Browse Topic: Fleets

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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
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
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
Agile Safety Case Assessments for Autonomous Vehicle Fleets2026-01-0521To be published on 04/07/2026
The large-scale deployment of autonomous vehicles (AVs) demands not only robust safety cases but also systematic approaches for their assessment. A safety case is a structured argument, supported by evidence, that a system is acceptably safe within its intended domain. While many AV developers are now adopting safety cases as a prerequisite for launch readiness, effective assessment methods have not kept pace with the field’s rapid progress. This paper presents a three-stage methodology for safety case assessment—framework, solution, and implementation assessment—developed through nearly fifty evaluations across commercial and defense AV programs. Framework assessment benefits from leveraging safety case frameworks, which accelerate development and prevent argument defects. Solution assessment focuses on defining clear evidence criteria, while implementation assessment evaluates the sufficiency of complete evidence packages. Together, these stages support iterative improvement and
Wagner, Michael
Design and validation of modern hydrogen and natural gas containers for on board vehicle storage2026-01-0188To be published on 04/07/2026
Ensuring the operational safety of gaseous fuel containers for hydrogen and natural gas vehicles requires robust and validated protective devices capable of responding reliably under diverse operating and accident scenarios. This paper presents the design considerations, test methodology, and validation outcomes for safety subsystems integrated into modern compressed gas storage vessels, with emphasis on pressure relief devices (PRDs), thermal protection systems, and valve assemblies. Drawing on experimental insights from previous studies of hydrogen and natural gas fueling systems and fire-resistance assessments, the work defines safety-critical conditions such as rapid temperature rise during bonfire exposure, localized flame impingement, over-pressurization from fast filling, and mixed-gas operation. A comprehensive validation protocol was established, combining standardized regulatory tests with additional stress profiles replicating service-realistic environments. These included
Flekiewicz, Marek, Flekiewicz, Wojciech
Optimal Vehicle Sampling for Fleet-Wide Emissions Monitoring using Submodular Maximization2026-01-0283To be published on 04/07/2026
This article addresses the problem of optimal vehicle sampling for fleet-wide in-use emissions monitoring, a necessity driven by the absence of direct emissions sensors in modern production vehicles and the variable impact of in-use changes and operational factors (mileage, time-in-service, workload) on emissions performance across a fleet. Recognizing that comprehensive fleet testing is impractical due to significant downtime and cost, we propose a novel approach to identify a small, yet optimally informative subset of vehicles for sampling. The proposed approach leverages submodular function maximization, a technique rooted in optimal experimental design, specifically D-optimal design, to maximize the determinant of the information matrix (e.g., of $X^TX$, where $X$ is the regressor/design matrix in the case of a linear in parameters model). This approach ensures that the collected data yields maximum information for refining and building accurate models for emissions characteristic
ZHANG, JIADI, Li, Xiao, Kolmanovsky, Ilya, Tsutsumi, Munecika, Nakada, Hayato
Operational Feasibility of Battery Swapping in U.S. Transit: A Case Study of King County Metro2026-01-0402To be published on 04/07/2026
Battery swapping technology has emerged as a promising alternative to conventional plug-in charging for electric bus fleets, offering rapid turnaround times and improved vehicle availability. This paper investigates whether battery swapping can be integrated into U.S. public transit bus operations without disrupting existing timetables. A Seattle based public transit agency- King County Metro is taken as a case study in this paper. Using General Transit Feed Specification data from King County Metro, a MATLAB model was built that reconstructs blocks and layovers, extracts dwell-time opportunities, and performs block distance and block time analyses to understand operational rhythms. A mathematical energy model was developed that maps route mileage, efficiency, and layover availability to swap decisions, using a look-ahead rule that defers exchanges whenever the next feasible layover can still be reached while respecting a minimum state-of-charge. The workflow estimates how many swaps
Vadlapatla, Taraka Rishi, Jankord, Gregory, D'Arpino, Matilde
Techno-Economic Evaluation of Electified Vehicle Options in Drayage Fleets2026-01-0454To be published on 04/07/2026
The transition of drayage fleets from diesel to battery electric vehicles (BEVs) poses both opportunities and uncertainties, especially regarding long-term economic viability. While total cost of ownership (TCO) is a useful benchmark, fleet operators and investors are equally concerned with investment performance metrics such as payback period and Internal Rate of Return (IRR), which better reflect financial risks and investment return timelines. This study proposes a comprehensive framework to evaluate the economic feasibility of transitioning from diesel to BEVs, jointly analyzing TCO parity conditions, payback periods, and IRRs under different technology, cost, and operational scenarios. Building on a previously developed cost modeling tool, the analysis incorporates evolving battery prices, energy costs, vehicle performance characteristics and other assumptions from 2025 to 2050. Real-world duty cycle data from a Class 8 drayage fleet at the Port of Savannah is used to capture
Sun, Ruixiao, Sujan, Vivek, Goulet, Nathan, Wang, Qixing
Data-Driven Objective Road-Driving Classification For Durable Design Of Vehicles2026-01-0181To be published on 04/07/2026
Durability development in the automotive industry depends on a clear understanding of how road surfaces and driving characteristics affect structural road loads and fatigue. Traditionally, road surface classification has been subjective (e.g., city, highway, rural), and done through driving instrumented vehicles over a small selection of roads. The variations in driving characteristics that are often consequent to the road surface quality is rarely accounted for in designing vehicle level durability tests. This makes it difficult to establish targets for the durability testing that accurately match the wide variations in real-world roads and driving across the market of vehicles. This paper presents a data-driven approach to objectively classify road surface and driving characteristics using metrics derived from existing vehicle and road response metrics like Vibration Dose Value (VDV), International Roughness Index (IRI) and statistical estimates of vehicle speed and acceleration
Shaurya, Shubham, Ramakrishnan, Sankaran, Demiri, Albion, Khapane, Prashant
A Real-Time Repositioning Strategy for SAEV Fleets Using a Simulation-Informed Deep Learning Model2026-01-0035To be published on 04/07/2026
Shared Autonomous Electric Vehicles (SAEVs) can enhance urban mobility and efficiency. However, their operational performance is often hindered by the spatio-temporal imbalance between vehicle supply and passenger demand, leading to long wait times. This paper develops a novel repositioning framework where a lightweight CNN, informed by computationally intensive multi-agent simulations, enables real-time strategy deployment. The results show that: (1) An optimized repositioning policy, calibrated via multi-agent simulation, effectively cuts the mean passenger waiting time from 12.0 to 3.0 minutes (a 75% reduction). (2) A lightweight CNN surrogate model enables real-time deployment, reducing the policy computation time from ~4 hours to ~5 minutes (>98% faster). (3) The deep learning surrogate achieves this speed with a negligible performance trade-off, increasing the waiting time by only 0.156 minutes (4.9%) compared to the full optimization.
Shang, Kai, Wang, Ning
Impact of User-Adjustable Vehicle-Dynamics Tuning on Vehicle Durability2026-01-0194To be published on 04/07/2026
Software-defined vehicles offer customers a greater degree of customization on vehicle operation. One such feature is a user-adjustable tuning of vehicle ride and handling, where customers can vary the ride height, the active damper stiffness (affecting ride comfort), front-rear torque balance (affecting ride handling), and other vehicle dynamics features. While promising a great customer experience, such a feature can expose the vehicle to a wider range of structural loads than before, particularly when such tuning is extended to cover spirited “sport” mode driving, offroad driving, etc. In this paper we present a novel methodology combining Road Load Data Acquisition (RLDA) data and real-world telemetry data to estimate the impact of user-adjustable vehicle-dynamics tuning on structural durability.  In doing so, the method combines the physics of damage accumulation (from RLDA data) with user behavior (from telemetry data) to present an accurate assessment of the impact on durability
Demiri, Albion, White, Dylan, Ramakrishnan, Sankaran, Borton, Zackery, Khapane, Prashant
Research on Intelligent Vehicle Fleet Longitudinal Control Based on Fuzzy Model Predictive Control2026-01-0040To be published on 04/07/2026
Addressing the challenge of balancing tracking efficiency and stability in longitudinal cooperative control of intelligent vehicle platoons, this paper proposes a hierarchical control strategy integrating fuzzy logic with Model Predictive Control (MPC). The upper-layer controller, based on the platoon's longitudinal dynamics model, formulates an optimization objective function that considers both tracking performance and ride comfort. A fuzzy logic system is introduced to adaptively adjust the weighting coefficients within the MPC objective function in real-time based on the prevailing tracking state, outputting an optimal desired acceleration. The lower-layer controller, utilizing an inverse longitudinal dynamics model incorporating the engine's universal characteristic map and a brake system model, accurately translates the desired acceleration into throttle opening or brake pressure via a Proportional Integral Derivative (PID) control algorithm. To validate the control strategy, a
Li, Ye
Clustering-Based Segmentation of Off-Road Vehicle Activity into Productive and Non-Productive States from GPS Data2026-01-0169To be published on 04/07/2026
Accurate identification of Productive and Non-Productive States or Off-Road vehicle duty cycles—comprising working, idle, and transport states—is critical for performance analysis, fuel optimization, and emissions modeling in agriculture machinery and fleet operations. This study explores the application of unsupervised machine learning techniques to classify duty cycles using GPS-derived parameters such as speed, location variance, and temporal patterns. Unlike supervised approaches, the proposed method does not rely on labeled datasets, making it scalable and adaptable across diverse operational contexts. Clustering algorithms, including K-means and DBSCAN, are employed to segment GPS data into distinct behavioral states. Feature engineering focuses on extracting motion signatures and spatial-temporal features that correlate with operational modes. Validation against manually annotated datasets demonstrates high accuracy in distinguishing idle, working, and transport phases. The
Maharana, Devi prasad, Gangsar, Purushottam, Dharmadhikari, Nitin, Pandey, Anand Kumar
Proactive Faulty Detection in EV Batteries Via Hybrid method: Cell-to-Cell Variation Pattern And Physics-Based Model insights.2026-01-0391To be published on 04/07/2026
This study presents a distinct methodology for the early detection of faulty cells in electric vehicle (EV) battery systems, leveraging temporal voltage deviation patterns under real-world charging scenarios alongside outputs from a physics-based model. A comparative longitudinal analysis was conducted on a fleet of ten EVs—five exhibiting stable performance and five demonstrating early-stage anomalies characterized by intermittent transitions from drive to neutral mode. These behavioral cues were investigated as precursors to deeper battery degradation. The analysis focused on cell-level voltage dispersion during the mid-to-high state-of-charge (SoC) range (approx. 20–30% to full charge). Vehicles in healthy condition consistently displayed minimal voltage differentials among cells, whereas those with latent faults showed markedly higher variance, particularly between the highest and model-expected voltages. Notably, this voltage divergence was often accompanied by a modest yet
Jawle, Bharat Sanjay, Selvakumar, Ashwin, Puttoji Rao, Nagaraj Kumar
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
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
Predictive Maintenance of EV Drive Unit Oil Pumps via Machine Learning on Fleet Telemetry2026-01-0161To be published on 04/07/2026
The reliability of Drive Unit (DU) oil pumps is critical to the performance and safety of electric vehicles, as these pumps provide essential lubrication and thermal management. However, direct physical sensors for real-time pump health monitoring are absent in current architectures, making early fault detection particularly challenging. To address this limitation, we present a novel, data-driven anomaly detection framework that leverages large-scale customer fleet telemetry and advanced machine learning to identify incipient pump degradation that traditional diagnostic methods often fail to capture. The core of the approach is an XGBoost regression model trained on time-series features—including commanded pump speed, oil temperature, and historical pump current—to predict expected current behavior under nominal conditions. Deviations are quantified using the Mean Absolute Percentage Error (MAPE) between predicted and actual currents, providing a continuous and interpretable measure of
Li, Jingman, Yao, Mengqi, Rahimi, Sahil, Lin, Joanne
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
Bio-Inspired Decentralized Mapping for Autonomous Vehicles2026-26-06351/16/2026
The BioMap system represents a groundbreaking approach to collaborative mapping for autonomous vehicles, drawing inspiration from ant colony behavior and swarm intelligence. It implements a fully decentralized protocol where vehicles use virtual pheromone trails to mark areas of uncertainty, change, or importance, enabling efficient map consensus without centralized coordination. Key innovations include novel pheromone-based compression algorithms and bio-inspired consensus mechanisms that allow real-time adaptation to dynamic environments. In a simulated urban scenario (Town10HD), three vehicles achieved balanced load distribution (±1.8% variance) and comprehensive coverage of a 253.2m × 217.9m × 22.4m area. The final fused map contained 311 chunks with 72,785 particles and required only 10.4 MB of storage. Approximately 49.2% of map particles exceeded the pheromone significance threshold, indicating active importance marking, while no high-uncertainty regions remained. These results
Bhargav, Anirudh S, Subbarao, Chitrashree
Crash Pulse Characterization for the Passenger Vehicle Fleet in India2026-26-00291/16/2026
A crash pulse is the signature of the deceleration experienced by a vehicle and its occupants during a crash. The deceleration-time plot or crash pulse provides key insights into occupant kinematics, occupant restraints, occupant loading and efficiency of the structure in crash energy dissipation. Analysing crash pulse characteristics like shape, slope, maximum deceleration, and duration helps in understanding the impact of the crash on occupant safety and vehicle crashworthiness. This paper represents the crash pulse characterization study done for the vehicles tested at ARAI as per the ODB64 test protocol. Firstly, the classification and characterization of the crash pulses is done on the basis of the unladen masses of the vehicles. The same are further analysed for suitability of mathematical waveform models such as Equivalent Square Wave (ESW), Equivalent Triangular Wave (ETW), Equivalent Sine Wave (ESW), Equivalent Haversine Wave (EHSW) as well as EDTW (Equivalent dual trapezia
Mishra, Satish, Kulkarni, Dileep, Borse, Tanmay, Mahindrakar, Rahula Ashok, Mahajan, Rahul, Jaju, Divyan
Early Prediction of CNG Filling Time Using Artificial Intelligence for Design Optimization2026-26-06621/16/2026
Over the past few decades, Compressed Natural Gas (CNG) has gained popularity as an alternative fuel due to its lower operating cost compared to gasoline and diesel, for both passenger and commercial vehicles. In addition, it is considered more environmentally friendly and safer than traditional fossil fuels. Natural gas's density (0.7–0.9 kg/m3) is substantially less than that of gasoline (715–780 kg/m3) and diesel (849–959 kg/m3) at standard temperature and pressure. Consequently, CNG needs more storage space. To compensate for its low natural density, CNG is compressed and stored at high pressures (usually 200-250 bar) in on-board cylinders. This results in an effective fuel density of 180 kg/m3 at 200 bar and 215 kg/m3 at 250 bar. This compression allows more fuel to be stored, extending the vehicle's operating range per fill and minimising the need for refuelling. Natural Gas Vehicles (NGVs), particularly those in the commercial sector like buses and lorries, need numerous CNG
Choudhary, Aditya Kant, Petale, Mahendra, Dutta, Surabhi, Bagul, Mithilesh
Quantifying the Energy Consumption of HVAC Operation and Its Impact on the Range of Electric Buses Across Diverse Climatic Conditions2026-26-02041/16/2026
Electric buses (e-buses) are essential to sustainable public transport, but their real-world efficiency and range are heavily affected by auxiliary systems, particularly the Heating, Ventilation, and Air Conditioning (HVAC) system. This study investigates how ambient temperature variations and HVAC loads influence energy consumption, range, and efficiency in e-buses operating under diverse climatic conditions. The methodology combines field data collection from urban e-buses across seasons—including extreme summer and winter—with controlled laboratory testing. Field measurements included ambient temperature, HVAC demand, vehicle speed, state of charge (SOC) variation, and energy consumption. These inputs were used to develop real-world duty cycles, replicating actual thermal loads, passenger profiles, idling periods, and driving patterns. In the laboratory, these cycles were simulated using a chassis dynamometer and environmental chamber, with HVAC systems tested at controlled ambient
Vishe, Prashant, Dalela, Saurabh, Saraswat, Shubham, Joshi, Madhusudan
Optimisation-Driven Framework for Heterogeneous Battery-Pack Mixing in Fleet Applications2026-26-02091/16/2026
Fleet owners often encounter significant logistical and financial problems when dealing with battery packs of different ages and conditions. The standard industry practice is to replace old batteries with identical new ones. This process is inefficient because it costs a lot, creates too much inventory, and eliminates battery packs that are still useful too soon. The problem worsens when manufacturers stop making older battery models, which can force a vehicle to retire early. This paper puts forward a framework for mixing different types of battery packs to deliver the performance needed for a vehicle’s mission. We show how this works in three everyday service situations: 1) Repair, when a single damaged pack needs replacing; 2) Life Extension, where aged packs are combined with newer ones to meet mission range; and 3) Performance Restoration, which uses next-gen packs when the original parts are obsolete. The study shows that a vehicle can complete its required missions by
Nair, Sandeep R., Ravichandran, Balu Prashanth, Hallberg, Linus
A Comprehensive Study: The Potential of Wireless Charging in Future E Mobility2026-26-01951/16/2026
Electric mobility is no longer a distant vision, it is a global imperative in the journey of fight against the climate change and the urban pollution. Yet, despite of explosive growth in the electric vehicle adoptions, a major bottleneck remains which is efficient and convenient charging. The current reliance on physical plug in charging station creates inconvenient, time consuming experience and also faces significant technical and economic challenges those threaten to stall the smooth clean transportation revolution. Without innovation in how we recharge our vehicle the promise of electric mobility appears under threat which is undermined by less efficient, less compatible, and infrastructure hurdles. Wireless charging technology stand out as the game changing breakthrough poised to tackle these all critical problems head on. By enabling the effortless, cable-free charging system across the wide spectrum of electric vehicles, from the personal cars to the public transport fleets and
Jain, Gaurav, Premlal, P, Pathak, Rahul, Gore, Pandurang
Simulation of EV Range Estimation for Different Payload of Vehicle2026-26-03891/16/2026
The electrification of transportation is revolutionizing the automotive and logistics sectors, with electric vehicles (EVs) assuming an increasingly pivotal role in both passenger mobility and commercial activities. As the adoption of EVs rises, the necessity for precise range estimation becomes essential, especially under diverse operational circumstances, including vehicle and battery characteristics, driving conditions, environmental influences, vehicle configurations, and user-specific behaviors. Among the varying factors, a key fluctuating one is user behavior—most notably, increased payload, which significantly affects EV range. A key business challenge lies in the significant variability of EV range due to changes in vehicle load, which can affect performance, operational efficiency, and cost-effectiveness—especially for fleet-based services. This research aims to tackle the technical deficiency in forecasting electric vehicle (EV) range under various payload conditions
Khatal, Swaraj, Gupta, Anjali, Krishna, Thallapaka
Testing Framework and Safety Evaluation for Vehicle-to-Vehicle (V2V) Charging Systems in Electric Mobility2026-26-01761/16/2026
This paper presents a comprehensive testing framework and safety evaluation for Vehicle-to-Vehicle (V2V) charging systems, incorporating advanced theoretical modeling and experimental validation of a modern, integrated 3-in-1 combo unit (PDU, DCDC, OBC). The proliferation of electric vehicles has necessitated the development of resilient and flexible charging solutions, with V2V technology emerging as a critical decentralized infrastructure component. This study establishes a rigorous mathematical framework for power flow analysis, develops novel safety protocols based on IEC 61508 and ISO 26262 functional safety standards, and presents comprehensive experimental validation across 47 test scenarios. The framework encompasses five primary test categories: functional performance validation, power conversion efficiency optimization, electromagnetic compatibility (EMC) assessment, thermal management evaluation, and comprehensive fault-injection testing including Byzantine fault scenarios
Uthaman, Sreekumar, Mulay, Abhijit B, Nikam, Sandip B.
A Dynamic Cybersecurity Benchmarking Framework for Automotive Cyber-Physical Systems2026-26-06121/16/2026
The modern vehicle is no longer a mechanical appliance—it has transformed into a software-defined cyber-physical system, integrating OTA updates, cloud-connected diagnostics, V2X services, and telematics-driven personalization. While this evolution promises unprecedented value in consumer experience and fleet operations, it also surfaces a dramatically expanded and evolving attack perimeter, especially across safety-critical ECUs and communication buses. Cyber vulnerabilities have shifted from isolated IT threats to real-time, embedded exploits. Controller area network (CAN), the backbone of vehicle bus systems, remains intrinsically insecure due to its lack of authentication and encryption, making it highly susceptible to message injection and denial-of-service by low-cost tools. Similarly, OEM implementations of BLE-based passive entry systems have proven vulnerable to replay and spoofing attacks with minimal hardware. In the Indian context, the transition to connected mobility is
Shah, Ravindra, Awasthi, Vibhu Vaibhav, Karle, Ujjwala
Assessment of Cybersecurity Risks in State-Operated Connected Electric Passenger Buses in India2026-26-06101/16/2026
State Transport Units (STUs) are increasingly using electric buses (EVs) as a result of India's quick shift to sustainable mobility. Although there are many operational and environmental benefits to this development, like lower fuel prices, fewer greenhouse gas emissions, and quieter urban transportation, there are also serious cybersecurity dangers. The attack surface for potential cyber threats is expanded by the integration of connected technologies, such as cloud-based fleet management, real-time monitoring, and vehicle telematics. Although these systems make fleet operations smarter and more efficient, they are intrinsically susceptible to remote manipulation, data breaches, and unwanted access. This study looks on cybersecurity flaws unique to connected passenger electric vehicles (EVs) that run on India's public transit system. Electric vehicle supply equipment (EVSE), telematics control units (TCUs), over-the-air (OTA) update systems, and in-car networks (such as the Controller
Mokhare, Devendra Ashok
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
Gijare, Sumant, Karthick, K., Juttu, Simhachalam, Thipse, Sukrut S., A, Jothikumar, J, Frederick Royston, SR, Subasree, G, Harini, M, Senthil Kumar
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
Powertrain Optimization of Indian City Electric Bus Using Active Learning Simulation Methodology2026-26-06571/16/2026
Public transport electrification is going to play a massive role in India’s COP26 pledge to achieve net zero emissions by 2070. India plans to electrify 800,000 buses in a push towards 30% EV penetration by 2030. Further encouraged by government incentives under National Electric Bus Program (NEBP), e-Bus market is expected to grow at a CAGR of ~86% annually over the next 5 years. With most OEMs going for fleet electrification for reducing CO2 emissions and to cater to growing demand in Indian cities for cleaner public transport, improving powertrain efficiency and performance of state-of-the-art e-Buses is a natural progression of e-mobility sector development in India. The first step in designing powertrain for an electric city bus is to determine the motor(s) size and transmission specifications (number of gears, gear ratios etc.). Complications arise due to a wider and non-linear operation range of eBus. This study focuses on powertrain optimization for a medium duty electric city
Sandhu, Rouble, Chen, Bicheng, Emran, Ashraf, Xia, Feihong, Lin, Xiao, Berry, Sushil
Hybrid Topology Selection & Optimization of a Compact SUV to Meet Future CO 2 Norms – an Indian Case Study2026-26-00771/16/2026
Rising environmental concerns and stringent emissions norms are pushing automakers to adopt more sustainable technologies. There is no single perfect solution for any market and there are solutions ranging from biofuels, green hydrogen to electric vehicles. For Indian market, especially in the passenger car segment, hybrid vehicles are favoured when it comes to manufacturers as well as with consumer because of multiple reasons such as reliability, performance, fuel efficiency and lower long-term cost of ownership. For automakers planning to upgrade their fleets in the context of upcoming CAFE III (91.7 g CO2 / km) & CAFE IV (70 g CO2/km) norms, hybridization emerges as the next natural step for passenger cars. Lately, various state governments have also promoted hybrid vehicle sales by offering certain targeted tax breaks which were previously reserved for EVs exclusively. Current study focuses on various parallel hybrid topologies for an Indian compact SUV, which is the highest
Warkhede, Pawan, Keizer, Ruben, Sandhu, Rouble, Emran, Ashraf
Leveraging AI/ML Algorithms for Analyzing Fleet Vehicles In-Use Performance Ratio Monitoring Data2026-26-06371/16/2026
In recent times, the governments are pushing for stringent emission regulations. These regulations call for reduction of pollutants as well as monitoring of engine components which are critical for emission control. Monitoring these emission critical engine components are to be done in real world driving conditions. The In-Use Performance Ratio Monitoring (IUPRm) framework quantifies how often onboard diagnostic systems check these components within defined boundaries for each vehicle. IUPRm is divided into several monitoring groups like catalyst monitoring, oxygen sensor monitoring, exhaust gas recirculation (EGR) monitoring, gasoline particulate filter monitoring and others. These groups are differentiated based on fuel type, engine technologies and exhaust treatment system configurations. For an Automotive manufacturer analyzing these parameters across large vehicle fleets is a complex and data intensive task. To address this, a user-friendly application was developed in-house
Ghadge, Ganesh Narayan, Jadhav, Marisha, Hosur, Viswanatha
Sensorless Tire Health Monitoring System based on Machine Learning for Passenger Vehicles2026-26-06701/16/2026
Tire wear progression is a nonlinear and multi-factor degradation phenomenon that directly influences vehicle safety, handling stability, braking performance, rolling resistance, and fleet operational cost. Global accident investigations indicate that accelerated or undetected tread depletion contributes to nearly 30% of highway tire blowouts, highlighting the limitations of conventional wear indicators such as physical tread wear bars, mileage-based service intervals, and periodic manual inspections. These manual and threshold-based approaches fail to capture dynamic driving loads, compound ageing, pressure imbalance effects, or platform-specific wear behaviours, thereby preventing timely intervention in real-world conditions. This work presents an Indirect Tire Wear Health Monitoring System that employs an advanced Machine Learning + Transfer learning architecture to infer tread wear level and Remaining Useful Life (RUL) without relying on any tire-mounted sensors. The system ingests
Imteyaz, Shahma, Iqbal, Shoaib
Optimizing Powertrain Performance a Customer Usage-Centric Approach2026-26-00671/16/2026
The US trucking industry heavily relies on the diesel powertrain, and the transition towards zero-emission vehicles, such as battery electric vehicles (BEV) and fuel cell electric vehicles (FCEV), is happening at a slow pace. This makes it difficult for truck manufacturers to meet the Phase 3 Greenhouse Gas standards, which mandate substantial emissions reductions across commercial vehicle classes beginning of 2027. This challenging situation compels manufacturers to further optimize the powertrain to meet stringent emissions requirements, which might not account for customer application specifics may not translate to a better total cost of ownership (TCO) for the customer. This study uses a simulation-based approach to connect customer applications and regulatory categories across various sectors. The goal is to develop a methodology that helps identify the overlap between optimizing for customer applications vs optimizing to meet regulations. To use a data-driven approach, a real
Mohan, Vignesh, Darzi, Mahdi
System-Level Simulation to Design a Hydrogen Powered Refuse Collection Vehicle Based on Real-World Fleet Operational Data2026-26-03991/16/2026
This study addresses one of the challenges in the energy transition of heavy-duty vehicles by converting a diesel Refuse Collection Vehicle (RCV) into a hydrogen-powered prototype. The research is part of the VeH2Dem project funded by NextGenerationEU and focuses on dimensioning the complete hydrogen propulsion system for a RCV, including the energy storage capacity, without compromising payload or operational functionality. The development of the propulsion system is based on a comprehensive analysis of operational data extracted from fleet management systems, complemented by detailed instrumental monitoring of various collection routes. This methodology ensured that the prototype inherits performance equivalent to the original internal combustion engine vehicle across all evaluated scenarios. The vehicle performance objectives were established following a comparative analysis with solutions currently available in the RCV market, incorporating statistical analyses to ensure continuous
Cano, Pablo, Barrio, Roberto, Roche, Marina, de-Lima, Daniela, Batista, Sara, Bertolí, Xavier
AI-Powered Intelligent Cognitive Lens for Driver Monitoring for Safe Transportation and Increased Fleet Efficiency Abstract2026-26-06341/16/2026
The rapid evolution of intelligent transportation systems has made drivers’ attentiveness and adherence to safety protocols more critical than ever. Traditional monitoring solutions often lack the adaptability to detect subtle behavioral changes in real time. This paper presents an advanced AI-powered Driver Monitoring System designed to continuously assess driver behavior, fatigue, distractions, and emotional state across various driving conditions. By providing real-time alerts and insights to vehicle owners, fleet operators, and safety personnel, the system significantly enhances road safety. The system integrates lightweight AI/ML algorithms, image processing techniques, perception models, and rule-based engines to deliver a comprehensive monitoring solution for multiple transportation modes, including automotive, rail, aerospace, and off-highway vehicles. Optimized for edge devices, the models ensure real-time processing with minimal computational overhead. Alerts are communicated
Chikhale, Shraddha, Sing, Sandip, Hivarkar, Umesh, Mardhekar, Amogh
To Study and Compare Communication Protocol for Fast Charging in Electric Two-Wheeler with Respect to CCS 2.0: An Indian Perspective2026-26-01981/16/2026
This comprehensive research presents an in-depth analysis of communication protocols essential for implementing fast charging systems in India's rapidly expanding electric two-wheeler and three-wheeler market. As India witnesses unprecedented growth in electric mobility, with two-wheelers representing over 95% of current EV sales, the establishment of standardized, secure, and efficient charging protocols becomes paramount for widespread adoption. This study examines the current landscape of AC charging methodologies, evaluates the technical and economic feasibility of DC fast charging implementation, and provides detailed comparative analysis of existing international standards including IS 17017-25, IS 17017-31, ChaoJi, and CCS 2.0. The research concludes with strategic recommendations for developing cyber-secure, cost-effective charging infrastructure specifically tailored to meet India's unique market requirements and operational constraints.
Uthaman, Sreekumar, Mulay, Abhijit B
Intelligent Sequential Charging Control via Fleet-Managed Cloud System2026-26-02071/16/2026
Electric Vehicles (EV) are increasingly becoming more and more popular in the markets, especially in the commercial vehicle segments. Amidst this, the need to find new elegant methods to perform charging of EV battery becomes extremely crucial. In areas with high demand and limited power capacity, performing charging for multiple vehicles necessitates efficient usage of charging infrastructure, which can’t be guaranteed by the traditional charging methods. Sequential charging is a new state of art technique for managing the charging of multiple EV’s simultaneously connected to a single charging station. Rather than dividing the available power equally among all connected vehicles or charging them one at a time, this technique dynamically allocates power based on various factors such as charging priority, vehicle needs and available infrastructure capacity. Currently, sequential charging can only be implemented by a particular set of chargers that are interconnected via backend and
De, Abir, Bhattacharya, Ullash, Parihar, Aakash
Pure Electric Bus of Operational Consulting and Analysis Transit: A Case Study of Wuhu City2025-99-025212/23/2025
As China’s socio-economic progress accelerates, residents’ mobility preferences are growing more varied. Owing to their eco-friendliness, high capacity, fixed routes and low prices, pure-electric buses have become a key component of urban transit. Yet day-to-day service is hindered by low fleet availability, limited daily kilometres and poor service quality, all of which erode operation efficiency. Taking Wuhu’s public transport network as a case study, this paper builds a performance-assessment framework for electric bus routes. Using stop-level topology, vehicle specifications and spatiotemporal passenger-flow data from eight representative routes, the study applies the Analytic Hierarchy Process (AHP). A three-tier hierarchy—goal, criteria and alternatives—is constructed; index weights and pairwise comparison matrices are then computed to rank overall route effectiveness. The findings accurately pinpoint operational bottlenecks and furnish quantitative guidance for adaptive network
Hu, Tingting, Liang, Zijun, Li, Xiaoyan, Zhang, Xinyi, Wang, Mengru, Hu, Yufeng, Jiang, Kang
Sustainable Aviation Fuels – A Review of the Challenges Associated to the Aviation’s Net Zero Commitment2025-36-021812/18/2025
The aviation sector currently accounts for 2-3% of global Greenhouse Gas (GHG) emissions, while the projected increased air travel demand (average 3.4% per year), might surge the aviation fuel use. This increase in jet fuel demand, associated with the current decarbonization pathway of other sectors might increase the aviation’s absolute emissions, as well as its relative global GHG share. This scenario has driven the aviation stakeholders into a decarbonization strategy, focused on an immediate and gradual GHG reduction effort associated with a net-zero commitment by 2050. Meanwhile, the aviation sector is known as one that set most difficulties to use alternative fuels and/or powertrains, such as battery electric or sustainable hydrogen fueled propulsion systems, already used on some road and rail applications, but still restricted to the aviation, due to the inherent weight and volume tight requirements. In this context, the sustainable aviation fuels (SAF) are set as the most
Barbosa, Fábio Coelho
Gear Ratio Variation Concept Influence on Energy Efficiency Optimization of Light Vehicles2025-36-010412/18/2025
The growing concern regarding global warming pushes the contribution of all emitting sources to mitigate greenhouse gases. The significant light passenger vehicle fleet deserves continued attention, both in the implementation of more efficient new technologies and in the optimization of conventional technologies, which are still widely used. The vehicle’s energy efficiency is directly influenced by the coupling of the internal combustion engine to the transmission system. Engines have a restricted operation region of maximum efficiency that must be adequately explored by the transmission system in the different conditions of vehicle use. Thus, this paper analyzes and quantifies the sensitivity of the vehicle’s energy efficiency of two distinct engine technologies, naturally aspirated and turbocharged, coupled to an automatic transmission system with six discrete or continuously variable gears. Experimental data on the overall efficiency of the engines and the transmission concepts
Rovai, Fernando Fusco, Menezes Lourenço, Maria Augusta de, Rohrig, Marcelo
SAE TOMORROW TODAY - Powering the Next Wave of EV Adoption1354712/12/2025
There's no doubt that convenience is the new currency for EV owners. But given the tug-of-war between utilities, data centers, and EV charging infrastructure providers, what does it all mean for consumers and commercial fleets? Listen in as we sit down with Jason Greenblatt, Senior Practice Consultant at Energetics, to explore the latest data, trends, and opportunities driving EV adoption. Discover how home and workplace charging options can transform consumer habits, what's really behind public charging prices, and how smart software and load management can revolutionize EV fleet charging while saving money and stabilizing the power grid. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen--and give us a review on your
Patterson, Lori
Research on Control Strategy for Heterogeneous Traffic at Signalized Intersections2025-99-045312/10/2025
In order to reduce conflicts between vehicles at intersections and improve safety, an optimization model of traffic sequence allocation is studied and established for the heterogeneous traffic scenario of connected autonomous vehicles and manual vehicles. With the minimum safe traffic time as constraint, the right of way is allocated to vehicles according to the microscopic traffic characteristics of heterogeneous traffic flow fleet movement and the phase of signal lights, and the optimal trajectory planning control of each vehicle and evaluation indicators are established. A jointly simulation running environment is built using VISSIM and MATLAB. The simulation results indicate that at the micro level, collaborative control slows down the waiting time for manually driven vehicles and improves the utilization of green light travel time. At the macro level, as the penetration rate of connected autonomous vehicles increases, the sum of squares of vehicle acceleration gradually decreases
Yuan, Shoutong, Li, Zhiqiang, Liu, Tianyu, Yu, Zhengyang
Stoneridge Innovation Truck envisions a digital-vision future25TOFHP12_0512/1/2025
Stoneridge displayed its vision for the future of commercial vehicle technology on the SAE COMVEC 2025 exhibit floor. The Innovation Truck showcases the Tier 1 supplier's next-generation vision and driver-assistance technologies designed to enhance driver safety and fleet optimization. Mario Gafencu, product design and evaluation specialist at Stoneridge, gave Truck & Off-Highway Engineering a tech truck walkaround at the event. The first technology Gafencu detailed was the second-generation MirrorEye camera monitor system that's designed to replace the glass mirrors on the sides of a truck.
Gehm, Ryan
Optimizing Heavy-Duty Mining Operations with LNG Powertrains: Boosting Efficiency and Minimizing Environmental Impact2025-28-023611/6/2025
Heavy-duty mining is a highly demanding sector within the trucking industry. Mining companies are allocated coal mine sites, and fleet operators are responsible for efficiently extracting ore within the given timeframe. To achieve this, companies deploy dumper trucks that operate in three shifts daily to transport payloads out of the site. Consequently, uptime is crucial, necessitating trucks with exceptionally robust powertrains. The profitability of mining operations hinges on the efficient utilization of these dumper trucks. Fuel consumption in these mines constitutes a significant portion of total expenses. Utilizing LNG as a fuel can help reduce operational fuel costs, thereby enhancing customer profitability. Additionally, employing LNG offers the potential to lower the CO2 footprint of mining operations. This paper outlines the creation of a data-driven duty cycle for mining vehicles and the simulation methodology used to accurately size LNG powertrain components, with a focus
John, Ann Veena, Pendharkar, Koustubh
Connected Aftermarket Services for Off-Highway Vehicles: Enhancing Fleet Management Through Real-Time Data and Predictive Maintenance2025-28-030711/6/2025
Off-highway vehicles (OHVs) are essential in heavy-duty industries like mining, agriculture, and construction, as equipment availability and efficiency directly affect productivity. In these harsh settings, conventional maintenance plans relying on set intervals frequently result in either early component replacements or unexpected breakdowns. This document presents a Connected Aftermarket Services Platform (CASP) that utilizes real-time data analysis, predictive maintenance techniques, and unified e-commerce functionalities to evolve OHV fleet management into a proactive and smart operation. The suggested system integrates IoT-enabled telematics, cloud-based oversight, and AI-powered diagnostics to gather and assess machine health indicators such as engine load, vibration, oil pressure, and usage trends. Models for predictive maintenance utilize both historical and real-time data to produce advance notifications for component failures and maintenance requirements. Fleet managers get
Vashisht, Shruti
Analysis of Maintenance Strategies of Electrical Systems of Electric Bus Charging Station2025-28-040710/30/2025
Charging management has a profound impact on the reliability and safety of electric bus (EB) services. However, the actual charging operation of EB fleets is a critical challenge due to uncertain energy consumption, limited charging resources and other factors. At present there are no operational and maintenance guidelines present for operation of EB charging stations since the running and operation of these facilities are at the initial stages of development. There is a need to develop these strategies that provides smooth operation of these newly developed facilities. In the present work maintenance strategies of electrical systems of Electric bus charging station were designed. The complete maintenance is divided into quarterly and annual maintenance based on the requirements and nature of work for smooth operation. Quarterly Maintenance is devised on detecting early signs of wear through visual inspections of key components, including transformers and ventilation systems while
Soam, Kumaresh, Vashist, Devendra
The Morning Commute Problem with Carpool and Dynamic Toll Scheme2025-99-002210/17/2025
This study extends the bottleneck model to analyze dynamic user equilibrium (UE) in carpooling during the morning peak commute. It is assumed that the carpooling platform offers both traditional human-driven vehicles and novel shared autonomous vehicles. First, we analyze the traffic distribution on a two-lane road. We find that traffic distribution is influenced by the additional cost of carpooling behavior. A corresponding functional relationship is established and visualized. Second, we derive the critical fare threshold for carpooling. Carpooling occurs only when the fare is below this threshold. Third, we obtain the user equilibrium (UE) solution under a specified case, including flow distribution, equilibrium cost, and total number of vehicle. Furthermore, a system-optimal dynamic tolling scheme is proposed to minimize total system cost while maintaining commuter UE. By equating the system marginal cost to the equilibrium cost, we derive the analytical expression for the lane
Zheng, XiaoLong, Zhong, RenXin
Solving Fleet Electrification Bottlenecks with Battery-Integrated Charging SystemsTBMG-5387510/1/2025
Charging infrastructure has become the primary bottleneck to fleet electrification. Despite growing demand for electric trucks and increasing pressure to decarbonize logistics operations, many fleet operators face long delays for grid upgrades, transformer installations, and permitting, at times waiting more than a year to power vehicles that have already arrived.
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