects and disconnects engine power to the drivetrain through the clutch disc and cover assemblies. The disc, with friction material linings is mounted on the transmission shaft, transmits power when clamped between the flywheel and cover assembly. During operation, wear occurs due to speed differences and slippage between the engine and transmission. Clutch performance is evaluated under repeat restart conditions on steep gradients to assess thermal durability and reliability in commercial vehicles. The repeat restart test on a 12% gradient replicates truck launches under full load, where excessive slippage generates heat that may lead to friction material wear or failure if critical temperature limits are exceeded. To address the high cost and time of physical testing, a 1D thermal simulation was developed using GT Suite. The model replicates 90 repeat vehicle launches on a 12% gradient in first gear, integrating driver inputs and drive cycles to predict clutch housing air temperatures. The simulation shows a 95% correlation with test data, validating its accuracy and reliability. This virtual approach enables early-stage design validation and optimization of parameters influencing heat generation and thermal degradation, minimizing dependence on physical trials and reducing development time. Applied to heavy commercial vehicles, this methodology supports design of experiments for drivetrain parameter analysis, guiding optimal configurations that minimize thermal stress. The adoption of GT Suite as a digital validation tool improves product development efficiency, lowers warranty costs, and enhances product quality. It provides a strategic advantage in competitive markets through faster, data-driven decision-making and enables more reliable and robust clutch design in early development stages.
Munisamy, Sathishkumar, Chollangi, Damodar, Mane, Sudhir
Analysis of Energy Consumption for Heating City Electric Buses and Ways to Reduce It2026-26-01561/16/2026
For regions with cold climate, the range of an electric bus becomes a serious restriction to expanding the use of this type of transport. Increased energy consumption affects not only the autonomous driving range, but also the service life of the batteries, the schedule delays and the load on the charging infrastructure. The aim of the presented research is to experimentally and computationally determine the energy consumption for heating the driver's cabin and passenger compartment of an electric bus during the autumn-winter operation period, as well as to identify and analyze ways to reduce this energy consumption. To determine the air temperature in the passenger compartment, a mathematical model based on heat balance equations was used. This model was validated using data from real-world tests. The research was conducted at a proving ground under two conditions: driving at a constant speed and simulating urban bus operation with stops and door openings. The causes of heat loss in electric bus heating were analyzed. It was found that approximately 35% of heat is lost through the ventilation system operating in fresh air intake mode, around 30% is lost due to door openings at stops, 17% dissipates through the windows, and 18% through the bus walls, floor, and roof. Using the mathematical model, a series of calculations were performed to estimate total energy consumption over the heating season for various heating systems and control system settings.
Kozlov, Andrey, Terenchenko, Alexey, Stryapunin, Alexander
Prediction of REESS Factor for SOC Correction in Hybrid Vehicles2026-26-04711/16/2026
The regulatory mechanisms to measure emissions from automobiles have evolved drastically over the years. Certification of CO2 emissions is one of them. It is not only critical for environmental protection but can also invite heavy fines to OEMs, if not complied with. In homologation test of a Hybrid Vehicle, it is necessary to correct the measured CO2 to account for deviations in measurement from failed Start-Stop phase and difference between start and end State of Charge (SOC) of battery. The correction methodology is also applicable for vehicle simulation in Software-in-Loop environment and for analyzing vehicle test data for CO2 emissions with programmed digital tools. The focus of this paper is on the correction of CO2 derived from SOC delta in the WLTP homologation drive cycle. The battery energy delta due to difference in SOC between start and end of drive cycle should be converted to corresponding CO2 expended from Internal Combustion Engine. The resulting correction factor is known as the REESS factor. To provide a reasonable correction factor for one type of engine in a particular car/weight class, a minimum of 3 measurements are required. Digitalization of the same will provide a significant cost benefit and a faster prediction of REESS factor with wider boundary condition of SOC balance applied. The current full vehicle simulation model was adopted to have better validation with REESS correction factors from measurement. A detailed analysis of the impact of operating strategy on the REESS correction factor is reviewed in this paper. The simulations are carried out on well validated models of different powertrain types. The aim of this study was to achieve a simulation setup which can predict REESS factor in tolerance range of +-0.03 (gCO2/km)/(Wh/km) in comparison to measurement.
Gopinath, Shravanthi Poorigali, Khatod, Krishna
Performance Evaluation of Battery Pack With E-Rickshaw on Chassis Dynamometer by Using In-House Developed Driving Cycle2026-26-01481/16/2026
The present work highlights a case-study that aims to determine the performance (power input/output) and battery temperature on in-house developed e-rickshaw battery pack. With the rise of e-rickshaws in Indian market, the demand for the batteries have also increased and being the largest state-run energy company of India, R&D Centre of Indian Oil Corporation Limited (IOCL) has developed a chemically modified nanomaterial-based lead acid battery. The lab scale experiments, which are not presented in the study due to confidentiality and intellectual property obligations, indicated that the nanomaterial doped lead acid battery pack performs better than the control (reference) and leading commercial batteries in terms of lifecycle, capacity etc. Subsequently, this paper highlights the performance with IOC R&D Centre manufactured 12V/100AH chemically modified (nanomaterial) lead acid battery pack for e-rickshaw on duty cycle developed indigenously based on the city driving experiences. The candidate battery pack of e-rickshaw were duly subjected for evaluation in comparison with control batteries (conventional lead acid configuration) along with leading commercial batteries. The performance tests for the e-rickshaw were conducted on chassis dynamometer which followed in-house charge-discharge cycles and measurement of power input & output with a sophisticated measurement device i.e., power analyzer. The nanomaterial impeded electroplates were made to enhance capacity and durability of lead acid battery and thereon the performance evaluation of e-rickshaws powered by nanomaterial-based control & commercial batteries were conducted in terms of Watt-hour (WH), Ampere-hour (AH), distance travelled per discharge cycle, durability & life cycle measurement by charging and discharging of batteries. Further, discharge (energy dissipation) of batteries were done by in-house developed driving cycle on chassis dynamometer. The cumulative distance covered under the driving cycle by nanomaterial powered e-rickshaw has shown significant improvement than the control and commercial batteries. Furthermore, it is concluded that in terms of durability, nanomaterial-based battery pack is better compared to control and commercial batteries for the SoC and battery temperature which are key performance parameters.
Saroj, Shyamsher, Sithananthan, M, Kumar, Prashant, Arora, Ajay, Sundaram, P, Kalita, Mrinmoy
Replacing Diesel Engines with CNG Engines – Impact on Emissions, Based on Chassis Dynamometer Tests of Medium and Heavy-Duty Buses2026-26-01271/16/2026
This paper compares carbon dioxide, carbon monoxide, methane, and oxides of nitrogen emissions from medium and heavy-duty buses using diesel, diesel-hybrid, and CNG powertrains. Comparisons are made using results from chassis dynamometer-based tests with driving cycles intended to simulate a wide range of operating conditions. Tail pipe emissions are measured by diluting the vehicle’s exhaust in a full-scale dilution tunnel by mixing with conditioned air. Samples are drawn through probes of raw exhaust, diluted exhaust and measured using laboratory grade emission analyzers. Fuel consumption of diesel is measured using a weighing scale, while a gas flow meter is used for measuring CNG consumption. Experimental data from 19 buses tested on a chassis dynamometer over the last 8 years has been analyzed and a comparison of results from similar buses with the differently fueled powertrains is presented. Based on these test results, it is shown that replacing diesel engines with CNG engines does not significantly reduce the emissions of carbon dioxide, while it increases carbon monoxide and methane emissions, reduces oxides of nitrogen emissions, and does not substantially help to reduce global warming.
Iyer, Suresh
CS Structure: New Metallic Catalyst Substrate Generation for 2- and 3-Wheeler Applications2026-26-02201/16/2026
Affordable, efficient and durable catalytic converters for the two and three-wheeler industry in developing countries are required to reduce vehicle emissions and to maintain them at a low level; and therefore, to participate in a cleaner and healthier environment. Especially, metallic catalyst substrates developed by Emitec Technologies GmbH with structured foils like the Longitudinal Structure (LS), or LS-Design® are fully compatible to this effort with more than 70% share of produced 2/3 Wheelers metallic catalyst substrates for the Indian market in 2024. One decade after the market introduction of this LS structure, Emitec Technologies GmbH will introduce now a new generation of foil structure: the Crossversal Structure (CS) or CS-Design®, that improves further the affordability, the efficiency of metallic catalytic converters, keeping the durability at same level as previous substrate generation. The paper will briefly review the development of metallic substrates for 2/3 wheelers applications, especially the development of structured foil substrates, describe the new foil structure CS, compare its performances to those of previously developed metallic substrates with structured LS foils. For this later purpose, experimental emission measurements under WMTC driving cycle on roller bench will be carried out on one Indian BS6 - OBD2 four stroke motorcycle. The results will be discussed and the benefits of CS for current and future motorcycle applications will be drawn.
Jayat, Francois, Seifert, Sven, Bhalla, Ashish, Ganapathy, Narayana Prakash
Optimizing System Design Performance and Its Accuracy Using Feature Importance2026-26-04211/16/2026
With advancements in model accuracy and computational power, system simulation is increasingly integrated into development tools as a “virtual test bed” alongside experimental testing. However, virtual vehicle and powertrain thermal models still face challenges, particularly in ensuring accuracy across systems developed by various internal and external sources. These models, often built using different software platforms, are difficult to validate consistently, especially when integrated in a Co-simulation environment. This integration can degrade the overall accuracy of the Vehicle Simulation Platform, reducing the return on investment in model development. To address these limitations, this paper proposes the use of machine learning-based feature importance techniques at the vehicle-level simulation stage. Feature importance helps identify the most influential variables affecting system outputs. By focusing calibration and validation efforts on these key variables, the approach aims to improve correlation with physical test data across drive cycles, targeting an accuracy threshold within ±90%
Srinivasan, Rangarajan, Sarapalli Ramachandran, Raghuveeran, Ashok Bharde, Pooja, Saravanan, Vivek
Representative Drive Cycle Velocity Profiles Prediction Using ML Algorithms for Energy Efficiency2026-26-06381/16/2026
Maximizing vehicle energy efficiency and its performance is a high priority for automotive industries as customers’ expectations rise. Engineers constantly face the challenge of balancing the conflicting goals of achieving superior performance and maximizing energy efficiency, all while meeting increasingly tight development timelines. Leveraging digital methods can potentially enable considerable reduction in development timelines. Driving cycles function as standardized measurement procedures for certifying vehicle fuel efficiency and driving range. Representative velocity profiles condense numerous real-life driving cycles to enable quicker energy analysis and driver feedback evaluations. This paper introduces a novel methodology for generating synthetic drive cycles, such as average velocity cycles and ideal consumption velocity cycles, based on real-life driving scenarios. In this study, the importance of creating representative drive cycles to enhance vehicle performance and energy consumption is highlighted. A detailed analysis of city cycle drives is provided, focusing on the prediction of average and ideal consumption velocity profiles using advanced machine learning, deep learning, and reinforcement learning techniques available in the field of data science. By leveraging data from numerous drives conducted at various ambient temperatures throughout the year, these velocity profiles were forecasted. The validation of these models is discussed in the paper, along with how effective and usable this method can be to save time in the overall vehicle development process. The merits and demerits of each ML model used to create synthetic drive cycles are discussed in the paper. This research offers valuable insights for automotive testing and development, significantly improving driver feedback analysis and providing essential insights to enhance driving habits and overall vehicle efficiency.
Kanakannavar, Rohit, Kelkar, Kshitij, Sadalge, Anand
Migration from MIDC to WLTP in India: Challenges & Solutions for a Small Commercial Vehicle2026-26-02151/16/2026
In line with global peers (EU, Japan, etc.), the Automotive Industry Standard (AIS) Committee in India has decided to adopt “World harmonized Light vehicle Test Procedure (WLTP)” for M2 and N1 category vehicles not exceeding 3500 kg and for all M1 category vehicles. As a result, “World harmonized Light-duty vehicles Test Cycle (WLTC)” is set to replace currently applicable “Modified Indian Drive Cycle (MIDC)” in the next couple of years. The draft Corporate Average Fuel Economy (CAFE) III & CAFE IV norms for CO2 emission limits, which are set to be implemented in year 2027 and 2032 respectively refer to a shift to WLTP from MIDC. The latest draft of Central Motor Vehicle Rules (CMVR) for BS-VI emissions is also being revised to use WLTC as test cycle. This migration to WLTC is in sync with the demand for test procedures to replicate real driving conditions more appropriately. Further, the move to WLTC along with stricter emission norms is a major step towards realizing India’s COP26 pledge to achieve net zero emissions by 2070. WLTC being much more dynamic with higher average speeds and acceleration compared to MIDC, has a major impact on the vehicle CO2 emission. Other gaseous pollutants like NOx also increase significantly with WLTC. This study takes into consideration the impact of change in test procedure on a conventional small commercial vehicle. Using the well validated models from FEV, simulations are performed to quantify the differences in various gaseous emissions under both MIDC and WLTC as well as under a typical Indian Real Driving Emissions (RDE) cycle with base calibration. Further, a step wise approach is detailed to have vehicles compliant with upcoming norms. The methodology lists both basic measures like calibration and hardware upgrades (e.g., change in injection pressure) as well as advanced measures including deploying additional technologies like advanced aftertreatment systems and hybridization etc. to improve fuel efficiency as well as reduce tail pipe emissions.
Pawar, Bhushan, Ehrly, Markus, Sandhu, Rouble, Emran, Ashraf, Berry, Sushil
Implementation of Rule-Based Controllers on an Electric Vehicle Thermal Management System Model in Simulink2026-01-50001/15/2026
Electric vehicles (EVs) are coming into usage quickly because of the environmental advantages and technological innovations. But among the most important issues in EV operation is effectively handling thermal loads, especially in the mobile air-conditioning (MAC) system. As opposed to internal combustion engine (ICE) vehicles, which have access to engine waste heat to use for climate control, EVs depend solely on the battery for propulsion and auxiliary systems. This renders the MAC system one of the primary energy consumers and directly influences vehicle range and overall efficiency. While MAC systems are inherently designed for energy efficiency, this study focuses on an addition to the controller-level optimization, providing an additional pathway to improve thermal management performance in existing EV architectures. The work uniquely implements and compares five rule-based supervisory controllers (RBCs) on an open-source Simulink-based electric vehicle thermal management (EVTM) model, demonstrating a simple and computationally efficient approach to compressor control. Five different RBC strategies are formulated, each of which controls the compressor depending on factors such as ambient temperature, cabin temperature variation, and battery thermal load. The controllers are tested over three varied driving cycles to determine their robustness: the Worldwide Harmonized Light Vehicles Test Procedure (WLTP) Class 2 cycle, the New European Driving Cycle (NEDC), and Bangalore Drive Cycle. These varied test cycles allow for examination over different traffic patterns, speed profiles, and environmental conditions. Simulation results show the optimum RBC delivers an optimal compressor power saving of 4.38% compared to a baseline control strategy.
Akkalkot, Yash Satish, Vidyasagar, Shekhar, Raju, Tarun L., Vaasuki, G., Kiran, M.
On Optima Selection Strategies of Adversarial Swarm Learning for Energy Management of PHEVs2025-01-732012/31/2025
Developing robust optimization and learning methods is necessary for intelligent vehicles since an increasing number of critical control functions will be handled by artificial intelligence. This paper proposes an adversary swarm learning (ASL) system and an optima selection strategy for robust energy management of plug-in hybrid electric vehicles (PHEVs). The proposed ASL system comprises an attacking swarm and a defending swarm, which compete against each other iteratively to derive the most robust equivalent consumption minimization strategy (ECMS) for PHEV energy management. During the attacking rounds, the ECMS settings are fixed by the defender. Meanwhile, the attacker generates worst-case driving conditions by training a model in order to Maximize the equivalent energy consumption. During the defending rounds, the ECMS settings are optimized by the defender based on the driving scenarios generated by the attacker. The settings of robust ECMS are derived by introducing the concepts of “attacking rate” and “defending rate”, based on the ASL system’s minimum attack rate (MAR) and maximum defence rate (MDR). The proposed methods demonstrate the superiority over traditional ECMS, which was optimized across four standard driving cycles as a baseline, on both software-in-the-loop and hardware-in-the-loop platforms. The experimental result shows that the robust ECMS derived through the ASL system outperforms the baseline across all tested driving scenarios. Significantly, the ASL system employing the MAR strategy proves more effective. Its average cost-reducing rate is 10.58% higher than the baseline.
Zhong, Danyang, Yu, Zhuoping, Xiong, Lu, Zhou, Quan
Vehicle Gear Shifting Optimization for Fuel Consumption and Emissions Minimization2025-36-011112/18/2025
Vehicles powered by internal combustion engines play a crucial role in urban mobility and still represent the vast majority of vehicles produced. However, these vehicles significantly contribute to pollutant emissions and fossil fuel consumption. In response to this challenge, various technologies and strategies have been developed to reduce emissions and enhance vehicle efficiency. This paper presents the development of a solution based on optimized gear-shifting strategies aimed at minimizing fuel consumption and emissions in vehicles powered exclusively by internal combustion engines. To achieve this, a longitudinal vehicle dynamics model was developed using the MATLAB/Simulink platform. This model incorporates an engine combustion simulation based on the Advisor (Advanced Vehicle Simulator) tool, which estimates fuel consumption and emissions while considering catalyst efficiency under transient engine conditions. Based on these models, an optimization method was employed to determine the optimal gear-shifting strategy, enabling the analysis of vehicle performance, fuel consumption, and emissions over a driving cycle under different gear-shifting configurations.
Da Silva, Vitor Henrique Gomes, Carvalho, Áquila Chagas, Lopez, Gustavo Adolfo Gonzales, Casarin, Felipe Eduardo Mayer, Dedini, Franco Giuseppe, Eckert, Jony Javorski
Control Strategies for a Hybrid Electric-Combustion Powertrain Applied to a Test Bench2025-36-024612/18/2025
This paper presents the design and implementation of a test bench intended for the development and validation of control strategies applied to a hybrid-electric powertrain. The setup combines a 48 V SEG BRM electric machine with a small-displacement internal combustion engine (ICE), the HONDA GX160, operating in a parallel hybrid configuration. The platform was developed to improve energy efficiency in comparison to a conventional ICE-only system. Modifications were carried out on an existing test bench at Instituto Mauá de Tecnologia, including the fabrication of a new enclosure for the battery pack and its battery management system (BMS), as well as the integration of a Vector VN8911 real-time controller. A custom control strategy was implemented and experimentally evaluated using a predefined drive cycle under two conditions: (I) ICE-only operation and (II) hybrid-electric operation with the proposed strategy. Results showed a fuel consumption reduction of approximately 13% with the hybrid-electric configuration. The comparability of both tests was ensured by maintaining equivalent initial and final battery state of charge (SoC), allowing a fair assessment based solely on fuel consumption.
Polizio, Yuri, Zabeu, Clayton, Pasquale, Gian, Pinheiro, Giovana, Vieira, Renato
Fuzzy Logic-Based Traction Control for a 4WD In-Wheel Electric Formula Vehicle2025-36-011412/18/2025
Vehicle dynamic control is crucial for ensuring safety, efficiency and high performance. In formula-type electric vehicles equipped with in-wheel motors (4WD), traction control combined with torque vectoring enhances stability and optimizes overall performance. Precise regulation of the torque applied to each wheel minimizes energy losses caused by excessive slipping or grip loss, improving both energy efficiency and component durability. Effective traction control is particularly essential in high-performance applications, where maintaining optimal tire grip is critical for achieving maximum acceleration, braking, and cornering capabilities. This study evaluates the benefits of Fuzzy Logic-based traction control and torque distribution for each motor. The traction control system continuously monitors wheel slip, ensuring they operate within the optimal slip range. Then, torque is distributed to each motor according to its angular speed, maximizing vehicle efficiency and performance. Thus, a longitudinal dynamic model was implemented in MATLAB/Simulink, incorporating traction forces, rolling resistance, aerodynamic drag and downforce, and load transfer during acceleration and braking. Tire grip was also modeled using the Pacejka formula, with data from the Tire Test Consortium (TTC). As a result, the model allows the calculation of acceleration, velocity, position, and the vehicle’s slip ratio. To simulate vehicle dynamic behavior, a representative driving cycle was defined and associated with an auxiliary control that emulates the driver throttle and braking inputs, aiming to match the desired speed profile. This approach allows the development and calibration of the fuzzy logic traction control, optimizing the vehicle performance.
Oliveira, Vivian Fernandes, Hayashi, Daniela Tiemi, Dias, Gabriel Henrique Rodrigues, Andrade Estevos, Jaqueline, Guerreiro, Joel Filipe, Ribeiro, Rodrigo Eustaquio, Eckert, Jony Javorski
Advanced Compliance Risk Assessment Using Big Data from Fleet Intelligence Data2025-28-034311/6/2025
In-Use emission compliance regulations globally mandate that machines meet emission standards in the field, beyond dyno certification. For engine manufacturers, understanding emission compliance risks early is crucial for technology selection, calibration strategies, and validation routines. This study focuses on developing analytical and statistical methods for emission compliance risk assessment using Fleet Intelligence Data, which includes high-frequency telematics data from over 500K machines, reporting more than 1000 measures at 1Hz frequency. Traditional analytical methods are inadequate for handling such big data, necessitating advanced methods. We developed data pipelines to query measures from the Enterprise Data Lake (A Structured Data storage system), address big data challenges, and ensure data quality. Regulatory requirements were translated into software logic and applied to pre-processed data for emission compliance assessment. The resulting reports provide actionable insights on NOx sensor activity, engine warmup operations, high-risk drive cycles, and load profiles across different operation regimes. This approach significantly reduces the reliance on costly and labor-intensive physical testing with Portable Emissions Measurement Systems (PEMS) by integrating advanced analytical methods into the workflow. By leveraging high-frequency telematics data, this method enables engineers to identify failed machines in the field more efficiently. It also provides valuable insights and reasoning behind these failures, facilitating quicker and more informed decision-making. This not only enhances emission compliance monitoring but also optimizes resource allocation and reduces overall regulatory risks. In summary, the developed methods enable effective emission compliance monitoring, reduce regulatory risks, and help optimize calibration strategies by understanding customer usage patterns. These methods are scalable for various emission regulations.
Arya, Satya Prakash, Shekarappa, Kiran
A Simulation Approach for the Impact Assessment of an Axial Flux Traction Motor Applied on Road Electric Vehicle2025-32-007711/3/2025
In the transition towards sustainable mobility, Circular Design principles are crucial. Electric Motors are subject to continuous innovation to improve efficiency, performance density and reduce externalities associated with their production. Therefore, the choice of technological solutions during design phase must guarantee optimal performance and minimal environmental impact throughout the entire product life cycle: production, use, and end-of-life. In the automotive sector, the use phase is particularly critical since the efficiency of the traction system is directly related to total energy consumption during the life cycle and, consequently, to its environmental impact. This research introduces a simulation-based approach to evaluate the use phase of an Axial Flux Electric Motor equipped with Permanent Magnets (AFPM). While providing high performance for electric traction motors, these magnets are composed of Rare Earth Elements (REEs), e.g. Neodymium, classified as Critical Raw Materials (CRMs) due to limited availability and environmental concerns associated with extraction and processing. However, the high torque and power density of this motor technology can potentially reduce the use of CRMs compared to other design solutions. The primary objective of this study is to show a preliminary scalable model that allows designers to evaluate motor performance under different design choices and use scenarios, defined through standard or custom driving cycles, providing immediate feedback in terms of environmental impact. The latter is evaluated by analyzing the powertrain’s energy consumption and efficiency using a road vehicle model, compiling the use phase inventory quickly, and simplifying access to information. This preliminary model thus serves as a decision-support system to balance performance optimization and environmental sustainability during the design phase. This work is part of a framework aimed at improving circularity of industrial products, particularly in the automotive industry. Incorporating environmental factors in design phases encourages innovative solutions that enhance efficiency and decrease reliance on limited resources.
Guadagno, Maurizio, Berzi, Lorenzo, Pugi, Luca, Delogu, Massimo
Real Driving Emission (RDE) Assessment for Motorcycles Using a Random Cycle Generator (RCG)2025-32-007111/3/2025
To conserve the atmospheric environment, regulations on vehicle exhaust gas emissions have become increasingly stringent. For Light Duty Vehicles (LDVs), Real Driving Emission (RDE) assessments based on Portable Emission Measurement Systems (PEMS) have been introduced. However, the application of PEMS measurements to motorcycles presents several challenges, including reduced measurement accuracy owing to the small engine displacement and number of cylinders and increased motorcycle weight owing to PEMS installation. Therefore, an alternative evaluation method that does not rely on the PEMS is required. In this study, we developed a Random Cycle Generator (RCG) to provide an evaluation method that can be used in a laboratory environment. The RCG enables the evaluation of driving cycles by combining different motorcycle speed patterns. It can generate arbitrary driving cycles that consider the average and upper limits of regional driving characteristics, thereby enabling accurate emission measurements to be performed in a laboratory. Thus, the RCG-based method is considered a viable alternative to the PEMS-based RDE assessment.
Matsuoka, Masahiro, Hirai, Hiroshi, Ito, Takayuki
Emission Performance of Motorcycles: Regulated and Non-Regulated Pollutants under Harmonized and Extended Testing Conditions2025-32-006911/3/2025
This study investigates emissions from motorcycles, focusing on both regulated gaseous pollutants (e.g., CO, NOx, HC) and particulate number (PN) emissions, which are non-regulated for this vehicle category in the actual EU emission regulation. Using a state-of-the-art testbench setup equipped with advanced exhaust gas analysis and particle measurement programme (PMP) system, emissions were analyzed under both standardized homologation cycles (WMTC) and more dynamic Real Driving Cycles (RDCs). Besides the measurement results the technological differences between different motorcycle categories are described. This is followed by a discussion of the influences of engine and exhaust gas aftertreatment systems on emission. The findings reveal, that there are two different subcategories of two-wheeler, which show different emission characteristics. L1e vehicles showed increased emissions compared to passenger cars, caused by the absence of advanced exhaust aftertreatment and on-board diagnostic systems and enabled by less stringent regulations and technical constraints. L3e vehicles in contrast own comparable exhaust aftertreatment systems to passenger cars, but are operated in higher dynamics and therefore show emissions up to three times higher under real-world conditions compared to standardized test cycles, caused by high-load phases, acceleration enrichment, and distinct operating characteristics. Besides regulated emission components, the results show significant values of PN emissions of motorcycles compared to passenger cars. Stricter regulations including PN limits, along with the development of more realistic testing methods and test cycles tailored to motorcycles' unique operational characteristics, are essential to lower real world particulate emission. These measures are vital to mitigate the environmental impact of motorcycles and to achieve reasonable emissions reductions.
Schurl, Sebastian, Schmidt, Stephan, Bretterklieber, Niko, Kupper, Martin, Kirchberger, Roland
Forward-Looking SiL-Based Fuel Consumption Simulation for Dual-Clutch Transmission Software Evaluation2025-32-007011/3/2025
Evaluating the impact of software changes on fuel consumption and emissions is a critical aspect of transmission development. To evaluate the trade-offs between performance improvements and potential negative effects on efficiency, a forward-looking Software-in-the-Loop (SiL) simulation has been developed. Unlike backward calculations that derive fuel consumption based solely on cycle speed and engine speed, this approach executes complete driving cycles as the Worldwide Harmonized Light-Duty Vehicle Test Cycle (WLTC) within a detailed SiL environment. By considering all relevant influencing factors in a dynamic simulation, the method provides a more accurate assessment of fuel consumption and emission differences between two versions of the transmission software. The significant contribution of this work lies in the high-fidelity integration of a real virtual Transmission Control Unit (vTCU) software within a comprehensive, validated forward-looking SiL environment. This approach enables precise delta comparisons that capture transient dynamic interactions, facilitating early, reliable software testing and validation.
Kengne Dzegou, Thierry Junior, Schober, Florian, Rebesberger, Ron, Henze, Roman
Experimental Study on Single Cylinder Diesel Engine Port Water Injection and Blends of Biodiesel for Performance & Emission2025-32-006211/3/2025
The increasing demand for alternative fuels due to environmental concerns has sparked interest in biodiesel as a viable substitute for conventional diesel. Most automotive engines use diesel fuel engines. They contribute a major portion of today’s air pollution, which causes serious health issues including chronic bronchitis, respiratory tract infections, heart diseases, and many more. Greenhouse gases are produced using fossil fuel in the engines and causes global warming. To combat air pollution, we need clean renewable and environmentally friendly fuels. Due to depletion of fossil fuels, it has become necessary to find alternative fuel which are safer for the environment and humankind. One such possible solution is Biodiesel. In present study, series of experiments were carried out on 435cc naturally aspirate DI Diesel engine with port water injection and different blend of Jatropha based Biodiesel. Biodiesel was derived from Jatropha oil, produced using a heterogeneous catalyst. The physical and chemical properties were determined for different blends of Biodiesel specifically JB20, JB30 and JB100 before engine testing. Engine tests were recorded on engine eddy current dynamometer and vehicle emission were recorded on chassis dynamometer to investigate the performance and emission characteristics of different biodiesel blends compared with conventional diesel. Recorded engine test performance infers improvement in observed torque and power. BSFC and Smoke results were comparable on full load and part load conditions. There is reduction in HC, CO raw emission with JB20 and JB30 Biodiesel fuel as compared to conventional Bharat Stage 6 Diesel fuel. Particulate Matter are comparable with all the above fuels. Vehicle mass emission with combination of port water injection and JB20 biodiesel has benefited further to reduce HC & NOx emission to meet Bharat Stage 6 emission norms on Diesel three-wheel vehicle on Indian Driving cycle. These findings highlight the potential of Jatropha biodiesel as a cleaner alternative to conventional diesel.
Bhoite, Vikram, Syed, Kaleemuddin, Chaudhari, Sandip, Khairnar, Girish, Jagtap, Pranjal, Reddy, Kameswar
Model-Based Design and Numerical Analysis of a Downsized Centrifugal Pump for Engine Cooling Applications2025-32-007811/3/2025
The water pump is the crucial component of the engine cooling system. It is usually designed considering as rated conditions the ones evaluated when the engine delivers its maximum power. This results in an overdesign of the pump, considering that almost never the engine delivers the maximum power, in usual operation. At these conditions, in fact, flow rate and pressure delivered reach the maximum values, which are not needed to cool the engine in most probable operating conditions. In fact, considering the real operating conditions during a typical driving mission or a homologation cycle, the mechanical power is far away from the maximum datum, as well as the cooling flow rate and pressure delivered by the pump. To a so unbalanced design for the pump corresponds a low efficiency of it, being the technology oriented to use a centrifugal type, whose efficiency is quite dependent on speed of revolution and flow rate delivered. Hence, modifying the design point of the pump causes a mechanical energy saving, improving the organic efficiency and reducing the efficiency penalization when it operates, as it happens always, at off design conditions. In this work, a model-based procedure to design a centrifugal pump in a more suitable engine working point from the point of view of the energy absorbed is considered. The procedure starts with an estimation of the engine thermal needs in different working conditions and on a driving cycle. Hence, a flow rate is targeted, and a pressure drop of the cooling circuit estimated, to have the specifics of the pump design. The model is able to evaluate all the hydraulic losses of the pump in its impeller and volute. The geometry generated has been refined and finally investigated through numerical CFD analysis. Subsequently, the turbulent flow field of the pump was analyzed in terms of static pressure, velocity, and kinetic energy distribution. The pump head and flow rate delivered were simulated using CFD techniques and compared with experimental results, assessing also the efficiency of the pump and the loss distribution. Finally, the pump performance has been evaluated along a driving cycle, to assess the energy absorbed during a real operating condition.
Di Battista, Davide, Deriszadeh, Ali, Di Prospero, Federico, Di Giovine, Giammarco, Di Bartolomeo, Marco, Fatigati, Fabio, Cipollone, Roberto
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