Hydrogen fuel cell powered vehicles for heavy duty trucking are a promising path for reducing future vehicle emissions due to their reduced mass for storage and faster refueling compared to battery electric trucks. These benefits come at the cost of increased system complexity stemming from the fact that fuel cells generate electricity through a chemical reaction which must be tightly controlled. The air handling system delivers the proper amount of air (oxygen) to react with fuel (hydrogen) in the fuel cell to produce power. Air delivery requires significant power and is the largest parasitic loss for a 300 kW fuel cell. Today’s systems use an electric motor driving an air compressor to supply pressurized air to the fuel cell stack. By operating at elevated pressure levels, fuel cells can achieve higher power density, which is important for vehicle powertrains. In addition to parasitic power loss, hydrogen fuel cell systems often have reliability issues associated with the air

Reich, Evan, Swartzlander, Matthew, Wine, Jonathan, McCarthy, Jr., James, Miller, Eric, Akhtar, Saad, Reddy, Sharan, Lawy, TJ

Quantifying Highway Flow Improvements via CACC-Based Truck Platooning: A Statistical Modeling Approach

2026-01-0463

4/7/2026

The advancement of Cooperative Adaptive Cruise Control (CACC) technology enables vehicle platooning on public roads, offering significant potential to enhance urban mobility, driving safety, and energy efficiency. Among various applications, truck platooning has become a promising strategy to increase highway flow rates by reducing vehicle headways, improving coordination, and optimizing space utilization. This paper presents a quantitative assessment of a CACC-based truck platooning system, focusing on its effectiveness in enhancing highway mobility under varying traffic conditions. A statistical regression model is developed and calibrated using simulations of real-world highway networks to identify key influencing factors and evaluate the resulting improvements in traffic flow. The analysis considers five primary variables: desired platoon speed, platoon size, space headway, percentage of platooning trucks, and non-platoon traffic flow. The study systematically examines the impact

Karbasi, Amir Hossein, Wang, Jinghui, Yang, Hao

Electrified Propulsion System Balancing for Light Duty Full Size Truck and Sport Utility Vehicles: A 2026 North American Market Perspective

2026-01-0412

4/7/2026

Achieving the stringent EPA CAFE 2032 standards for light-duty full-size trucks and sport-utility vehicles (SUVs) in North American poses significant challenges. While Battery Electric Vehicles (BEVs) offer a clear path to zero tailpipe emissions, their widespread adoption in this segment faces hurdles including range anxiety, payload/towing capabilities, and traditional truck/SUV use cases. This paper investigates a balanced approach, focusing on optimizing propulsion system design with appropriate hardware content, can effectively meet future fuel economy and emissions standards. This investigation examines advanced BEVs and hybrid electric vehicle architectures, including full hybrids (HEVs), and plug-in hybrids (PHEVs) tailored for full-size trucks and SUVs. Considerations include the optimal sizing of internal combustion engines, electric motors, and battery packs to deliver robust performance while maximizing energy efficiency. This paper analyzes the integration of technologies

Babcock, Dillon, Robinette, Darrell

Accelerating Electrification through Modular Propulsion: GM’S Strategy for BEV and BET Platforms

2026-01-0410

4/7/2026

General Motors (GM) continues to advance its electrification strategy through the development of scalable Battery Electric Vehicle (BEV) and Battery Electric Truck (BET) platforms. This paper highlights GM’s latest BEV and BET products that leverage shared Drive Unit (DU), Rechargeable Energy Storage System (RESS), and integrated power electronic (IPE) components across multiple vehicle programs. By adopting a modular and commonized propulsion architecture, GM achieves significant benefits in manufacturing efficiency, cost optimization, speed to market, and product flexibility. The shared DU, RESS, and IPE components are engineered to meet diverse performance requirements while maintaining high standards of energy efficiency, thermal management, and durability. This approach enables rapid deployment of electrified solutions across various segments, from passenger vehicles to full-size trucks, without compromising on capability or customer experience. The paper outlines the technical

Liu, Jinming, Sevel, Kris, Anwar, Mohammad, Oury, Andrew, Welchko, Brian, Gagas, Brent

Energy Efficiency Analysis of U-Haul Truck and Trailer Systems: Impact of Tongue Weight and Load Variations

2026-01-0466

4/7/2026

The demand for improved energy efficiency in real-world vehicle operations continues to grow with technology enhancement. When transporting large cargo loads with passenger pickup trucks and rental trailers, the interaction between vehicle payload, towing configuration, and fuel consumption becomes a key factor in overall system efficiency. Understanding how towing configurations and trailer loading influence fuel consumption and vehicle performance is critical for both consumer guidance and vehicle system design. This study investigates the energy efficiency of U-Haul truck and trailer systems, with a particular focus on the influence of trailer tongue weight. U-Haul truck and trailer simulation models were developed using AVL Vehicle Simulation Model (VSM) software, with an F-350 engine brake-specific fuel consumption (BSFC) map integrated to represent realistic engine performance. Two configurations with equal payload were evaluated: (1) a U-Haul truck alone, and (2) a U-Haul truck

Wang, Gang, Kathadi, Mohammad, Yang, William, Chen, Yan

A Comparison of Road Grade Preview Signals from Lidar and Maps

2026-01-0026

4/7/2026

Road grade can impact the energy efficiency, safety, and comfort associated with automated vehicle control systems. Currently, control systems that attempt to compensate for road grade are designed with one of two assumptions. Either the grade is only known once the vehicle is driving over the road segment through proprioception, or complete knowledge of the oncoming road grade is known from a pre-made map. Both assumptions limit the performance of a control system, as not having a preview signal prevents proactive grade compensation, whereas relying only on map data potentially subjects the control system to missing or outdated information. These limits can be avoided by measuring the oncoming grade in real-time using on-board lidar sensors. In this work, we use point returns accumulated during travel to estimate the grade at each waypoint along a path. The estimated grade is defined as the difference in height between the front and rear wheelbase at a given waypoint. Kalman filtering

Schexnaydre, Logan, Poovalappil, Aman, Robinette, Darrell, Bos, Jeremy

Characterizing Regenerative Braking of the Tesla Model 3 Using Chassis Dynamometer Testing

2026-01-0453

4/7/2026

Regenerative braking has a strong influence on the energy efficiency and drivability of battery-electric vehicles. This study establishes an empirical baseline analysis under controlled conditions of the regenerative braking behavior of the 2020 Tesla Model 3 to support the interpretation of on-road performance and serve as a reference for subsequent testing and analysis. The tests were performed on a four-wheel-drive chassis dynamometer at Argonne National Laboratory, combining Multi Cycle Testing (MCT) to simulate real world driving patterns (city, highway) with coast-down tests to isolate periods where the motor is operating in regen mode and compare the behavior across different parameters. Vehicle data was collected from the vehicle using taps in the Controller Area Network (CAN) bus as well as a high-resolution power analyzer. The vehicle displayed the highest efficiency during simulated city driving conditions (3.62 miles/kWh followed by highway (3.40 miles/kWh) and aggressive

Pierce, Benjamin Branch, Di Russo, Miriam, Das, Debashis, Zhan, Lu, Stutenberg, Kevin

AI-Powered Requirements Engineering & Design Optimization of Electric Powertrains

2026-01-0500

4/7/2026

The development of electric vehicle powertrains is driven by diverse and often conflicting requirements. In early development phases, these requirements are often vague, incomplete, continuously refined and subject to change as development progresses. Moreover, powertrain designs must be competitive regarding multiple key performance indicators (KPIs) such as performance, cost, energy efficiency, and package integration. This challenges engineers to concurrently develop the powertrain design alongside the requirements on which the design is based on. Managing this combination of uncertain requirements and multi-KPI design optimization represents a complex challenge in automotive engineering. The present work introduces a requirements engineering approach based on OPED (Optimization of Electric Drives). OPED digitalizes the transition from requirements to technical solutions by integrating parametric system models with an AI-based evolutionary optimization algorithm. This enables

Hofstetter, Martin, Lechleitner, Dominik

Adaptive Eco-Driving Feedback for Battery Electric Vehicles: A Reinforcement Learning Approach Using Context-Aware Policy Optimization

2026-01-0165

4/7/2026

Energy efficiency and range optimization remain critical challenges to the widespread adoption of battery electric vehicles (BEVs). As a result, there is a growing demand for intelligent driver assistance systems that can extend the operating range and reduce range anxiety. This paper presents an adaptive eco-feedback and driver rating system based on proximal policy optimization (PPO) reinforcement learning, designed to support drivers with the target to reduce energy consumption and maximize driving range. The system processes real-time driving data, such as velocity, acceleration and powertrain status. Map data of high quality is used to anticipate traffic events, including but not limited to speed limits, curves, gradients, preceding vehicles and traffic lights. This contextual awareness allows the system to continuously assess driving behavior and provide personalized, context-aware visual feedback alongside a dynamic driving behavior rating. A PPO agent learns optimal feedback

Stocker, Christoph, Hirz, Mario, Martin, Michael, Kreis, Alexander, Stadler, Severin

A Study on Fatigue Life Prediction Method for Point-Based Joints Considering Multiple Fracture Modes – FDS, Blind Rivet, and Blind Nut Focus

2026-01-0180

4/7/2026

The application of multiple materials in vehicle bodies is accelerating as the adoption of lightweight aluminum alloys and composite materials advances rapidly. These materials play a crucial role in reducing overall vehicle weight, enhancing fuel efficiency, and complying with increasingly strict environmental regulations. As the automotive industry continues to evolve toward electrification and sustainability, the integration of lightweight and high-performance materials has become a key design strategy. However, the use of multiple materials creates new challenges in manufacturing, particularly for joining technologies. Since different materials have varying mechanical properties, thermal behavior, and surface characteristics, the selection of appropriate joining methods is essential for ensuring structural integrity and durability. Depending on material types, thicknesses, production processes, and cost constraints, various joining techniques—such as mechanical fastening, welding

Takuno, Sougo, Isono, Toshiyuki, Urakawa, Kazushi, Goto, Suguru, Kawamura, Hiroaki, Niisato, Eita, Ishigami, Yuta

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

Application of Topology Optimization to Production-Ready Passenger Seat Components Design

2026-01-0498

4/7/2026

Lightweighting of components has become a key challenge in the development of modern transportation systems. In the automotive and aerospace industries, the overall mass of a vehicle has a significant impact on its fuel efficiency and manufacturing cost. Therefore, the lightweight design of vehicle components is crucial in the industrial field. Topology optimization (TO) is a computational design approach aimed at achieving lightweight designs. However, most existing studies focus on simplified academic models, with limited demonstration in real-world applications. This paper presents a revised TO workflow to obtain production-ready design and a practical implementation of TO in the design of three structural components in the aerospace industry: seatback frame, seat fuselage mount, and seat spreader. The revised TO workflow incorporates the practical demands of industry, including enhanced manufacturability and cost efficiency through TO design. The resulting designs are evaluated to

Lee, Hanbok Jake, Shi, Yifan, Gray, Savannah, Orr, Mathew, Park, Taeil, Wotten, Erik, LeFrancois, Richard, Huang, Yuhao, Patel, Anuj, Kim, Hansu, Jalayer, Shayan, Burns, Nicholas, Hansen, Eric, Grant, Robert, Kok, Leo, Kim, Il Yong

Multi-Objective Reinforcement Learning Framework for Transmission Shift Schedule Optimization

2026-01-0162

4/7/2026

Building upon previous work that successfully employed a Reinforcement Learning (RL) agent for the autonomous optimization of transmission shift programs to enhance fuel efficiency, this paper addresses a critical limitation of that approach: the neglect of human-centric factors. While the prior methodology achieved substantial fuel consumption reductions by training an RL agent in a Software-in-the-Loop (SiL) environment, it did not explicitly account for aspects such as driver comfort and preferences, which are paramount for real-world user acceptance and drivability. This work presents a multi-objective optimization framework extending the artificial calibrator to simultaneously maximize fuel efficiency and enhance driver comfort. The method introduces a modified RL reward function that penalizes undesirable shift behavior to ensure a smooth driving experience (drivability). This new methodology also incorporates a mechanism to capture and integrate driver preferences, moving beyond

Kengne Dzegou, Thierry Junior, Schober, Florian, Rebesberger, Ron, Henze, Roman, Sturm, Axel

Improving Winter Driving Range of EVs with a Novel Desiccant Device: Ceramic Humidity Regulator (CHR)

2026-01-0117

4/7/2026

With rapid growth of Electric Vehicles (EVs) in the market, challenges such as driving range, charging infrastructure, and reducing charging time needs to be addressed. Unlike traditional Internal combustion vehicles, EVs have limited heating sources and primarily uses electricity from the running battery, which reduces driving range. Additionally, during winter operation, it is necessary to prevent window fogging to ensure better visibility, which requires introducing cold outside air into the cabin. This significantly increases the energy consumption for heating and the driving range can be reduced to half of the normal range. This study introduces the Ceramic Humidity Regulator (CHR), a compact and energy-efficient device developed to address driving range improvement. The CHR uses a desiccant system to dehumidify the cabin, which can prevent window fogging without introducing cold outside air, thereby reducing heating energy consumption. CHR is based on desiccant dehumidification

Sakai, Naoki, Takahiko, Nakatani, Shinoda, Narimasa, Ihara, Yukio, Wakida, Norihiro, Kato, Kyohei, Anoop, Reghunathan-Nair

Automotive Aerodynamics Surrogate Modeling using NVIDIA PhysicsNeMo on Mid-Sized SUV GM Dataset

2026-01-0600

4/7/2026

Aerodynamic simulations are crucial in vehicle design and performance evaluation. Traditionally, these simulations utilize Computational Fluid Dynamics (CFD) techniques to compute flow quantities such as velocity, pressure, and wall-shear stresses. Accurate prediction of these quantities is vital for estimating drag and lift forces, which directly impact fuel efficiency, stability, and acoustics. This study focuses on developing an AI surrogate for aerodynamic design of production mideo-size SUVs using NVIDIA’s PhysicsNeMo framework. Firstly, high-fidelity 3D CFD data are generated using first-principles solvers on 102 different geometry variants at a uniform inlet velocity of 38.89 m/s and a fixed set of boundary conditions. The DoMINO (Decomposable Multiscale Iterative Neural Operator) AI model, part of the PhysicsNeMo framework, is then used to train on this dataset, accurately predicting surface pressure and flow fields around vehicles for rapid estimation of critical aerodynamic

Keum, Seunghwan, Raul, Vishal, Grover, Ronald, Parrish, Scott, Ranade, Rishikesh, Ghasemi, Abouzar, Kamenev, Alexey, Tadepalli, Srinivas

Optimization Method for Vehicle Shape to Drastically Reduce Aerodynamic Drag in Heavy-Duty Vehicles

2026-01-0603

4/7/2026

Since air drag is proportional to the square of the speed, it is expected that reducing air drag will significantly improve fuel efficiency for on-highway trucks and buses, which are often driven at high speeds. Therefore, the purpose of this study is to propose an optimization method for vehicle shape to drastically reduce aerodynamic drag in heavy-duty vehicles. Using NSGA-II, one of a genetic algorithm, the overall vehicle shape was optimized with drag coefficient (CD) and lift coefficient (CL) values as objective functions and design variables as parameters in a total of 13 locations. Among the Pareto solutions, an 86% reduction in CD was achieved compared to the base shape when the CD value was the lowest. Since the CL value remains low with this shape, it can be seen that driving stability does not deteriorate. Among the design variables in optimization, it was confirmed that the corner radius of the vehicle side was particularly effective in reducing the CD value. In addition

Kawano, Daisuke

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

Real-time Parameter Optimization for Eco-driving Control in Connected and Automated Vehicles Using Reinforcement Learning

2026-01-0041

4/7/2026

This paper introduces a novel methodology to enhance the energy efficiency of eco-driving controllers in Connected and Automated Vehicles (CAVs) by leveraging reinforcement learning (RL) techniques for real-time parameter optimization. Traditional eco-driving strategies rely on fixed control parameters, which limit adaptability across diverse traffic and road conditions. To address this, we apply continuous action space RL algorithms, specifically Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO), to dynamically tune four key parameters within a model predictive control framework that is grounded in Pontryagin’s Maximum Principle (PMP). These parameters influence acceleration, braking, cruising, and intersection-approach behaviors, making them critical for achieving optimal eco-driving performance. Our study employs Argonne National Laboratory’s RoadRunner simulator, a Simulink-based environment designed for high-fidelity CAV analysis, incorporating

Zhang, Yaozhong, Ammourah, Rami, Han, Jihun, Moawad, Ayman, Shen, Daliang, Karbowski, Dominik

MBSE-Driven XIL Workflow for Energy Evaluation of Automated Vehicles: From Simulation to VIL Testing

2026-01-0289

4/7/2026

With the increasing market penetration of automated vehicles, there is a critical need for credible and repeatable methods to quantify their energy impacts. This paper presents a Model-Based Systems Engineering (MBSE)-driven Anything-in-the-Loop (XIL) methodology for quantifying the powertrain energy consumption and potential savings from various controls for automated vehicles in realistic road scenarios while preserving high-fidelity powertrain behavior. The novelty of this approach lies in its use of a unified MBSE backbone (AMBER: Argonne National Laboratory’s [Argonne’s] MBSE-centric platform for transportation energy analysis) to automate the seamless and traceable progression from pure simulation to Vehicle-in-the-Loop (VIL) testing. This work utilizes Argonne's multi-vehicle simulation tool, RoadRunner, which automatically constructs closed-loop road scenarios (road geometry, vehicle sensors, other vehicles, and traffic controls) and connects them to Argonne’s validated, high

Jeong, Jongryeol, Sharer, Phillip, Di Russo, Miriam, Das, Debashis, Zhang, Yaozhong, Karbowski, Dominik

Simulation Verification and Design Guide for P1+P2 Hybrid Built-in Damper System

2026-01-0006

4/7/2026

The damper system in a hybrid TMED system reduces engine-induced vibration and damps the rapid torsional torque applied by the motor through spring stiffness. Furthermore, the built-in damper system of the P1+P2 TMED-II hybrid system offers improved fuel efficiency compared to the external damper system of the existing P0+P2 TMED-I. Although the internal layout of the transmission is limited, the built-in damper system was redesigned to accommodate installation between the P1 and P1 motor. However, CAE analysis techniques for damper systems are currently not clearly defined, and research data on their strength under rotational torque loads are lacking. To reduce development costs and provide direction, CAE analysis technology development and validation are necessary. In this study, a finite element model of the damper system was developed and compared with experimental results to ensure CAE reliability. Furthermore, based on the validated model, structural and fatigue durability

Sun, Hyang Sun, Ganesan, Karthikeyan

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

Modular Smart Corner Systems for Next-Generation Electric Vehicle Architecture

2026-01-0639

4/7/2026

The transition to software-defined vehicles (SDVs) necessitates a paradigm shift in both control strategies and vehicle architecture. The EU-funded R&D project SmartCorners addresses this challenge by developing integrated, modular, and scalable smart corner systems (SCS) that combine in-wheel motor (IWM)-based propulsion, brake blending, active suspension system, and steer-by-wire functionality in one module. These SCS can be retrofit or smoothly integrated into the highly adaptable skateboard chassis architecture of modern electric vehicles (EVs), enabling scalable deployment across diverse vehicle types. The central approach of this paper is the utilization of artificial intelligence (AI) and machine learning (ML) to implement multi-layer, data-driven control strategies, facilitating real-time actuation, fault mitigation, and user-centric EV architecture. The SmartCorners project strives to demonstrate significant enhancements, including improved real-world driving range due to

Ratz, Florian, Armengaud, Eric, Formento, Cecilia, Moscone, Giulia, Sorrentino, Gennaro, Bisciaio, Giorgio, Sorniotti, Aldo, Amati, Nicola, Braun, Daniel, Deibler, Bernd, Boxberger, Valerius, Sottile, Salvatore, Ivanov, Valentin, Fuse, Hiroyuki, Kompara, Tomaž

An Adaptive Brake Force Allocation Strategy for Rear-Wheel Driven EVs

2026-01-0636

4/7/2026

Due to changed requirements compared to conventional propulsion concepts, electromobility demands new and innovative strategies for energy-efficient vehicle motion control. For example, the challenge in purely rear-wheel drive (RWD) electric vehicles (EVs) is to achieve a maximum of regenerative braking power in order to increase energy recovery and to ensure, that this does not impair the braking stability. Within this conflict between energy efficiency and braking dynamics, it is necessary to design an intelligent strategy to optimise recuperation. This paper presents such a strategy, which improves an existing approach formerly presented by the authors, but specifically optimised to overcome weaknesses. The previous approach had two major limitations: First, the efficiency map of the in-wheel machines (IWMs) was not considered. Second, there was no possibility of switching flexibly between different brake force distributions to guarantee both, maximized recovery potential and high

Mitsching, Thomas, Heydrich, Marius, Ivanov, Valentin

Look-Ahead Information based Predictive Cruise Controller for Energy Efficient Powertrain Operation of Heavy-Duty Electric Trucks

2026-01-0039

4/7/2026

Heavy-duty electric trucks represent a growing innovation in the transport and logistics sector, aiming to reduce emissions and reliance on fossil fuels. A major challenge with battery electric trucks is the long recharging time which takes significantly longer than refueling conventional diesel trucks. This limitation highlights the importance of optimizing powertrain operations to reduce energy losses and maximize efficiency. One effective approach is implementing optimal speed control through a predictive cruise controller. By anticipating road conditions, traffic, and elevation changes, the predictive cruise controller can adjust the truck’s speed in real time to minimize energy consumption, enhancing the range and reducing the need for frequent charging. Many problem formulations for electric trucks focus primarily on minimizing the energy required at the wheels, often overlooking the impact of powertrain efficiencies. This simplification neglects critical factors such as the

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

Optimization of Engine Cooling System for Improved Fuel Efficiency and Vehicle Marketability

2026-01-0297

4/7/2026

Effective thermal management in internal combustion engines is essential for meeting increasingly stringent emissions regulations and achieving fuel efficiency improvements. This study introduces a novel and comprehensive approach to optimize engine thermal management by addressing key system components, including coolant circuit design, Integrated Thermal Management Module (ITM) control strategies, port-specific flow management, zero-flow operation techniques, and HVAC (Heating, Ventilation, and Air Conditioning) settings standardization. Unlike previously published works, this study focuses on reducing coolant circuit thermal mass to accelerate engine and component warm-up, refining ITM control logic through linear mapping and advanced signal filtering for precision, and enhancing zero-flow operation for minimizing lubricant oil dilution during start-up and reducing heat loss under low ambient conditions. Additional optimizations include port-specific adjustments and radiator flow

Lee, Changjoo, Lee, Kyumin, Kim, Seonyeong, Nam, Choonho, Yoo, Jihun

Evaluating the Impact of Drag Coefficient Changes on Driving Energy Using a Probability Distribution Model of Yaw Angle Based on Empirical Data from North American Highway

2026-01-0615

4/7/2026

This study estimates the impact on driving energy of differences in aerodynamic characteristics for yaw angle from natural wind during North American Highway mode driving. A previous study [1] clarified the potential to estimate the fuel consumption impact of natural wind by integrating the drag coefficient yaw characteristics and yaw angle occurrence probability. The natural wind was measured on a vehicle while driving a representative North American Highway test course [2]. Driving energy is predicted from the obtained yaw probability and the drag coefficient yaw sweep data in a wind tunnel. Measurements were conducted every weekday for 8 hours in 2023, covering 70% of the traffic volume. The validity of the measurement period was evaluated by the deviation from the annual average of wind direction and speed. Since yaw probability varies depending on the road environment, it is necessary to weigh the road environment type probability when calculating the driving energy. The

Onishi, Yasuyuki, Nucera, Fortunato, Nichols, Larry, Metka, Matt

A Comprehensive Modelling Tool for Heavy-Duty Hybrid Fuel Cell/Battery Powertrains

2026-01-0430

4/7/2026

The global transition towards sustainable transportation is driving the development of efficient, low-emission propulsion systems. Battery-electric solutions are effective in urban contexts, but face limitations in heavy-duty and long-haul applications due to the size and weight of the required energy storage. Hybrid battery/fuel cell powertrains offer a promising alternative for such use cases, reducing vehicle mass and charging times while maintaining high energy efficiency. This study presents an original zero-dimensional MATLAB/Simulink model, named HyPoST (Hydrogen Powertrain Simulation Tool), for a parallel hybrid fuel cell/battery system, here applied to heavy-duty vehicles. The model encompasses the main vehicle sub-systems, including the fuel cell stack with auxiliaries, battery pack, electric drive, transmission and the vehicle longitudinal dynamics, coordinated through a rule-based energy management strategy. Two representative heavy-duty vehicle configurations were analysed

Montecchi, Gianluca, Martoccia, Lorenzo, D'Adamo, Alessandro

Enabling Sustainable E-Mobility: An Edge–Cloud Collaborative Framework for Battery Lifecycle Health Management in Electric Vehicles

2026-01-0460

4/7/2026

The rapid adoption of electric vehicles (EVs) is a cornerstone of the transition to sustainable transportation. However, uncertainty regarding battery degradation remains a significant obstacle, hindering vehicle energy efficiency, operational safety, and the recovery of end-of-life value. Accurate estimation of the battery state of health (SOH) and prediction of the remaining useful life (RUL) are therefore critical for sustainable vehicle lifecycle management. This study proposes an edge–cloud collaborative intelligent framework for in-vehicle deployment that leverages a Transformer-based architecture to jointly model SOH and RUL. The cloud-side model retains the full configuration to capture long-term degradation trajectories for high-accuracy RUL prediction. A lightweight edge-side model, engineered via pruning and knowledge distillation, delivers millisecond-level inference for real-time SOH estimation onboard the vehicle. To ensure efficiency, only four core health indicators are

Gao, Weimin, Lv, Zhilong, Ou, Shiqi(Shawn)

From LLM to Deep Learning: Efficient Simulation of Last-Mile Energy Behavior in Campus Communities

2026-01-0461

4/7/2026

Communities are critical nodes in the urban energy network, integrating energy, transportation, and building systems to enhance overall energy efficiency. Accurate management of these systems depends on characterizing individual energy-demand behaviors. However, traditional last-mile behavioral models often fail to capture the complex and non-linear nature of human decision-making, underscoring the need for advanced simulation techniques. Although agent-based simulations powered by Large Language Models (LLMs) have demonstrated considerable efficacy across diverse domains, their substantial computational demands, specifically in terms of token consumption, pose pronounced constraints in large-scale simulations involving tens of thousands of agents, considerably limiting their practical applicability. To address this challenge, a deep learning-based approach is proposed for single-step prediction of last-mile behavior. A hybrid architecture, consisting of an MLP encoder and a

Yang, Zhifeng, Chen, Yongjian, Ou, Shiqi(Shawn)

Optimization Strategies of Gliding Arc Plasma-Based Ammonia Cracker towards Onboard Applications

2026-01-0292

4/7/2026

Ammonia has emerged as a viable hydrogen energy carrier owing to its superior hydrogen density and mature industrial utilization. However, ammonia faces critical challenges including inadequate ignition characteristics and sluggish combustion kinetics, necessitating supplementary high-reactivity fuels for optimizing combustion. Onboard ammonia decomposition technology resolves this problem through on-demand hydrogen real-time production. Among existing ammonia decomposition methods, gliding arc plasma (GAP) demonstrates exceptional promise for onboard hydrogen production given its high processing flow rate，decent hydrogen conversion rate, and transient response capability. Prevailing research predominantly relies on experimental approaches, with insufficient understanding of the effects of specific electrical field parameters and inlet pressure on system performance. This study established a quasi-one-dimensional numerical model for GAP-assisted ammonia decomposition. A comprehensive

Dong, Guangyu, Li, Xian, Zhou, Yanxiong, Xu, Jie, Li, Liguang

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

Advanced Integrated Chassis Control for Improved Energy Efficiency and Driving Experience in Electric Vehicle Applications

2026-01-0643

4/7/2026

Complexity of modern ground vehicles grows constantly, since car manufacturers want to provide functionality, while customers are expecting innovation and recent technologies to be integrated into the latest models released to the market. Recent advances in hard- and software opened the gates for new means of vehicle control and operation. Especially the transition to electric propulsion systems and decoupled chassis actuators offer completely new opportunities of dynamics control and manipulation. This paper presents an approach for integrated chassis and vehicle motion control in (battery) electric vehicle applications by using new and innovative controllers as well as mechatronic chassis systems. In several experiments on public roads with a fully instrumented vehicle demonstrator, that features in-wheel based rear-wheel drive and a hybrid brake-by-wire-system, the proposed control is tested under real environmental and traffic conditions with respect to aspects like energy

Heydrich, Marius, Mitsching, Thomas, Ivanov, Valentin

Development and Testing of a Concentric Tube Camshaft for Continuous Adjustment of Intake Valve Duration and Timing in Otto and Miller Cycle Operations

03-19-02-0010

4/1/2026

The development of technologies capable of expanding the operational flexibility of internal combustion engines—particularly through advanced valve actuation strategies—has become essential for improving energy efficiency and reducing exhaust emissions. This work presents the design, manufacturing, and experimental evaluation of a novel, mechanically simple, and low-cost valve control system intended for spark-ignition engines originally designed to operate under the Otto cycle. The proposed innovation, designated VVT-D (Variable Valve Timing—Duration), introduces continuous and independent control of intake valve opening duration using a concentric tube camshaft architecture. Unlike conventional variable valve timing systems limited to phase control, the VVT-D concept enables continuous transition between Otto- and Miller-equivalent operating conditions by modulating intake valve duration as a function of engine load. This approach allows engine load control via Late Intake Valve

Alvares, Gabriel Coelho Rodrigues, Woiski, Emanuel Rocha, dos Santos, Paulo Sergio Barbosa, Kashani, Masoud Ghanbari, Gasche, José Luiz

Control Strategy Optimization to Improve Energy Efficiency of Electric Wheel Loader Wheel Drive System

02-19-02-0011

3/27/2026

Driven by the dual-carbon goals of “peak carbon emissions” and “carbon neutrality,” improving energy efficiency in electric construction machinery has become a key focus. This study proposes an energy-saving torque control strategy for the traction motor of electric wheel loaders, aiming to reduce drive system energy consumption. The innovation lies in coupling parameter optimization of the pedal–torque mapping and regenerative braking to enhance overall efficiency. An electric model was built using Cruise and validated against real-world V-cycle test data, showing good agreement with an average relative error of 4.08%. Based on the model, two optimized control strategies were developed and evaluated through simulations and field tests. The results showed energy savings of 7.08% and 16.18% in simulation, and 6.83% and 15.51% in tests, respectively, demonstrating the effectiveness and practical value of the proposed method.

Ming, Qiaohong, Wang, Yangyang, Wang, Feng

System-Level Performance of Torque Vectoring Controllers for Electric Vehicles with In-Wheel Motors

10-10-04-0029

3/13/2026

Torque Vectoring (TV) is a critical control technology for enhancing the vehicle dynamics and stability of electric vehicles equipped with four-wheel-independent-drive (4WID) systems. A central challenge in TV design is managing the trade-off between maximizing handling performance and minimizing energy consumption, a crucial factor for EV range. While numerous advanced TV control strategies have been proposed, a comprehensive and comparative benchmark of foundational controllers evaluated on a platform that captures this trade-off is notably absent from the literature. Among the numerous TV control strategies proposed in literature, they are typically evaluated using simplified vehicle models that neglect the detailed dynamics and efficiency losses of the electric powertrain. This study addresses this gap by presenting a comprehensive comparison of six distinct TV control strategies—PID, LQR, two first-order Sliding Mode Controls (SMC), and two second-order SMCs. The controllers are

de Carvalho Pinheiro, Henrique, Carello, Massimiliana

Eco-Driving Control for Electric Vehicles with Multi-Speed Transmission: Optimizing Vehicle Speed and Powertrain Operation in Dynamic Environments

12-09-03-0021

2/28/2026

This article presents an eco-driving algorithm for electric vehicles featuring multi-speed transmissions. The proposed controller is formulated as a co-optimization problem, simultaneously optimizing both vehicle longitudinal speed and powertrain operation to maximize energy efficiency. Constraints derived from a connected vehicle–based traffic prediction algorithm are used to ensure traffic safety and smooth traffic flow in dynamic environments with multiple signalized intersections and mixed traffic. By simplifying the complex, nonlinear mixed-integer problem, the proposed controller achieves computational efficiency, enabling real-time implementation. To evaluate its performance, traffic scenarios from both Simulation of Urban MObility (SUMO) and real-world road tests are employed. The results demonstrate a notable reduction in energy consumption by up to 11.36% over an 18 km drive.

He, Suiyi, Sun, Zongxuan

Optimization of Combustion Stability and Fuel Economy for Dedicated Hybrid Engines with High Exhaust Gas Recirculation Rate

03-19-02-0008

2/26/2026

In recent years, the rapid growth of hybrid vehicles has driven the development of dedicated hybrid engines (DHEs) as a key powertrain technology for achieving high thermal efficiency and low emissions. Driven by stringent emissions regulations and demand for improved fuel economy, enhancing thermal efficiency in gasoline engines remains a critical industry challenge. Exhaust gas recirculation (EGR) technology dilutes oxygen in the intake charge, suppresses knock, and optimizes combustion phasing. However, excessive EGR rates compromise combustion stability by inducing elevated cyclic variability and potential misfire, posing challenges in maintaining stable combustion and improving fuel efficiency at high EGR levels. Thus, combustion stability and fuel efficiency optimization in Geely’s DHEs under high EGR conditions was investigated in this article. In this study, a high tumble combustion system was designed to enhance charge motion and promote stable flame propagation. Furthermore

Li, Qiang, Deng, Xiaorong, Ren, Siming, Zhang, Peiyi, Zhu, Yunfeng, Li, Hongzhou, Yan, Pingtao, Gu, Xiangsheng

Airport Electrical Power System Harmonics

AIR5387 (Current)

2/17/2026

This SAE Aerospace Information Report (AIR) is intended to cover all airport 50 or 60 Hz electrical systems as well as all electrical utilization equipment that is attached to those systems.

AGE-3 Aircraft Ground Support Equipment Committee

The Membrane Engine: Concept, Model, and First Results

03-19-01-0003

2/12/2026

This study investigates the feasibility of a novel internal combustion engine (ICE) architecture, termed the membrane engine, in which the conventional piston is replaced by a flexible elastic membrane. Although the concept appears in several patent documents proposing reduced friction, improved sealing, and lower heat losses, no empirical data has been published to support these claims. To the authors’ knowledge, this work presents the first membrane engine built and experimentally tested. The primary aim is to verify whether such an engine can operate as a functional ICE, regardless of its current efficiency or performance level. To support concept validation, a simplified mathematical model was developed to describe the membrane’s deformation and its effect on combustion chamber volume. Unlike conventional piston engines, the membrane introduces a pressure-dependent geometry, enabling a variable compression ratio. The model is not intended to predict performance but to assist in

Allmägi, Roland, Ilves, Risto

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

AI Smart Timer-Based Power Control and Energy Monitoring System for Home Appliances

2026-28-0060

2/12/2026

The paper presents the design and implementation of an AI-enabled smart timer-based power control and energy monitoring solution for household appliances. The proposed system integrates real-time sensing of electrical device parameters with cloud artificial intelligence for predictive analytics and automatic control. Continuous measurement of voltage, current and power consumption of the connected appliances are performed for analysis of the usage patterns. The appliance operation is completely automated by choosing between the best option which is the user-defined schedule or the load shifted schedule recommended by AI. The AI recommendation depends on peak demand of the day and the current load requirement thereby aiding approximate smoothening of daily load curve and improving load factor. The data collected is transmitted to the cloud for real-time and historical data collection, for prediction of consumption patterns, anomaly detection, and clustering appliances according to their

D, Anitha, D, Suchitra, Jain, Utsav, Maity, Souvik, Dinda, Atish

Accidental Dooring Warning and Assist System

2026-28-0053

2/12/2026

Dooring accidents occur when a vehicle door is opened into the path of an approaching cyclist, motorcyclist, or other road user, often causing serious collisions and injuries. These incidents are a major road safety concern, particularly in densely populated urban areas where heavy traffic, narrow roads, and inattentive behavior increase the likelihood of such events. To address this challenge, this project presents an intelligent computer vision based warning system designed to detect approaching vehicles and alert occupants before they open a door. The system can operate using either the existing rear parking camera in a vehicle or a USB webcam in vehicles without such a feature. The captured live video stream is processed by a Raspberry Pi 4 microprocessor, chosen for its compact size, low power consumption, and ability to support machine learning frameworks. The video feed is analyzed in real time using MobileNetSSD, a lightweight deep learning object detection model optimized

C, Jegadheesan, T, Karthi, Gurusamy, Varun Sankar, Balraj, Tharun, Murugaiya, Tamilselvan

Design evolution of Novel Subframe for a Multilink Rear Suspension of an e-SUV

2026-28-0007

2/12/2026

As there is a major shift in customer demand for energy efficient transportation, electric vehicle development has taken prominence worldwide as they provide pollution free and noise free mobility. The subframe being an important structural component of the chassis system, the designers always find it challenging to provide best-in-class rear subframe (RSF) optimized in terms of cost and weight within the available packaging space especially in an electric sport vehicular boundary. The main function of rear subframe is to transmit forces to BIW without deflections hence for this it should be very stiff. At the same time, it should be light in weight and simpler to industrialize. In the present work, the design evolution of a novel sub-frame assembly for a multilink rear suspension of a born electric sports utility vehicle (e-SUV) platform is detailed. With increased rear axle weight contributed by the battery weight and rear mounted motor, the design evolution of the rear subframe (RSF

Nidasosi, Basavraj Maruti, J, Ramkumar, Nayak, Bhargav, Mani, Arun, M, Sudhan

Development of Plug-In Hybrid Motorbike and Analysis towards Sustainable Urban Mobility

2026-28-0111

2/12/2026

The transportation system is one major catalyst to urban ecological imbalance. In developing countries, two-wheelers are considered a major mode of urban personal transportation because of their compactness, easy maneuver in heavy traffic and good fuel efficiency. In India, middle and lower middle-class people prefer to choose two wheelers, and these vehicles are dominantly fuelled by gasoline. Although, the energy consumption by a two-wheeler is comparatively less than that of a four-wheeler, they use about 60% of the nation’s petroleum for on-road vehicles and the impact on urban air quality and climatic change is significantly high. This high proportion of gasoline utilization and emission contribution by two wheelers in cities demand greater attention to improve urban air quality and near-term energy sustainability. Electrification of two-wheelers through the application of a plug-in hybrid idea is a promising solution. A plug-in hybrid motorbike was developed by putting forth a

Kannan, Prashanth, Shaik, Amjad, Talluri, Srinivasa Rao

Enhanced Solar Desalination Using Natural Dolomite-Integrated PCM and MWCN Thermal Storage: An Experimental Study

2026-28-0082

2/12/2026

Systems for solar desalination provide a practical and environmentally friendly way to turn salty or polluted water into drinkable water. Three configurations are experimentally investigated in this study: a traditional solar desalination system, a system integrated with a thermal energy storage unit (TESU) based on phase change material (PCM), Multi wall Carbon nano Tube were mixed with PCM at 2% of total volume of the PCM and a system that incorporates powdered natural dolomite/MWCNT at 1% each into the PCM-based TESU. Each of the four configurations was created, tested simultaneously, and thoroughly examined. In comparison to the Standard Still (SS), the experimental findings showed that the adoption of PCM-based TESUs increased daily cumulative water output (collection efficiency) by 24%, 26% with addition of MWCNT and the addition of dolomite powder/MWCNT further increased productivity by 27%. The average exergy efficiencies for for SS, SS with PCM, SS with nano enriched PCM, and

R L, Krupakaran, Petla, Ratnakamala, Anchupogu, Praveen, P, Umamaheswarrao, Satya Meher, R, Dunna, Vijay

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