Browse Topic: Energy consumption

Items (3,012)
Find
Research on Economy Mode Control Algorithm for Pure Electric Tractor with Powered Trailer2026-01-0425To be published on 04/07/2026
This research presents a comprehensive investigation into the electrification development pathway and energy-saving optimization methodologies for commercial tractor vehicles. Given the pivotal role that commercial vehicles play in modern logistics and transportation sectors, their propulsion systems are undergoing a fundamental transition from traditional internal combustion engines toward more sustainable hybrid and pure electric architectures. Simultaneously, the emerging trend of trailer electrification has gained considerable attention, where conventional trailers are retrofitted with motorized axles to form cooperative powertrain systems, demonstrating substantial energy-saving potential as confirmed by multiple research studies. Utilizing an integrated Trucksim and MATLAB/Simulink co-simulation platform, this study developed sophisticated vehicle dynamics models, detailed electric drive system models, and advanced battery models, which were subsequently integrated to establish a
tong, zi yu, Zheng, Hongyu, Li, Gang
Energy Management Strategy for Hybrid Mining Trucks Based on Global SOC Planning and Neural Network Optimal Control2026-01-0446To be published on 04/07/2026
Hybrid mining trucks, as core equipment for mine transportation, face high energy consumption and significant fluctuations in power demand during cyclic operations due to prolonged exposure to demanding operating conditions characterized by heavy loads and variable working conditions. To address the issues of high energy consumption and significant fluctuations in power demand during the cyclic operation of mining trucks, this paper proposes a hybrid mining truck energy management strategy based on global SOC (State of Charge) planning and neural network optimization control. First, a powertrain model was developed for a typical operating cycle of a hybrid mining truck, and its accuracy was validated by comparing it with experimental data. Using dynamic programming algorithms to plan the SOC for single-cycle operations provides a rational reference for energy allocation across different operational phases of mining trucks during a single cycle. Next, using the powerful nonlinear
Yang, Jianyu, Zhao, Zhiguo, Chen, Huiyong, Li, Tao, ZHUANG, Wenyu, Shen, Peihong, Tang, Peng
ENERGY AND OPERATIONAL EFFICIENCY OF SHARED AUTONOMOUS FLEET2026-01-0464To be published on 04/07/2026
Electrifying shared autonomous fleets (Robotaxis) presents challenges in balancing decarbonization, service quality, and operational costs, given the limited driving range, long charging times, and suboptimal planning of charging infrastructure. This study develops an integrated energy management and fleet dispatching simulation framework to support cost-effective, low-carbon Robotaxi deployment. The proposed system models both battery electric vehicles (BEV) and internal combustion engine vehicles (ICEV) technologies, and is extensible to other powertrain types. The study also integrates a life cycle assessment module to evaluate well-to-wheel carbon emissions. A total of 1,440 scenarios are designed to test the performance of two service modes (ride-hailing vs. ride-pooling) in terms of energy consumption, emissions, service quality, and operational costs, across varying levels of trip demand and market penetration of different powertrain technologies. The test aims to verify the
Tang, Kang, Abdulsattar, Harith, Yang, Hao, Wang, Jinghui
Modelling of Le Mans Daytona Hybrid Hypercar Powertrain System2026-01-0270To be published on 04/07/2026
This study presents the development, validation, and application of a full-vehicle simulation model of the BMW M Hybrid V8 LMDh Hypercar, designed to investigate and optimise hybrid energy recovery strategies in compliance with FIA and IMSA 2025 technical regulations. With increasing regulatory and sustainability demands in endurance racing, the deployment and recovery of electric energy using MGU-K have become pivotal for both performance and fuel efficiency. A physics-based simulation model was constructed in GT-SUITE, incorporating a map based internal combustion engine, transmission, and 50 kW rear-axle hybrid system. Complementary to this, a novel telemetry extraction method using Optical Character Recognition (OCR) in MATLAB and Python was developed to generate real-world drive cycles from on-board race footage. The model was validated using telemetry data. Subsequently, multiple energy deployment strategies were evaluated under varying race scenarios to identify optimal control
Buysse, Alexander, Samuel, Stephen
Integrated Simulation Framework for Multi-Objective Electric Vehicle Powertrain Design: Energy, Cost and Environmental Impact Optimization2026-01-0508To be published on 04/07/2026
Eco-design represents a key challenge in vehicle development, as increasingly stringent regulations and rising customer performance expectations require the optimal combination of environmental considerations and efficiency. This work presents three integrated simulation tools for electric vehicle (EV) powertrain design, targeting energy consumption, cost optimization, and environmental impact assessment. Three complementary models were developed and validated: a dynamic energy consumption model, a comprehensive cost estimator, and a life cycle carbon footprint assessment tool. Validation against real-world data demonstrated residuals consistent with existing literature, with prediction errors under 9% for energy efficiency and cost models. This integrated approach enables designers to account for the complete life-cycle impact of the vehicle being designed while optimizing component sizing for specific behavioral usage patterns, thereby avoiding costly oversizing. As an example of
Maréchal, Bérénice, Carlos Da Silva, Daniel, AHNINE, bouchra, El Ganaoui-Mourlan, Ouafae, Couillandeau, Matthieu, Miliani, El Hadj, PETIT, Quentin
A System-Level Calibration Framework for Embedded Vision: Integration of Sensor, Firmware, and Software Enhancements2026-01-0013To be published on 04/07/2026
Embedded vision systems are essential for contemporary applications, including robotics, advanced driver assistance systems (ADAS), and intelligent surveillance; yet they frequently experience diminished image quality due to resource constraints, environmental variability, and inconsistent illumination conditions. Such degradations impact multiple visual attributes-sharpness, contrast, color accuracy, noise levels, and structural similarity-that are critical for reliable perception in safety- and performance-driven domains. This study introduces a comprehensive system-level calibration architecture that integrates three coordinated layers: sensor-level adjustment, firmware optimization, and adaptive software enhancements. At the sensor level, exposure control, gain tuning, and white balance adjustments mitigate luminance imbalance and color shifts under changing light conditions. Firmware optimization leverages image signal processor (ISP) parameters to reduce temporal and spatial
Indrakanti, Rama Kiran Kumar, Vishnoi, Nitin, KAMADI, VENKATA
Evaluating the Impact of Drag Coefficient Changes on Driving Energy Using a Probability Distribution Model of Yaw Angle Based on Empirical Data from North American Highways2026-01-0615To be published on 04/07/2026
This study estimates the impact on driving energy of differences in aerodynamic characteristics for yaw angle from natural wind during North American Highway mode driving. A previous study[1] clarified the potential to estimate the fuel consumption impact of natural wind by integrating the CD yaw characteristics and yaw angle occurrence frequency. The natural wind was measured on a vehicle while driving a representative North American Highway test course[2]. The driving energy is predicted from the obtained yaw frequency and CD yaw sweep data in a wind tunnel. Measurements were conducted every weekday for 8 hours in 2023, covering 70% of the traffic volume. The validity of the measurement period was evaluated by the deviation from the annual average of wind direction and speed. Since yaw frequency varies depending on the road environment, it is necessary to weight the road environment type frequency when calculating the driving energy. The frequency was calculated using machine
Onishi, Yasuyuki, Nucera, Fortunato, Nichols, Larry, Metka, Matt
Look-ahead Information based Predictive Cruise Controller for Energy Efficient Powertrain Operation of Heavy-Duty Electric Trucks2026-01-0039To be published on 04/07/2026
Heavy-duty electric trucks represent a growing innovation in the transport and logistics sector, aiming to reduce emissions and reliance on fossil fuels. A major challenge with battery electric trucks is the long recharging time which takes significantly longer than refueling conventional diesel trucks. This limitation highlights the importance of optimizing powertrain operations to reduce energy losses and maximize efficiency. One effective approach is implementing optimal speed control through a predictive cruise controller. By anticipating road conditions, traffic, and elevation changes, the predictive cruise controller can adjust the truck’s speed in real time to minimize energy consumption, enhancing the range and reducing the need for frequent charging. Many problem formulations for electric trucks focus primarily on minimizing the energy required at the wheels, often overlooking the impact of powertrain efficiencies. This simplification neglects critical factors such as the
Safder, Ahmad Hussain, Villani, Manfredi, Khuntia, Satvik, Nelson, James, Meijer, Maarten, Ahmed, Qadeer
Radar-Based Dynamic Pixel Activation in Camera Sensors for Optimized Image Processing, Power Efficiency, and Thermal Management2026-01-0047To be published on 04/07/2026
This paper presents a radar-based dynamic imager pixel activation method designed to optimize image processing efficiency, power consumption, and thermal management in automotive In-cabin monitoring systems (DMS and OMS). By integrating radar sensing with high-resolution cameras, the proposed approach selectively activates image sensor pixels only within radar-identified regions of interest, such as moving occupants or key cabin areas. This selective pixel activation substantially reduces the computational workload and data bandwidth required, as the camera transmits high-resolution data solely for relevant zones while maintaining lower resolution or deactivating pixels elsewhere. The fusion of radar and camera data enables adaptive sensor control that caters to distinct scenarios, including driver gaze tracking and passenger monitoring, thereby enhancing DMS and OMS functionality. The architecture supports mounting configurations such as headliner or rearview mirror installations
Kasarla, Nagender Reddy, Patel, Krunal, Balaraman, Nirmal Kumar
Real-time Parameter Optimization for Eco-driving Control in Connected and Automated Vehicles Using Reinforcement Learning2026-01-0041To be published on 04/07/2026
This paper introduces a novel methodology to enhance the energy efficiency of eco-driving controllers in Connected and Automated Vehicles (CAVs) by leveraging Reinforcement Learning (RL) techniques for real-time parameter optimization. Traditional eco-driving strategies rely on fixed control parameters, which limit adaptability across diverse traffic and road conditions. To address this, we apply continuous action space RL algorithms, specifically Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO), to dynamically tune four key parameters within a model predictive control framework that is grounded in Pontryagin’s Maximum Principle (PMP). These parameters influence acceleration, braking, cruising, and intersection-approach behaviors, making them critical for achieving optimal eco-driving performance. Our study employs Argonne National Laboratory’s RoadRunner simulator, a Simulink-based environment designed for high-fidelity CAV analysis, incorporating
Zhang, Yaozhong, Ammourah, Rami, Han, Jihun, Moawad, Ayman, Shen, Daliang, Karbowski, Dominik
Time-of- Flight (ToF) estimation using the Discrete Logarithmic Frequency (DLF) method2026-01-0019To be published on 04/07/2026
When incorporated into the small, curved, and enclosed spaces of contemporary automotive lighting systems, conventional measurement tools like photodetectors, optical sensors, and laser-based systems frequently encounter difficulties. Physical space restrictions, light attenuation along curved surfaces, and the high energy consumption of conventional sensors—particularly in intelligent headlight modules and advanced driver-assistance systems (ADAS)—are some of these limitations. To address these issues, this work presents a novel method for Time-of-Flight (ToF) estimation based on the Discrete Logarithmic Frequency (DLF) approach. The suggested method improves the accuracy and dependability of ToF-based measurements by examining the time-frequency correlations of modulated light signals sent through optical channels in automotive systems. Applications like adaptive beam shaping, vehicle-to-vehicle (V2V) optical communication, and in-lens diagnostics benefit greatly from this. The
Chinni, Venkata Sai Sandeep
Predictive Battery Preconditioning Strategy Considering Charging Time, Battery Degradation, and Energy Consumption2026-01-0126To be published on 04/07/2026
During fast charging, battery temperature plays a critical role in determining charging time, battery degradation, and auxiliary energy consumption. If the battery is not within the optimal temperature range at the start of charging, performance can deteriorate significantly. To enable optimal performance, the battery must be thermally preconditioned before arriving at the charging station. This work presents an optimal battery preconditioning strategy that considers charging time, battery degradation, and energy consumption. The approach utilizes route-based velocity prediction and optimizes the battery thermal management strategy on the way to the charging station using nonlinear programming. The optimization is based on precomputed maps that represent charging time, degradation, and energy consumption as functions of battery temperature and state of charge at the start of charging. These maps are generated through fast charging simulations using a validated high-fidelity simulation
Acker, Lukas, Hofmann, Peter, Konrad, Johannes
Energy Management Optimization of Sweepers Based on Working Conditions2026-01-0505To be published on 04/07/2026
Against the backdrop of the accelerating global transition to low-carbon development, the electrification of sweepers holds significant importance. Range-extended sweepers have emerged as a vital branch of new energy sanitation vehicles. Influenced by their service characteristics, these vehicles typically exhibit distinct features such as relatively fixed routes, concentrated operational areas, and highly regular working cycles. However, current range-extended sweepers employing conventional energy management strategies often demonstrate shortcomings: upon completing tasks such as sweeping and transportation, the battery State of Charge (SOC) frequently remains at an elevated level, leading to redundant energy waste. Meanwhile, the frequent start-stop cycles of the range extender during operations not only compromise the stability and service life of the power system but also reduce the overall utilization efficiency of battery energy. To address these issues, this study focuses on
Dong, Chenchen, Tan, Gangfeng, Wei, Haoting, Liu, Chongjian, Liu, Dan zhong
BEV incremental CO2 emissions in a ranked order electric grid2026-01-0424To be published on 04/07/2026
Battery Electric Vehicles (BEV) have been promoted and required by many governments towards the need to reduce transportations CO2 emissions. BEV are not “Zero Emissions Vehicles” because they run on grid electricity, but could be ‘effectively ZEV’ if the incremental CO2 is ‘very small’. To answer this question at the national level, three metrics are needed and estimated: (1) ICEV fuel consumption, approximated by the total US gasoline consumption divided by the total fleet miles driven, 25 mpg or 350 g CO2/mi. For strong HEV it is about one third less, 240 g CO2/mi. (2) BEV energy consumption, using data from systematic on-road testing of a wide range of vehicles, estimated at 40 kWh/100 mi for a US sales mix. (3) Electric grid marginal CO2, with ranked order sourcing: zero-CO2 sources are prioritized and supplemented by fossil sources. IEA hourly data show that the US 48 contiguous states are nearly self-contained, with zero-CO2 sources providing about a third of total demand. The
Phlips, Patrick
Development of the Honda S+ Shift Utilizing e:HEV System2026-01-0418To be published on 04/07/2026
Honda is promoting mobility electrification to realize a carbon-neutral society by 2050. Hybrid vehicles will remain advantageous over electric vehicles in terms of manufacturing cost and driving range until renewable energy usage increases, charging infrastructure is sufficiently developed, and battery costs are reduced. In response to this situation, Honda has developed a new control system, “Honda S+ Shift”, which further enhances the “emotional value of driving pleasure” inherent to the e:HEV system and creates new value for hybrid vehicles. Honda S+ Shift synchronizes the engine and vehicle speed and selects a virtual gear position according to the driver's operation such as acceleration, cornering, and deceleration. Subsequently, the system achieves the required system output in cooperation with a dedicated energy management system. It also works with each vehicle system, such as drive force control, sound control, and meter cluster, to stimulate all five senses of the driver
Murata, Naoya, Narimoto, Ryosuke, Saito, Masatoshi, Ishida, Daichi, GUNJI, HIROKI, UKAI, YOHEI, Kurachi, Shinobu, SHIKI, KAZUKI, Nagakura, Akari, Maeda, Sadaharu, Mitogawa, Terumasa
Research on MPC-based Control of Electric Active Anti-roll Bar2026-01-0632To be published on 04/07/2026
To enhance vehicle lateral stability and anti-roll performance, a model predictive controller (MPC) is designed for an electrically driven active anti-roll bar. First, a vehicle roll dynamics model and an active anti-roll bar model based on motor drive are established. Then, model predictive controllers for the front and rear suspension anti-roll bars are designed to calculate the anti-roll moments for the front and rear suspensions. Finally, co-simulation using Carsim and Simulink is conducted to simulate typical vehicle driving scenarios. The simulation results of passive suspension, PID and MPC control are compared to validate the improvement in vehicle stability achieved by MPC control, and the sensitive parameters of the controller affecting anti-roll performance are analyzed. The research results show that, compared to passive suspension and traditional PID control, the maximum roll angle with model predictive control is reduced by 80% and 40%, respectively. The MPC controller
Shen, Leiming
A Real-Time Vehicle Detection and Counting System Using a GUI Interface.2026-01-0156To be published on 04/07/2026
The growing need for intelligent traffic monitoring and environmental control has fuelled the importance of automated vehicle detection and counting systems. Fuel consumption estimation, vehicular emissions calculations, and traffic pattern comprehension- all play a vital role in shaping sustainable environmental policies. Therefore, accurate identification and counting of vehicles is essential. In this paper, we have shown the development of a real-time vehicle detection and counting application that uses computer vision and deep learning techniques to effectively detect, categorise, and count several types of vehicles on Indian roads. The computer vision vehicle counting application uses the YOLO (You Only Look Once) deep learning model for real-time identification and categorisation of diverse vehicle types, including cars, buses, trucks, and motorbikes [1]. A tracking module based on the SORT (Simple Online and Real-time Tracking) algorithm ensures unique identification of vehicles
Gusain, Ashna, Maurya, Akshay, PATHAK, SUNIL, Khan, Tuhin
A Comprehensive Modelling tool for Heavy-Duty Hybrid Fuel Cell/Battery Powertrains2026-01-0430To be published on 04/07/2026
The global transition towards sustainable transportation is driving the development of efficient, low-emission propulsion systems. Battery-electric solutions are effective in urban contexts but face limitations in heavy-duty and long-haul applications due to the size and weight of the required energy storage. Hybrid battery/fuel cell powertrains offer a promising alternative for such use case, reducing vehicle mass and charging times while maintaining high energy efficiency. This study presents a zero-dimensional MATLAB/Simulink model of a parallel hybrid fuel cell/battery system for heavy-duty vehicles. The model encompasses the main vehicle subsystems, including the fuel cell stack with auxiliaries, the battery pack, the electric drive, the transmission and vehicle longitudinal dynamics, coordinated through a rule-based energy management strategy. Two representative vehicle configurations were analysed: a Group 2 urban delivery vehicle, and a Group 5 long-haul truck. Model validation
Montecchi, Gianluca, Martoccia, Lorenzo, D'Adamo, Alessandro
Assessing Energy Use, Cost, and Emissions of Small Regional Rail Vehicles: Methodology and Case Study on a German Track2026-01-0419To be published on 04/07/2026
The decarbonization of regional rail remains a pressing challenge in the European Union, where nearly half of the 200,000 km network remains unelectrified. Lightweight, single-car railbuses historically provided efficient service on such lines, but modern replacements are scarce. This work evaluates a standardized 30-ton, 16 m railbus platform for unelectrified regional service, focusing on propulsion system design and trade-offs between range, cost, and emissions. A MATLAB/Simulink drive-cycle model was developed to simulate energy consumption under realistic operating conditions. The Erfurt–Rennsteig route in Germany (130 km round trip, gradients up to 6 %) was selected as a representative case study. The model incorporates sub-models for traction motors, lithium-ion batteries (LFP and LTO), fuel storage, fuel cells, and ICE gensets across multiple fuel options (diesel, gasoline, methane, ethanol, methanol, HVO, FAME, and hydrogen). Battery lifetime is estimated using a combined
Ahrling, Christoffer, Tuner, Martin, Gainey, Brian, Torkiharchegani, Amir, Scharmach, Marcel, Hertel, Benedikt, ALAKÜLA, Mats
Energy-Aware Traffic Signal Optimization for Sustainable Urban Networks Using Valid Action Policies2026-01-0462To be published on 04/07/2026
Optimizing signal control across urban networks is crucial for reducing energy consumption and improving traffic efficiency. Advances in Vehicle-to-Everything (V2X) technologies have enabled deep reinforcement learning (DRL) to demonstrate significant promise in traffic signal control. However, many existing studies focus primarily on agent performance while neglecting practical operational constraints, which can lead to fairness issues in traffic control. Moreover, prior work often treats energy optimization as a by-product of efficiency. To address these gaps, this study proposes a multi-agent DRL framework to mitigate peak-hour congestion and reduce excessive emissions in urban networks. Building on this framework, a variant of Independent Proximal Policy Optimization (IPPO), termed Global State Independent PPO (GS-IPPO), is developed to enhance IPPO's coordination capability. To further improve practical applicability and fairness, this study incorporates constraints imposed by the
Qian, Wangrui, Jiang, Shuyan, Qi, Hao, Ou, Shiqi(Shawn)
Improving EV Energy Forecast Accuracy via ML Residual Correction: The Role of Wind Speed in Data-Driven Predictions2026-01-0150To be published on 04/07/2026
The increasing demand for electrified transportation is leading to accelerated development of highly efficient hybrid and battery electric vehicles. A major concern for customers adapting to battery electric vehicles (BEV) is range anxiety due to low charging speeds, charging infrastructure not matching expectations and unreliable range estimations shown to the customers by their vehicles. Estimating the range more accurately has been difficult due to the sensitivity of vehicle’s energy consumption to real-world environmental and driving conditions. This paper aims to find out the effect of true wind in the road load experienced by BEVs in the real-world driving scenarios and how using a highly accurate wind speed measurement improves the energy consumption estimation better. On-road tests were conducted on public roads and in controlled test-track environments to collect reliable wind speed measurements using a wind probe. Additional coastdown tests were also conducted to find more
RAGHUPATHY, Vishnu Prasaad, Kim, Shinhoon, Evans, Nic, Niimi, Keisuke, Mochihara, Takahiro
Adaptive Eco-Driving Feedback for Battery Electric Vehicles: A Reinforcement Learning Approach Using Context-Aware Policy Optimization2026-01-0165To be published on 04/07/2026
Energy efficiency and range optimization remain critical challenges to the widespread adoption of battery electric vehicles (BEVs). As a result, there is a growing demand for intelligent driver assistance systems that can extend the operating range and reduce range anxiety. This paper presents an adaptive eco-feedback and driver rating system based on proximal policy optimization (PPO) reinforcement learning, designed to support drivers with the target to reduce energy consumption and maximize driving range. The system processes real-time driving data, such as velocity, acceleration and powertrain status. Map data of high quality is used to anticipate traffic events, including but not limited to speed limits, curves, gradients, preceding vehicles and traffic lights. This contextual awareness allows the system to continuously assess driving behavior and provide personalized, context-aware visual feedback alongside a dynamic driving behavior rating. A PPO agent learns optimal feedback
Stocker, Christoph, Hirz, Mario, Martin, Michael, Kreis, Alexander, Stadler, Severin
Energy Efficiency Analysis of U-Haul Truck and Trailer Systems: Impact of Tongue Weight and Load Variations2026-01-0466To be published on 04/07/2026
The demand for improved energy efficiency in real-world vehicle operations continues to grow with technology enhancement. When transporting large cargos 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
Wang, Gang, Yang, Allan, Chen, Yan
Development of Markov-Chain Prediction Model for Work-Stealing Scheduler in Dynamic Scheduling for Multicore Embedded Software Systems2026-01-0066To be published on 04/07/2026
Designing embedded software that achieves effective utilization of the fast-growing multicore embedded hardware should help to reduce their execution time and power consumption and improve their reliability. AI and machine learning algorithms are making their way into such rapidly enhanced multicore embedded hardware. We have developed a Markov-chain prediction model and integrated it into a work-stealing scheduler within a dynamic scheduling runtime layer (DSRL). Dynamic scheduling with a work-stealing scheduler was adapted from MIT’s Cilk framework. Dynamic scheduling allows independent computations to be spawned so they can be scheduled dynamically and executed in parallel on available cores. Cilk used a random model in its work-stealing scheduler where an idle core randomly selects other cores to steal computations from them. However, our Markov-chain-based scheduler allows idle cores to make informed decisions about which cores are better to steal their computation to increase
Sadeh, Waseem, Ganesan, Subramaniam, Qu, Guangzhi, Rawashdeh, Osamah
Methodology to Quantify the Petroleum Equivalent Fuel Economy Impact of BEV Thermal System Energy Consumption2026-01-0132To be published on 04/07/2026
This paper will present research and 1D modeling results to support a proposed methodology to determine a fleet ambient weighted average Petroleum Equivalent Fuel Economy (PEFE) for the energy consumed by the thermal system of a BEV, including a proposal of how factor in the thermal system PEFE result into an OEM’s overall CAFE determination on an incentivized (not punitive) basis, within the existing regulatory framework. The topic is particularly important because EPA and NHTSA final rules issued in early 2024 eliminated A/C Efficiency Credits for BEVs that are currently in place for all classes of light duty vehicles. However, without some sort of regulatory incentivization, market forces will dictate thermal comfort system solutions that consume more energy – and with that more negative secondary effects such as higher greenhouse gas emissions and vehicle mass - than would otherwise be the case. Eliminating incentivization for BEVs is particularly concerning because the thermal
Taylor, Dwayne
The Impact of Extreme Ambient Temperatures on Electric Vehicle Performance in the Kingdom of Saudi Arabia2026-01-0133To be published on 04/07/2026
The rapid expansion of Saudi Arabia’s electric vehicle sector driven by manufacturers such as CEER Lucid Tesla and BYD necessitates evaluating vehicle performance under extreme thermal conditions. This study investigates the impact of high ambient temperatures on electric vehicle operation by analyzing a 60 kWh NMC battery pack under representative Riyadh summer conditions of up to 47 °C using MATLAB Simulink simulations. The results show that elevated temperatures accelerate state of charge (SOC) depletion and increase battery internal resistance. To maintain safe operating limits the BTMS must operate continuously consuming up to 20 percent of the available energy. Cooling demand increases nonlinearly with temperature exceeding 40 kW at 50 °C which leads to significant parasitic losses and a 15 to 20 percent reduction in driving range. At the system level this increased energy demand raises charging frequency and intensifies electricity load during peak summer periods thereby
Bedywi, Lama Mohammed A, AlJuhani, Haneen, Duan, Chen, AbdulNour, Bashar
Wind-tunnel investigation of snow/ice drag on a 30%-scale DrivAer model2026-01-0604To be published on 04/07/2026
Wind-tunnel tests were conducted using a 30%-scale DrivAer model, in estateback and notchback rear-geometry configurations, to investigate aerodynamic performance changes associated with snow and ice buildup on passenger vehicles. Around 20 snow/ice accumulation patterns were tested, at a Reynolds number of 2.8 million based on model wheelbase, for each of the notchback and estateback variants. 5 additional patterns were tested on the estateback with roof-rack support bars. Snow accumulation was modelled with foam, while ice accumulation was simulated with aluminum tape hand-formed to the desired shape. A simulated full-scale snow thickness of 58 mm on the hood, roof and trunk increased the wind-averaged drag coefficient by 16% for both model variants. With 90 mm of snow, the drag of the estateback variant increased by 19%. Drag changes increased with, but were not proportional to, snow thickness. Chamfered front and rear edges, representing windblown shapes, reduced the drag penalty
de Souza, Fenella, McAuliffe, Brian
Energy-Efficient Predictive Cruise Control in Heavy-Duty Electric Trucks with Optimization of the Penalty Factor as a Function of Payload2026-01-0042To be published on 04/07/2026
Heavy-duty Class 8 battery electric trucks not only offer a significant reduction in greenhouse gas (GHG) emissions compared to conventional diesel trucks but can also provide significant savings in fuel costs. To further enhance the energy and freight efficiency, Predictive Cruise Control (PCC) algorithms can be developed that generate optimal acceleration profiles for the vehicle by minimizing a cost function which combines both energy consumption and deviation from the desired velocity. A critical component of the cost function is the penalty factor, which governs the trade-off between energy use and travel time, which are two conflicting objectives in freight logistics. Selecting an appropriate penalty factor is essential, as freight deliveries are time-sensitive but minimizing energy consumption remains a priority. Moreover, variations in payload significantly affect vehicle dynamics and energy usage, making it critical to adapt the penalty factor to different payload conditions
Safder, Ahmad Hussain, Villani, Manfredi, Wang, Eric, Khuntia, Satvik, Nelson, James, Meijer, Maarten, Ahmed, Qadeer
Lateral stability and energy-saving integrated control of distributed drive vehicles with four wheel hub motors2026-01-0635To be published on 04/07/2026
Abstract: In order to improve the lateral stability and energy optimization of four-wheel hub motor distributed drive vehicles under complex road conditions, a layered control strategy for yaw stability is proposed. The upper controller is designed based on the sliding mode control principle to create a yaw moment controller, establishing both a two-degree-of-freedom model and a seven-degree-of-freedom model for the vehicle, tracking the desired yaw rate, desired center of mass side slip angle, actual yaw rate, and actual center of mass side slip angle to obtain the corresponding additional yaw moment. Moreover, the state of the vehicle is analyzed using the phase plane method of the center of mass side slip angle and yaw rate, and the total additional yaw moment for the vehicle is computed based on weighted calculations according to the state. Additionally, an unscented Kalman filter observer is established to enhance the tracking accuracy of the actual yaw rate and actual center of
shi, cheng'ao, liu, bingsen, Zou, Xiaojun, Wang, Tao, Zhang, Ming
Improving Winter Driving Range of EVs with a Novel Desiccant Device: Ceramic Humidity Regulator (CHR)2026-01-0117To be published on 04/07/2026
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
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 modes2026-01-0416To be published on 04/07/2026
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 also 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
Sok, Ratnak, Kusaka, Jin
Cradle-to-grave assessment of Austrian passenger car traffic based on actual vehicle movements and derivation of future forecasts for minimising greenhouse gas emissions2026-01-0457To be published on 04/07/2026
As part of the decarbonisation process for passenger car fleet in Austria, battery electric cars in particular have been subsidised in recent years, as these vehicles are considered to be largely emission-free during use and are expected to reduce emissions in future. However, in order to sustainably reduce the global greenhouse gas emissions of Austrian passenger car traffic, taking into account all types of propulsion systems, it is necessary to apply a cradle-to-gate approach, as is commonly done in comparable analyses in the literature, which evaluates the emissions of the entire vehicle life cycle. The most important phase in the life cycle assessment remains the well-to-wheel phase, which includes emissions from energy supply and vehicle use. Due to the large number of influencing factors, highly simplified models are usually used for this phase in the literature. As part of this work, a methodology was developed that, allows an in-depth analysis of entire vehicle fleets by
Lischka, Gregor, Tober, Werner
Novel Air System for 300 kW Heavy Duty Fuel Cell2026-01-0434To be published on 04/07/2026
Hydrogen fuel cell powered vehicles for heavy duty trucks is a potential path for reducing heavy duty vehicle emissions in the future. The air handling system delivers the proper amount of air (oxygen) to react with fuel (hydrogen) in the fuel cell to produce power. Air delivery requires significant power and is the largest parasitic loss for a 300 kW fuel cell. Today’s systems use an electric motor to power an air compressor that supplies oxygen to the fuel cell stack. In addition to parasitic power loss, hydrogen fuel cell systems often have reliability issues associated with the air handling system. Reliability is of significant concern for heavy duty applications (especially long-haul applications). This project aims to improve both the electrical power consumption and reliability of hydrogen fuel cell air handling systems to meet the needs of heavy duty on-highway vehicle applications. The air handling is provided by a twin vortices series (TVS) compressor in addition to adding a
Reich, Evan, Swartzlander, Matthew, Wine, Jonathan, McCarthy, James, Miller, Eric, Akhtar, Saad, Reddy, Sharan, Lawy, TJ
Research on Energy Consumption Optimization Control Strategy for Hybrid Electric Passenger Vehicles2026-01-0450To be published on 04/07/2026
The energy management strategy of hybrid electric vehicles (HEVs) is critical to achieving overall vehicle efficiency. Current global optimization methods involve significant computational complexity. Conventional or adaptive equivalent consumption minimization strategy (ECMS) also present challenges in robustness and calibration efficiency, as they typically depend on real-time driving condition recognition and online parameter adjustment. To address these issues, this study proposes an engineering optimization framework based on a system efficiency lookup table approach. The method uses offline iterative simulation to determine a globally representative equivalent factor. It pre-computes the optimal system efficiency, drive modes, and energy allocation across the entire operating range. These results are stored in a lookup table that the vehicle control unit (VCU) can access in real time. This approach eliminates the uncertainties associated with online optimization. It also provides
Lin, Haoqiang, Wang, Jinhang, Chen, Lihua, Li, Huan
Generative Agents for High-Fidelity Simulation of Community-Scale Mobility and Energy Behavior2026-01-0465To be published on 04/07/2026
The transition to sustainable mobility and energy systems is a complex socio-technical challenge, where the success of new technologies and policies hinges on their interaction with human behavior. Traditional models often fail to capture the nuanced, heterogeneous, and adaptive nature of occupant decision-making, particularly in terms of mobility choices and energy use, creating a critical uncertainty gap for system design and policy evaluation. This paper presents a new paradigm to address this gap: a scalable, high-fidelity simulation framework based on Generative Agents. The framework's core innovations are twofold: 1) It leverages Large Language Models (LLMs) to autonomously generate agents with intrinsic personality traits autonomously, enabling a realistic simulation of diverse, human-like responses to environmental, social, and transportation-related cues. 2) It introduces a hierarchical "Prototype-Individual" architecture that makes large-scale simulation of entire communities
Chen, Yongjian, Yang, Zhifeng, Ou, Shiqi(Shawn)
From Propane to Pi: Affordable AI-Driven Solutions for Sustainable Bus Heating.2026-01-0164To be published on 04/07/2026
The design of an affordably luxurious school bus means improving passenger comfort while greatly decreasing energy usage and emissions. The key consideration is to replace traditional propane heating with an electric heated-seat system and supported by green electricity/energy efficient smart power management. The system is designed from a 24V/48V battery system; to a Smart Energy Cube - a portable dock in power module requiring solar, battery and grid connection. Seat heating is only applied to seats that are occupied. The on-board image processing models (YOLOv5/YOLOv8, OpenCV, TensorFlow Lite) are run on affordable/portable microcomputers capable of detecting the number of occupied and available seats e.g. Raspberry Pi. There is one bottle neck when using the Raspberry Pi in the experiments to validate concept, when using low/medium power frame rates of ~ 1 fps for Raspberry Pi 3; to 7-8 fps with a Raspberry Pi 5. As a baseline, there is still potential for improvement and
Chikkala, Daney Bhargav, Zadeh, Mehrdad, TAN, Teik-Khoon, Ponnam, Jitin, BATTE, JAI RATHAN
Ambient and Initial Temperature Effects on Battery Electric Vehicle (BEV) Range and Energy Consumption Rate Modeled in FASTSim2026-01-0277To be published on 04/07/2026
Ambient and initial temperatures have a significant impact on the energy consumption rate of battery electric vehicles (BEVs) due to auxiliary loads and the dependence of battery efficiency on temperature. Quantifying these impacts can help BEV owners, communities, and researchers understand and prepare for the impact of temperature on BEV vehicles, especially as adoption increases. We used NREL's FASTSim 3 to investigate the impacts of a two-dimensional design-of-experiment that swept ambient and initial temperature in simulating a compact BEV hatchback over UDDS (city) and HWFET (highway) US EPA drive cycles. Compared to a 22 ° C ("cozy") initial / ambient temperature baseline in UDDS, a cold initial / cold ambient temperature (-7 ° C) resulted in a 221% increase in the energy consumption rate, a cozy initial / cold ambient temperature resulted in a 94% increase, a hot initial (45 ° C) / hot ambient (40 ° C) temperature resulted in a 40% increase, and a cozy initial / hot ambient
Baker, Chad, Steuteville, Robin, Holden, Jake, Gonder, Jeffrey, Carow, Kyle
An Integrated Methodology for Sustainable Mobility Infrastructure Planning: The Atlanta Aerotropolis Case Study2026-01-0097To be published on 04/07/2026
By the early 2020s, more than 4.5 billion people have been living in urban areas worldwide, compared to just 1 billion in 1960. Rising growth in urban populations present challenges to infrastructure and transportation systems. Higher traffic levels and reliance on conventional vehicles have contributed to heightened greenhouse gas (GHG) emissions, rising global temperatures, and irreversible environmental degradation. In response, emerging transportation solutions—including intelligent ridesharing, autonomous vehicles, zero-tailpipe-emission transport, and urban air mobility—offer opportunities for safer and more sustainable transportation ecosystems. However, their widespread adoption depends not only on technological performance and efficiency, but also on integration with current infrastructure, safety, resilience to unexpected disruptions, and economic viability. A dynamic, agent-based System-of-Systems (SoS) transportation model is developed to simulate vehicle traffic and human
Rana, Vishva, Balchanos, Michael, Mavris, Dimitri, Valenzuela Del Rio, Jose
Climate Heat Recovery Energy Study in Electric Vehicles2026-01-0129To be published on 04/07/2026
Climate control systems in Battery Electric Vehicles (BEVs), Plug-in Hybrid Electric Vehicles (PHEVs), and Extended Range Electric Vehicles (EREVs) rely on electrical energy to provide cabin heating. In winter conditions, the absence of waste heat from internal combustion engines necessitates increased energy consumption for thermal comfort, which directly impacts vehicle range. Conventional HVAC systems typically operate with a mixture of cold ambient air and recirculated cabin air. However, the proportion of recirculated air is limited due to windshield fogging risks, constraining energy-saving potential. To address this, MAHLE has developed modular heat recovery technologies that utilize the thermal energy of exhaust cabin air, that would normally leave the passenger compartment through vehicle body vents, to precondition incoming fresh air, thereby reducing the heating load. These solutions are engineered for scalable integration into existing HVAC architectures, allowing
Wolfe, Edward, Wochele, Kerstin, Reid, Bailey
Electric Vehicles & the Grid - Synergy or Strain?2026-01-0398To be published on 04/07/2026
As the demand for electrical power has surged over recent years due to the increasing popularity of data centers for Artificial Intelligence (AI) and Electric Vehicles (EVs), it is becoming evident that the aging electrical grid infrastructure is struggling to keep up. Some of the problems this aging infrastructure has resulted in include frequent blackouts due to weather related events, reduced efficiency resulting in higher maintenance costs and outdated communication systems causing poor monitoring and response times. Modernization of the grid in conjunction with integration of the transportation sector with the grid is essential to ensure the reliability and resiliency of the grid. Electric vehicles have dramatically increased in popularity, with most vehicle manufacturers offering at least one electric option in their lineups. Looking at recent developments in vehicle-to-grid (V2G) technology, a new possibility becomes evident; instead of straining the power grid, the electric
Dahlmann, Alexander Drake, Lele, Sneha
Quantifying the Impact of Towing on BEV Energy Use: Instrumented On Road Testing and Chassis Dynamometer Reproduction2026-01-0431To be published on 04/07/2026
Electrification is rapidly entering all vehicle classes, including light- and heavy-duty trucks designed for heavy towing capabilities. Still, the quantitative impact of towing on battery-electric vehicle (BEV) energy use and range remains under-characterized. We conducted controlled towing tests with a Ford F-150 Lightning using two trailers of different sizes and varying payloads to isolate aerodynamic and mass effects across the vehicle’s rated towing capacity. The vehicle was instrumented at the CAN bus level to capture motor power, torque, speed, and other related internal signals. On-road testing consisted of repeated back-and-forth passes on level, straight road segments at set speeds focusing on highway operation, where aerodynamic drag is stronger and real-world towing use cases occur. From these data, we extracted road load equations and dynamometer coefficients for each trailer combination, then reproduced equivalent conditions on a four-wheel drive chassis dynamometer
Timermans Ladero, Inigo
Establishing a Standard S-Factor: Improving Accuracy and Flexibility in SOC Corrections for HEV Testing2026-01-0422To be published on 04/07/2026
Hybrid-electric vehicle (HEV) fuel economy test procedures require that the net energy change (NEC) of the battery remain small enough to ensure accurate fuel usage results. SAE J1711-2010 required the NEC stay within 1% of fuel energy consumption, assuming that residual changes in state of charge (SOC) would have negligible impact. In practice, however, the asymmetry between fuel and electricity conversion efficiencies means that a 1% SOC imbalance can translate into a fuel consumption error approaching 3%. A standard S-Factor—a dimensionless ratio of marginal fuel change to marginal NEC change—was introduced in J1711-2023 to improve SOC corrections. The method improves accuracy by correcting all results for NEC changes and expands the NEC-to-fuel ratio (NECFR) window, enabling HEVs to use electric propulsion more aggressively and achieve higher fuel economy in testing and real-world usage. Using a standardized value (instead of requiring additional testing to determine the vehicle
Duoba, Michael
Intelligent Steering System to Improve Fuel Efficiency in Heavy Duty Vehicles2026-28-0103To be published on 02/12/2026
In commercial vehicles, conventional engine-driven hydraulic steering systems result in continuous energy consumption, contributing to parasitic losses and reduced overall powertrain efficiency. This study introduces an Electric Powered Hydraulic Steering (EPHS) system that decouples steering actuation from the engine and operates only on demand, thereby optimizing energy usage. Field trials conducted under loaded conditions demonstrated a 3–6% improvement in fuel economy, confirming the system’s effectiveness in real-world applications. A MATLAB-based simulation model was developed to replicate dynamic steering loads and vehicle operating conditions, with results closely aligning with field data, thereby validating the model’s predictive accuracy. The reduction in fuel consumption directly translates to lower CO₂ emissions, supporting regulatory compliance and sustainability goals, particularly in the context of tightening emission norms for commercial fleets. These findings position
T, Aravind Muthu Suthan, Mani, Kishore, Ayyappan, Rakshna, D, Senthil Kumar, S, Mathankumar
Development of Plug-in Hybrid Motorbike and Analysis towards Sustainable Urban Mobility2026-28-0111To be published on 02/12/2026
The transportation system is one major catalyst to urban ecological imbalance. In developing countries, two-wheelers are considered a major mode of urban personal transportation because of their compactness, easy maneuver in heavy traffic and good fuel efficiency. In India, middle and lower middle-class people prefer to choose two wheelers and these vehicles are dominantly fueled by gasoline. Although, the energy consumption by a two-wheeler is comparatively less than that of a four-wheeler, they use about 60% of the nation's petroleum for on-road vehicles and the impact on urban air quality and climatic change is significantly high. This high proportion of gasoline utilization and emission contribution by two wheelers in cities demand greater attention as a way to improve urban air quality and near-term energy sustainability. Electrification of two-wheelers through the application of a plug-in hybrid idea is a promising solution. A plug-in hybrid motorbike was developed by putting
Kannan, Prashanth, Shaik, Amjad, Talluri, Srinivasa Rao
AI Smart Timer-Based Power Control and Energy Monitoring System for Home Appliances2026-28-0060To be published on 02/12/2026
This paper presents an intelligent home automation system that combines timer-based power control, real-time energy monitoring, and cloud-based AI analytics for individual appliances. The system is built using a Wi-Fi-enabled embedded controller connected to sensing circuits that measure voltage, current, and power consumption. Electromechanical relays are used to control appliance power based on user-defined timers. Users can configure either a fixed runtime or a scheduled cutoff, enabling automated energy-saving without manual input. Alongside local control, the system collects energy consumption data and transmits it to a cloud platform for storage and analysis. A dedicated mobile or web app provides live monitoring, device status, and remote control access. A major innovation is the integration of a cloud-based AI engine that processes historical and real-time data to offer predictive insights. The AI module forecasts energy use, detects anomalies, classifies appliances by behavior
D, Anitha, D, Suchitra, Jain, Utsav, Maity, Souvik, Dinda, Atish
Development of Multiple level Auto Mode for Climate control in electric vehicles2026-28-0008To be published on 02/12/2026
Electric vehicles, or EVs, are significantly transforming the automotive industry as a cleaner and more environmentally friendly which finds an alternative solution for the vehicles propelled using internal combustion engines. As EV utilize the energy directly from the battery which causes customer or a user to be in panic state as charging infra is not developed well in most of the countries, thus improving vehicle efficiency and driving range requires optimizing energy consumption from battery of EVs. Apart from traction motor load for driving, major supplemental loads in electric car are climate control system, sometimes referred to as HVAC (Heating, Ventilation, and Air Conditioning). HVAC systems can consume a lot of battery power, especially during severe temperatures, which can drastically reduce the vehicle's range. Range anxiety continues to be an important barrier to the general adoption of EVs, the intelligent climate management systems which helps to reduce HVAC power usage
Mulamalla, Sarveshwar Reddy, SV, Master Eniyan, M, Nisshok, Anugu, Anil, E A, Muhammed, Guturu, Sravankumar
Benchmark of Conventional and By-Wire Brake System Layouts for Electric Vehicle Applications by Numerical Simulations2026-01-5008To be published on 02/03/2026
The recently increasing global concern about sustainability and greenhouse gas emission reduction has boosted the diffusion of electric vehicles. Research on this topic mainly focuses on either re-designing or adapting most conventional vehicle subsystems, especially the propulsion motor and the braking components. In this context, the present work aims to model, analyze, and compare three-braking system layouts design alternatives focusing on their contribution to vehicle performance and efficiency: a commercial vacuum-boosted hydraulic braking system, a commercial integrated electrohydraulic braking system, and a concept distributed electrohydraulic brake system. Braking systems performance are evaluated by simulating key maneuvers adopting a full model of a battery electric vehicle (BEV), which includes all relevant components like tires, and powertrain dynamics, which is validated against real-world data. Implementation and integration of the first two systems are discussed
Savi, Lorenzo, Garosio, Damiano, Floros, Dimosthenis, Vignati, Michele, Travagliati, Alessandro, Braghin, Francesco
Transforming Gear Manufacturing: A Deep Learning Approach to Quality Prediction2026-26-06461/16/2026
The automotive industry is rapidly transitioning towards Industry 4.0, transforming vehicle manufacturing. To achieve a lower carbon footprint, it is crucial to minimize raw material wastage and energy consumption. Reducing component wastage, lead time, and automating gear manufacturing are key areas. Gear micro-geometry inspection is vital, as variations affect service life and NVH (Noise, Vibration, Harshness). Despite standards for permissible errors, manual evaluation of gear microgeometry inspection is often needed. This subjective evaluation approach will have a possibility that a gear with undesired variations gets assembled into the product. These issues can be detected during NVH testing, leading to replacement of part and re-assembly thus increasing lead time. This generates a need for an automated system which could reduce the human intervention and perform gear inspection. The research aims to develop a deep learning-based model to eliminate the ambiguity of manual
Ramakrishnan, Gowtham Raj, Baheti, Palash, PR, Vaidyanathan, Durgude, Ranjit, Bathla, Archana, R, Greeshmita, V, Rangarajan
Computer Vision Based Driver Health and Wellness Monitoring System: A) Fatigue Detection, B) Emotional Wellbeing2026-26-06391/16/2026
Computer vision has evolved from a supportive driver-assistance tool into a core technology for intelligent, non-intrusive occupant health monitoring in modern vehicles. Leveraging deep learning, edge optimization, and adaptive image processing, this work presents a dual-module Driver Health and Wellness Monitoring System that simultaneously performs fatigue detection and emotional wellbeing assessment using existing in-cabin RGB cameras without requiring additional sensors or intrusive wearables. The fatigue module employs MediaPipe-based facial and skeletal landmark analysis to track Eye Aspect Ratio (EAR), Mouth Aspect Ratio (MAR), head posture, and gaze dynamics, detecting early drowsiness and postural deviations. Adaptive, driver-specific thresholds combined with CAN-bus data fusion minimize false positives, achieving over 92% detection accuracy even under variable lighting and demographics. The emotional wellbeing module analyzes micro-expressions and facial action units to
Iqbal, Shoaib, Imteyaz, Shahma
Advanced Thermal Management for High-Tonnage EVs: Increasing Coolant Flow and Reducing Energy Losses2026-26-01961/16/2026
With the increasing tonnage of electric heavy commercial vehicles, there is a growing demand for higher power and torque-rated traction motors. As motor ratings increase, efficient cooling of the EV powertrain system becomes critical to maintaining optimal performance. Higher heat loads from traction motors and inverters pose significant challenges, necessitating an innovative cooling strategy to enhance system efficiency, sustainability, and reliability. Battery-electric heavy commercial vehicles face substantial cooling challenges due to the high-pressure drop characteristics of conventional traction system cooling architectures. These limitations restrict coolant flow through key powertrain components and the radiator, reducing heat dissipation efficiency and constraining the operating ambient temperature range. Inefficient cooling also leads to increased energy consumption, impacting the overall sustainability of electric mobility solutions. This paper presents a novel approach of
Dixit, Sameer, Patil, Bhushan, Ghosh, Sandeep
Items per page:
1 – 50 of 3012