Software-defined vehicles offer customers a greater degree of customization of vehicle controls and driving experience. One such feature is user-adjustable tuning of vehicle ride and handling, where customers can vary ride height, damper stiffness, front-rear torque balance, and other aspects of vehicle dynamics. While promising a great customer experience, such a feature can expose the vehicle to a wider range of structural loads than those in the nominal design condition, particularly when such tuning is extended to cover spirited “sport” mode driving, off-road 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, moving

Demiri, Albion, Ramakrishnan, Sankaran, White, Dylan, Khapane, Prashant, Borton, Zackery

Operational Feasibility of Battery Swapping in U.S. Transit: A Case Study of King County Metro

2026-01-0402

4/7/2026

Battery swapping technology has emerged as a promising alternative to conventional charging for electric bus fleets, offering rapid turnaround times and improved vehicle availability. This paper utilizes existing bus routing information to perform an initial site evaluation for battery swapping stations. A Seattle-based public transit agency—King County Metro, a partner on this project—is used as a case study. Using General Transit Feed Specification (GTFS) data from King County Metro, a MATLAB model was built to reconstruct blocks and layovers, extracts dwell-time opportunities, and performs block-distance and block-time analyses to understand operational rhythms. based bus model was developed that maps route mileage, efficiency, and layover availability for battery swap decisions, using a look-ahead rule that defers battery exchanges whenever the next feasible layover can still be reached while respecting a minimum state-of-charge. The workflow estimates how many swaps each block

Vadlapatla, Taraka Rishi, Jankord, Gregory, D'Arpino, Matilde

Standardization of On-Vehicle Measurement of BEV Thermal System Energy Consumption and Range Impacts

2026-01-0132

4/7/2026

This paper presents research and digital twin modeling results to support work on a methodology to properly account for the energy consumed by the thermal system of a BEV, for use within both existing Petroleum-Equivalent Fuel Economy (PEFE) calculations, and the proposed addition of hot and cold weather range values to the consumer-facing Monroney label [1]. Properly accounting for thermal system impacts would incentivize minimizing energy consumption of these systems, since 1) BEV PEFE is a direct input to an OEMs overall CAFE performance, and 2) the values on the Monroney label has some impact on consumer vehicle choice. The impetus for this work was Final Rules issued by the EPA and NHTSA in early 2024 eliminating A/C Efficiency Credits for BEVs from the 2027 MY, thus eliminating regulatory incentives to minimize energy consumption of these systems. Higher energy consumption will produce a number of negative secondary effects, including higher real-world greenhouse gas emissions

Taylor, Dwayne

Techno-Economic Evaluation of Electrified Vehicle Options in Drayage Fleets

2026-01-0454

4/7/2026

The electrification of drayage fleets offers potential economic and operational benefits, but the financial viability of electrified vehicles remains sensitive to battery cost, energy price, and fleet usage patterns. 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 (PB) and Internal Rate of Return (IRR), which better reflect financial risks and investment return timelines. This study develops a unified techno-economic framework that jointly evaluates TCO, PB, and IRR to determine when electrified trucks become cost-effective alternatives to diesel trucks. Building on a previously developed cost modeling tool and using real-world telematics data from a Class 8 drayage fleet at the Port of Savannah, the analysis incorporates projected battery cost trajectories, electricity and diesel price trends, vehicle efficiency improvements, and multiple battery capacities

Sun, Ruixiao, Sujan, Vivek, Goulet, Nathan, Wang, Qixing

Agile Safety Case Assessments for Autonomous Vehicle Fleets

2026-01-0521

4/7/2026

Rapidly upcoming deployment of autonomous vehicles (AVs), including robotaxis and trucks, has intensified the need for rigorous safety assessment of complex AI-driven systems. While considerable effort has been invested in constructing safety cases for AVs, systematic approaches for evaluating these safety cases remain underdeveloped. This paper presents a three-stage methodology for assessing AV safety cases. A process for assessing argumentation is presented that involves traceability to pre-reviewed and peer-reviewed safety cases such as the Open Autonomy Safety Case (OASC). Next, we present a structured process for evaluating the quality of evidence supporting these arguments. We applied this methodology to evaluate safety cases from multiple AV developers, enabling iterative refinement throughout the development lifecycle. Our agile approach supports efficient assessments by establishing clear traceability to industry standards and enabling early identification of potential gaps

Wagner, Michael

A Real-Time Spatio-Temporal Configuration Framework for Shared Autonomous Electric Vehicle (SAEV) Fleets Integrating Deep Learning and Multi-Agent Simulation

2026-01-0035

4/7/2026

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

From Propane to Pi: Affordable AI-Driven Solutions for Sustainable Bus Heating

2026-01-0164

4/7/2026

This paper proposes an intelligent, artificial intelligence (AI) enabled seat heating system for school buses that saves energy by only activating heating elements when a passenger is identified. A custom-trained YOLOv8 deep learning model identifies passengers in real time and opens/closes real-time control of the individual electric seat heaters via a Raspberry Pi 5. The detector achieves around 10 frames-per-second (FPS) of inference on the Raspberry Pi 5 and 80–90 FPS on a laptop with over 92% detection confidence across various illumination conditions. Energy modeling shows the anticipated demand for a 10-kW propane-based heater is approximately 75% lower by implementing a 2.52 kW electric seat-heating system. In a typical operation schedule of 540 hours a year, this results in 4,000–5,000 kWh of annual savings, $465–$579 of annual cost savings and mitigates 0.9–1.3 t CO₂ per bus, annually. When implemented at the fleet level, the energy and cost saving will be in proportion. This

Chikkala, Daney Bhargav, Zadeh, Mehrdad, Tan, Teik-Khoon, Ponnam, Jitin, Batte, Jai Rathan

Cradle-To-Grave Assessment of Austrian Passenger Car Traffic based on Actual Vehicle Movements and Derivation of Future Forecasts for Minimising Greenhouse Gas Emissions

2026-01-0457

4/7/2026

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 fuel systems, it is necessary to apply a cradle-to-grave 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 linking

Lischka, Gregor, Tober, Werner

Optimal Vehicle Sampling for Fleet-Wide Emissions Monitoring using Submodular Maximization

2026-01-0283

4/7/2026

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 XTX, 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 changes. We compare

Zhang, Jiadi, Li, Xiao, Kolmanovsky, Ilya, Tsutsumi, Munecika, Nakada, Hayato

Energy-Efficient Predictive Cruise Control in Heavy-Duty Electric Trucks with Optimization of the Penalty Factor as a Function of Payload

2026-01-0042

4/7/2026

Heavy-duty Class 8 battery electric trucks not only offer the potential to significantly reduce greenhouse gas (GHG) emissions compared to conventional diesel trucks but can also provide significant savings in fuel costs. To further enhance 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 tradeoff 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

Safder, Ahmad Hussain, Villani, Manfredi, Wang, Eric, Khuntia, Satvik, Nelson, James, Meijer, Maarten, Ahmed, Qadeer

Data-Driven Objective Road-Driving Classification for Durable Design of Vehicles

2026-01-0181

4/7/2026

Design for durability 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 are rarely accounted for in designing vehicle level durability tests. This makes it difficult to establish targets for durability testing that accurately match the wide variations in real-world roads and driving. This paper presents a data-driven approach to objectively classify road surface and driving characteristics using metrics derived from existing road response metrics like Vibration Dose Value (VDV) and statistical estimates of vehicle speed and acceleration. Data collected at the proving grounds on gravel roads, smooth roads, city-like roads

Shaurya, Shubham, Ramakrishnan, Sankaran, Demiri, Albion, Khapane, Prashant

Integrated DBSCAN-Based Segmentation of Tractor Activity into Productive and Non-Productive States from GPS Data

2026-01-0169

4/7/2026

Accurate identification of Productive and Non-Productive States or tractor duty cycles—comprising working, idle, and transport states—is critical for performance analysis, fuel optimization, and emissions modeling in agriculture machinery and fleet monitoring. This study explores the application of integrated unsupervised machine learning (ML) 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 several labeled engine and vehicle parameters, making it scalable and adaptable across diverse operational contexts. Clustering algorithm DBSCAN (Density-Based Spatial Clustering of Applications with Noise) in integration with hybrid rule-based and a road feature is 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

Maharana, Devi prasad, Gangsar, Purushottam, Dharmadhikari, Nitin, Pandey, Anand Kumar

BEV Incremental CO ₂ Emissions in a Ranked Order Electric Grid

2026-01-0424

4/7/2026

Battery Electric Vehicles (BEV) have been sold as ‘Zero Emissions Vehicles’ (ZEV) by governments to reduce transportation CO2. While they are not ZEV because they run on grid electricity, they could be ‘effectively ZEV’ if the incremental CO2 is ‘very small’. At the national level, this is estimated using following metrics: (1) Internal Combustion Engine Vehicle (ICEV) fuel consumption, from the total US gasoline consumption divided by the total fleet miles driven, 25 mpg or 350 g CO2/mi, (2) Strong Hybrid Electric Vehicles (HEV) about one third less, 240 g CO2/mi. (3) 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. (4) Electricity marginal CO2: in a ranked order grid, zero-CO2 sources are prioritized and supplemented by fossil sources. IEA hourly data show that the US 48 contiguous states are self-contained, with zero-CO2 sources providing a third of total demand. The response to hourly

Phlips, Patrick

Proactive Fault Detection in EV Batteries Via Hybrid Method: Cell-to-Cell Variation Pattern and Physics-Based Model Insights

2026-01-0391

4/7/2026

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 twelve EVs—six exhibiting stable performance and the other six 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 in battery pack during the mid-to-high state-of-charge (SoC) range (approx. 20–30% to full charge). Vehicles in healthy condition consistently displayed minimal voltage deviation between BMS-measured cell voltages and physics-based model predictions, whereas those with latent faults showed markedly higher variance, particularly between the highest battery and model

Jawle, Bharat Sanjay, Selvakumar, Ashwin, Puttoji Rao, Nagaraj Kumar

Energy and Operational Efficiency of Shared Autonomous Fleet Powered with Battery Electric and Internal Combustion Engine Technologies

2026-01-0464

4/7/2026

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 testing aims to verify the

Tang, Kang, Abdulsattar, Harith, Yang, Hao, Wang, Jinghui

Predictive Maintenance of EV Drive Unit Oil Pumps through Machine Learning on Fleet Telemetry Data

2026-01-0161

4/7/2026

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. In modern EV architectures, real-time health monitoring of these pumps typically relies on indirect signals than dedicated sensing hardware, a design choice optimized for cost, weight, and system complexity. This makes early fault detection a non-trivial challenge. 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. Specifically, we develop 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

Li, Jingman, Yao, Mengqi, Rahimi, Sahil, Lin, Joanne

Optimized Design and Economic Evaluation of a Renewable Hydrogen Refueling Station for Public Transport on Elba Island

14-15-01-0004

2/13/2026

The aim of this study is to develop a methodology to significantly reduce emissions in bus fleet renewal scenarios by investigating both technical and economic aspects. This work presents a case study based on Elba Island, Italy, which investigates optimal solutions for replacing existing Diesel buses through a total cost of ownership analysis. The investigation is carried out for four different potential scenarios: renewing the fleet with Diesel buses, renewing the fleet with electric buses, adopting fuel cell buses, and implementing a hybrid solution. The latter represents a synergistic solution that integrates fuel cell buses with the development of a hydrogen refueling station driven by a proton exchange membrane electrolyzer, unlocking the techno-economic potential of self-producing green hydrogen for bus refueling. The novelty of this study is its integrated methodology that combines a total cost of ownership analysis with a tailored design of a green hydrogen production network

Bove, Giovanni, Sorrentino, Marco, Baldinelli, Arianna, Desideri, Umberto

Intelligent Steering System to Improve Fuel Efficiency in Heavy Duty Vehicles

2026-28-0103

2/12/2026

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

Rewriting the engineer's playbook: What OEMs must do to spin the AI flywheel

26AUTP02_04

2/1/2026

The automotive industry's future hinges on a new AI-native engineering workflow that accelerates iteration, strengthens system thinking, and preserves human judgment. Automotive development cycles are compressing at a pace the industry has never seen. The shift to all-electric fleets of software-defined vehicles is moving faster than traditional processes can absorb. In parallel, regulatory pressure and customer expectations keep rising, demanding greater performance, higher safety, better energy efficiency, and sharper competitiveness. In this environment, OEMs R&D competitiveness depends on three factors: How quickly teams can explore and iterate on design choices while delivering differentiated value, product performance, and cost efficiency. How early system-level interactions can be detected, before they turn into delivery friction or costly late-stage failures. How effectively a company can encode and scale its internal engineering know-how into lean development processes.

Allard, Théophile

A proactive approach to hydraulic health in Class 8 trucks

26TOFHP02_07

2/1/2026

For any fleet or logistics manager, the specter of a downed Class 8 truck is a constant concern. The costs aren't just in parts and labor; they're in lost productivity, missed deadlines and potential damage to your reputation. While many factors can sideline a heavy-duty vehicle, one of the most persistent and costly culprits is hydraulic system failure. These failures often trace back to a single, preventable issue: contamination.

Lapierre, Luc

Integrated architectures reshape the mobile-hydraulic ecosystem

26TOFHP02_06

2/1/2026

Off-highway equipment operates in an environment defined by extremes - extreme loads, extreme duty cycles, extreme temperatures and extreme expectations. OEMs and fleet operators face mounting pressure to deliver more power, more uptime and more precision from platforms that are becoming increasingly compact, intelligent and complex. Whether the task is hauling, lifting, dumping, clearing or moving materials, the equipment must deliver consistent, reliable performance without compromise. This pressure is reshaping the mobile-hydraulic ecosystem. The industry is steadily shifting away from piecemeal systems and toward integrated, intelligent power architectures that maximize efficiency across the entire vehicle. Leaders in this space, Eaton among them, demonstrate how a system-level approach to PTOs, hydraulic pumps and control valves is enabling a new generation of off-highway innovation.

Bogdan, Corneliu

To Study and Compare Communication Protocol for Fast Charging in Electric Two-Wheeler with Respect to CCS 2.0: An Indian Perspective

2026-26-0198

1/16/2026

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

Crash Pulse Characterization for the Passenger Vehicle Fleet in India

2026-26-0029

1/16/2026

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

Sensorless Tire Health Monitoring System based on Machine Learning for Passenger Vehicles

2026-26-0670

1/16/2026

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

A Dynamic Cybersecurity Benchmarking Framework for Automotive Cyber-Physical Systems

2026-26-0612

1/16/2026

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 India

2026-26-0610

1/16/2026

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

Computer Vision Based Driver Health and Wellness Monitoring System: A) Fatigue Detection, B) Emotional Wellbeing

2026-26-0639

1/16/2026

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

Optimizing Powertrain Performance a Customer Usage-Centric Approach

2026-26-0067

1/16/2026

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

AI-Powered Intelligent Cognitive Lens for Driver Monitoring for Safe Transportation and Increased Fleet Efficiency Abstract

2026-26-0634

1/16/2026

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

System-Level Simulation to Design a Hydrogen Powered Refuse Collection Vehicle Based on Real-World Fleet Operational Data

2026-26-0399

1/16/2026

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

Leveraging AI/ML Algorithms for Analyzing Fleet Vehicles In-Use Performance Ratio Monitoring Data

2026-26-0637

1/16/2026

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

Bio-Inspired Decentralized Mapping for Autonomous Vehicles

2026-26-0635

1/16/2026

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

Hybrid Topology Selection & Optimization of a Compact SUV to Meet Future CO ₂ Norms – an Indian Case Study

2026-26-0077

1/16/2026

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

A Comprehensive Study: The Potential of Wireless Charging in Future E Mobility

2026-26-0195

1/16/2026

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

Powertrain Optimization of Indian City Electric Bus Using Active Learning Simulation Methodology

2026-26-0657

1/16/2026

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

Early Prediction of CNG Filling Time Using Artificial Intelligence for Design Optimization

2026-26-0662

1/16/2026

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

Drive Cycle Based Energy Audit & Range Estimation for 4X2 Electric Truck

2026-26-0163

1/16/2026

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

Testing Framework and Safety Evaluation for Vehicle-to-Vehicle (V2V) Charging Systems in Electric Mobility

2026-26-0176

1/16/2026

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.

Pure Electric Bus of Operational Consulting and Analysis Transit: A Case Study of Wuhu City

2025-99-0252

12/23/2025

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 Commitment

2025-36-0218

12/18/2025

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 Vehicles

2025-36-0104

12/18/2025

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 Adoption

1354712/12/2025

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 Intersections

2025-99-0453

12/10/2025

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 future

25TOFHP12_05

12/1/2025

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 Impact

2025-28-0236

11/6/2025

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 Maintenance

2025-28-0307

11/6/2025

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

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