Browse Topic: Energy conservation

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From LLM to Deep Learning: Efficient Simulation of Last-Mile Energy Behavior in Campus Communities2026-01-0461To be published on 04/07/2026
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, creating a 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 Transformer
Yang, Zhifeng, Chen, Yongjian, Ou, Shiqi(Shawn)
Enabling Sustainable E-Mobility: An Edge–Cloud Collaborative Framework for Battery Lifecycle Health Management in Electric Vehicles2026-01-0460To be published on 04/07/2026
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)
ENERGY AND OPERATIONAL EFFICIENCY OF SHARED AUTONOMOUS FLEET2026-01-0464To be published on 04/07/2026
Electrifying shared autonomous fleets (Robotaxis) presents challenges in balancing decarbonization, service quality, and operational costs, given the limited driving range, long charging times, and suboptimal planning of charging infrastructure. This study develops an integrated energy management and fleet dispatching simulation framework to support cost-effective, low-carbon Robotaxi deployment. The proposed system models both battery electric vehicles (BEV) and internal combustion engine vehicles (ICEV) technologies, and is extensible to other powertrain types. The study also integrates a life cycle assessment module to evaluate well-to-wheel carbon emissions. A total of 1,440 scenarios are designed to test the performance of two service modes (ride-hailing vs. ride-pooling) in terms of energy consumption, emissions, service quality, and operational costs, across varying levels of trip demand and market penetration of different powertrain technologies. The test aims to verify the
Tang, Kang, Abdulsattar, Harith, Yang, Hao, Wang, Jinghui
A Novel Development Method for a Control Model of TWC in Hybrid Electric Vehicles Based on Mechanism Simplification and Temperature Update2026-01-0371To be published on 04/07/2026
Under increasingly stringent emission regulations, hybrid electric vehicles face heightened requirements for tailpipe emission control. Their complex operational profiles and frequent transitions between power modes pose significant challenges to experimental efforts in emission measurement and aftertreatment control, leading to substantially increased economic and temporal costs that severely constrain emission optimization. To address these challenges, this study employs the development and calibration of a high-accuracy virtual model to conduct simulation experiments that accurately replicate real-world driving conditions, significantly reducing development costs while maintaining predictive accuracy. Focusing on hybrid electric vehicles, we developed an aftertreatment model integrating catalytic reaction mechanisms and energy conservation equations, incorporating coupled temperature and concentration calculations. This model enables real-time monitoring of dynamic oxygen storage
Lian, Xuetong, Wang, Zhiao, Liu, Weiqiang, Chen, Tao
From Propane to Pi: Affordable AI-Driven Solutions for Sustainable Bus Heating.2026-01-0164To be published on 04/07/2026
The design of an affordably luxurious school bus means improving passenger comfort while greatly decreasing energy usage and emissions. The key consideration is to replace traditional propane heating with an electric heated-seat system and supported by green electricity/energy efficient smart power management. The system is designed from a 24V/48V battery system; to a Smart Energy Cube - a portable dock in power module requiring solar, battery and grid connection. Seat heating is only applied to seats that are occupied. The on-board image processing models (YOLOv5/YOLOv8, OpenCV, TensorFlow Lite) are run on affordable/portable microcomputers capable of detecting the number of occupied and available seats e.g. Raspberry Pi. There is one bottle neck when using the Raspberry Pi in the experiments to validate concept, when using low/medium power frame rates of ~ 1 fps for Raspberry Pi 3; to 7-8 fps with a Raspberry Pi 5. As a baseline, there is still potential for improvement and
Chikkala, Daney Bhargav, Zadeh, Mehrdad, TAN, Teik-Khoon, Ponnam, Jitin, BATTE, JAI RATHAN
Optimization Strategies of Gliding Arc Plasma-Based Ammonia Cracker towards On-Board Applications2026-01-0292To be published on 04/07/2026
Ammonia has emerged as a viable hydrogen energy carrier owing to its superior hydrogen density, 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 cracking technology resolves this problem through on-demand hydrogen in-time production. Among existing decomposition methods, gliding arc plasma (GAP) demonstrates exceptional promise for on-board crackers given its decent hydrogen conversion rate and transient response capability. Prevailing research predominantly employs experimental methodologies wherein macroscopic parameters inadequately proxy microscopic electric field characteristics, thereby impeding mechanistic insight. This study establishes a quasi-1D numerical model to interrogate how electric field parameters modulate ammonia-to-hydrogen conversion yield and system energy efficiency in
Dong, Guangyu, LI, Xian, ZHOU, Yanxiong, XU, Jie, Li, Liguang
Energy Efficiency Analysis of U-Haul Truck and Trailer Systems: Impact of Tongue Weight and Load Variations2026-01-0466To be published on 04/07/2026
The demand for improved energy efficiency in real-world vehicle operations continues to grow with technology enhancement. When transporting large cargos with passenger pickup trucks and rental trailers, the interaction between vehicle payload, towing configuration, and fuel consumption becomes a key factor in overall system efficiency. Understanding how towing configurations and trailer loading influence fuel consumption and vehicle performance is critical for both consumer guidance and vehicle system design. This study investigates the energy efficiency of U-Haul truck and trailer systems, with a particular focus on the influence of trailer tongue weight. U-Haul truck and trailer simulation models were developed using AVL Vehicle Simulation Model (VSM) software, with an F-350 engine brake-specific fuel consumption (BSFC) map integrated to represent realistic engine performance. Two configurations with equal payload were evaluated: (1) a U-Haul truck alone, and (2) a U-Haul truck
Wang, Gang, Yang, Allan, Chen, Yan
An Adaptive Brake Force Allocation Strategy for Rear-Wheel Driven EVs2026-01-0636To be published on 04/07/2026
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 optimises it 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
Mitsching, Thomas, Heydrich, Marius, Ivanov, Valentin
A Comparison of Road Grade Preview Signals from Lidar and Maps2026-01-0026To be published on 04/07/2026
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
Advanced Integrated Chassis Control for Improved Energy Efficiency and Driving Experience in Electric Vehicle Applications2026-01-0643To be published on 04/07/2026
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
Study on performance parameters of retarder in mountain condition of hybrid electric vehicle2026-01-0617To be published on 04/07/2026
In mountain driving scenarios, the continuous braking demands of vehicles pose significant challenges to the performance and reliability of braking systems. Traditional fuel-powered vehicles primarily rely on friction brakes for deceleration, which face issues such as high thermal fade risks and severe brake wear. While pure electric vehicles can utilize motor energy recovery to provide braking force and recover energy, their braking capacity is limited by the maximum regenerative torque of the motor. Moreover, when the battery State of Charge (SOC) is high, the energy recovery function may be restricted or disabled to protect the battery, leading to a sharp reduction or complete loss of braking torque—a safety hazard. Range-extended hybrid vehicles combine the advantages of both engine and electric drive systems. During long downhill operations, their motors can also serve as priority decelerators for efficient energy recovery. However, similar to pure electric vehicles, their motor
duan, xianqi, Tan, Gangfeng, Li, LiuYuanHeng, Tian, Tian, Yang, Yong
Look-ahead Information based Predictive Cruise Controller for Energy Efficient Powertrain Operation of Heavy-Duty Electric Trucks2026-01-0039To be published on 04/07/2026
Heavy-duty electric trucks represent a growing innovation in the transport and logistics sector, aiming to reduce emissions and reliance on fossil fuels. A major challenge with battery electric trucks is the long recharging time which takes significantly longer than refueling conventional diesel trucks. This limitation highlights the importance of optimizing powertrain operations to reduce energy losses and maximize efficiency. One effective approach is implementing optimal speed control through a predictive cruise controller. By anticipating road conditions, traffic, and elevation changes, the predictive cruise controller can adjust the truck’s speed in real time to minimize energy consumption, enhancing the range and reducing the need for frequent charging. Many problem formulations for electric trucks focus primarily on minimizing the energy required at the wheels, often overlooking the impact of powertrain efficiencies. This simplification neglects critical factors such as the
Safder, Ahmad Hussain, Villani, Manfredi, Khuntia, Satvik, Nelson, James, Meijer, Maarten, Ahmed, Qadeer
Accelerating Electrification Through Modular Propulsion: GM’s Strategy for BEV and BET Platforms2026-01-0410To be published on 04/07/2026
General Motors 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, 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 rationale behind
Liu, Jinming, Sevel, Kris, Anwar, Mohammad, Oury, Andrew, Welchko, Brian, Gagas, Brent
A Comprehensive Modelling tool for Heavy-Duty Hybrid Fuel Cell/Battery Powertrains2026-01-0430To be published on 04/07/2026
The global transition towards sustainable transportation is driving the development of efficient, low-emission propulsion systems. Battery-electric solutions are effective in urban contexts but face limitations in heavy-duty and long-haul applications due to the size and weight of the required energy storage. Hybrid battery/fuel cell powertrains offer a promising alternative for such use case, reducing vehicle mass and charging times while maintaining high energy efficiency. This study presents a zero-dimensional MATLAB/Simulink model of a parallel hybrid fuel cell/battery system for heavy-duty vehicles. The model encompasses the main vehicle subsystems, including the fuel cell stack with auxiliaries, the battery pack, the electric drive, the transmission and vehicle longitudinal dynamics, coordinated through a rule-based energy management strategy. Two representative vehicle configurations were analysed: a Group 2 urban delivery vehicle, and a Group 5 long-haul truck. Model validation
Montecchi, Gianluca, Martoccia, Lorenzo, D'Adamo, Alessandro
Characterizing Regenerative Braking of Tesla Model 3 Using Dynamometer Testing2026-01-0453To be published on 04/07/2026
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
Modelling of Le Mans Daytona Hybrid Hypercar Powertrain System2026-01-0270To be published on 04/07/2026
This study presents the development, validation, and application of a full-vehicle simulation model of the BMW M Hybrid V8 LMDh Hypercar, designed to investigate and optimise hybrid energy recovery strategies in compliance with FIA and IMSA 2025 technical regulations. With increasing regulatory and sustainability demands in endurance racing, the deployment and recovery of electric energy using MGU-K have become pivotal for both performance and fuel efficiency. A physics-based simulation model was constructed in GT-SUITE, incorporating a map based internal combustion engine, transmission, and 50 kW rear-axle hybrid system. Complementary to this, a novel telemetry extraction method using Optical Character Recognition (OCR) in MATLAB and Python was developed to generate real-world drive cycles from on-board race footage. The model was validated using telemetry data. Subsequently, multiple energy deployment strategies were evaluated under varying race scenarios to identify optimal control
Buysse, Alexander, Samuel, Stephen
Optimization of Engine Cooling System for Improved Fuel Efficiency and Vehicle Marketability2026-01-0297To be published on 04/07/2026
Effective thermal management in internal combustion engines is essential for meeting increasingly stringent global emissions regulations and fuel efficiency standards. This paper presents a comprehensive solution to enhance engine thermal management performance by addressing key system aspects, including coolant circuit architecture, Integrated Thermal Management Module (ITM) control strategies, port-specific flow management, zero-flow operation, and standardization of HVAC (Heating, Ventilation, and Air Conditioning) settings. The research was conducted on a newly developed engine platform, where a suite of advanced thermal management measures was applied and verified through both vehicle and bench testing. Key improvements included reducing the thermal mass of the coolant circuit to enable faster warm-up of the engine and major components, as well as refining ITM control logic through linear mapping and signal filtering for more precise target coolant temperature regulation. Enhanced
Lee, ChangJoo, LEE, KyuMin, Kim, Seonyeong, Nam, Choonho, YOO, JiHun
AI-Powered Requirements Engineering & Design Optimization of Electric Powertrains2026-01-0500To be published on 04/07/2026
The development of electric vehicle powertrains is driven by diverse and often conflicting requirements. In early development phases, these requirements are often vague, incomplete, and 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 Optimization2026-01-0165To be published on 04/07/2026
Energy efficiency and range optimization remain critical challenges to the widespread adoption of battery electric vehicles (BEVs). As a result, there is a growing demand for intelligent driver assistance systems that can extend the operating range and reduce range anxiety. This paper presents an adaptive eco-feedback and driver rating system based on proximal policy optimization (PPO) reinforcement learning, designed to support drivers with the target to reduce energy consumption and maximize driving range. The system processes real-time driving data, such as velocity, acceleration and powertrain status. Map data of high quality is used to anticipate traffic events, including but not limited to speed limits, curves, gradients, preceding vehicles and traffic lights. This contextual awareness allows the system to continuously assess driving behavior and provide personalized, context-aware visual feedback alongside a dynamic driving behavior rating. A PPO agent learns optimal feedback
Stocker, Christoph, Hirz, Mario, Martin, Michael, Kreis, Alexander, Stadler, Severin
Real-time Parameter Optimization for Eco-driving Control in Connected and Automated Vehicles Using Reinforcement Learning2026-01-0041To be published on 04/07/2026
This paper introduces a novel methodology to enhance the energy efficiency of eco-driving controllers in Connected and Automated Vehicles (CAVs) by leveraging Reinforcement Learning (RL) techniques for real-time parameter optimization. Traditional eco-driving strategies rely on fixed control parameters, which limit adaptability across diverse traffic and road conditions. To address this, we apply continuous action space RL algorithms, specifically Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO), to dynamically tune four key parameters within a model predictive control framework that is grounded in Pontryagin’s Maximum Principle (PMP). These parameters influence acceleration, braking, cruising, and intersection-approach behaviors, making them critical for achieving optimal eco-driving performance. Our study employs Argonne National Laboratory’s RoadRunner simulator, a Simulink-based environment designed for high-fidelity CAV analysis, incorporating
Zhang, Yaozhong, Ammourah, Rami, Han, Jihun, Moawad, Ayman, Shen, Daliang, Karbowski, Dominik
Research on Reliability Test Methods for Electric Drive Axle Differentials2026-01-0007To be published on 04/07/2026
With the rapid socio-economic development, global issues such as the energy crisis and environmental pollution have become increasingly severe. Electric vehicles have garnered growing attention due to their environmental friendliness, simple structure, and high energy efficiency. Electric drive technology is regarded as the most ideal universal driving solution for pure electric vehicles, hybrid electric vehicles, and fuel cell vehicles. The electric drive axle, as a new type of integrated mechatronic transmission system, is being increasingly adopted owing to its advantages such as high drive efficiency, flexible spatial arrangement, and facilitation of digital and automated chassis control. This study focuses on reliability testing methods for the differential in electric drive axles, mainly including the extraction of reliability test conditions and feasibility analysis of the test scheme. The main research contents are as follows: First, based on specific parameters of an electric
Zheng, Hongyu, Li, Ziyu, Wang, Dajiang, tian, kai
A Study on Fatigue Life Prediction Method for Point-Based Joints Considering Multiple Fracture Modes – FDS, Blind Rivet, and Blind Nut Focus2026-01-0180To be published on 04/07/2026
The application of light weight alloys for vehicle bodies such as aluminum alloys and composite materials are rapidly advancing to support the construction of multi-material vehicle bodies. 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 introduces new challenges in manufacturing, particularly in joining technologies. Since different materials have varying mechanical properties, thermal behaviors, and surface characteristics, selecting appropriate joining methods is essential to ensure structural integrity and durability. Depending on the material types, thicknesses, production processes, and cost constraints, various joining techniques—such as
Takuno, Sougo, Isono, Toshiyuki, Urakawa, Kazushi, Goto, Suguru, Kawamura, Hiroaki, Niisato, Eita, Ishigami, Yuta
Multi-Objective Reinforcement Learning Framework for Transmission Shift Schedule Optimization2026-01-0162To be published on 04/07/2026
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 realworld 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
Simulation verification and design guide for P1+P2 hybrid built-in damper system2026-01-0006To be published on 04/07/2026
Among the hybrid system methods, the TMED (Transmission mounted electric device) method is divided into the TMED-1 system driven by the existing mass-produced P0+P2 motor and the TMED-2 system driven by the P1+P2 motor. It is known that the TMED-2 method improves power and fuel efficiency by about 10% or more compared to the TMED-1 due to the intervention of the P1 motor, and that carbon emissions can be reduced due to the improvement of engine thermal efficiency. However, the TMED-2 system is a structure that replaces the P0 motor with the P1 motor to improve power performance and transmission efficiency. The P1 motor is disadvantageous in terms of mountability due to its longer overall length compared to the P0. To solve this problem, the existing external damper system was designed and shaped to be assembled into the inner space between the P1 and P2 motors (built-in damper system), thereby securing mountability. The main development item that our company is currently researching is
Sun, Hyang Sun, GANESAN, KARTHIKEYAN
Electrified Propulsion System Balancing for Light Duty Full Size Truck and Sport Utility Vehicles: A 2026 North American Market Perspective2026-01-0412To be published on 04/07/2026
Achieving the stringent EPA CAFE 2032 standards for light-duty full-size trucks and SUVs in North American poses significant challenges. While Battery Electric Vehicles (BEVs) offer a clear path to zero tailpipe emissions, their widespread adoption in this segment faces hurdles including range anxiety, payload/towing capabilities, and traditional truck/SUV use cases. This paper investigates a balanced approach, focusing on optimizing propulsion system design with appropriate hardware content, can effectively meet future fuel economy and emissions standards. This investigation examines advanced BEVs and hybrid electric vehicle architectures, including full hybrids (HEVs), and plug-in hybrids (PHEVs) tailored for full-size trucks and SUVs. Considerations include the optimal sizing of internal combustion engines, electric motors, and battery packs to deliver robust performance while maximizing energy efficiency. This paper analyzes the integration of technologies such as electrified
Babcock, Dillon, Robinette, Darrell
26M-00552026-01-0603To be published on 04/07/2026
Since air drag is proportional to the square of the speed, it is expected that reducing air drag will significantly improve fuel efficiency for long-distance trucks and buses, which are often driven at high speeds. Therefore, the purpose of this study is to propose a conceptual shape that will dramatically improve the aerodynamic performance of heavy-duty vehicles, assuming that restrictions on overall length will be relaxed in the future. Using NSGA-II, a genetic algorithm, the overall vehicle shape was optimized with the objective functions CD and CL values and design variables as parameters in a total of 13 locations. Among the Pareto solutions, CD = 0.08 was obtained 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, when
Kawano, Daisuke
Improving Winter Driving Range of EVs with a Novel Desiccant Device: Ceramic Humidity Regulator (CHR)2026-01-0117To be published on 04/07/2026
With Rapid growth of Electric Vehicles (EVs) in the market, challenges such as driving range, charging infrastructure, and reducing charging time needs to be addressed. Unlike traditional Internal combustion vehicles, EVs have limited heating sources and primarily uses electricity from the running battery, which reduces driving range. Additionally, during winter operation, it is necessary to prevent window fogging to ensure better visibility, which requires introducing cold outside air into the cabin. This significantly increases the energy consumption for heating and the driving range can be reduced to half of the normal range. This study introduces the Ceramic Humidity Regulator (CHR), a compact and energy-efficient device developed to address driving range improvement. The CHR uses a desiccant system to dehumidify the cabin, which can prevent window fogging without introducing cold outside air, thereby reducing heating energy consumption. CHR is based on desiccant dehumidification
Sakai, Naoki, Takahiko, Nakatani, Shinoda, Narimasa, Ihara, Yukio, Wakida, Norihiro, Kato, Kyohei, Anoop, Reghunathan-Nair
Automotive Aerodynamics Surrogate Modeling using NVIDIA PhysicsNeMo on Mid-Sized SUV GM Dataset2026-01-0600To be published on 04/07/2026
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 mid-size SUVs using NVIDIA’s PhysicsNeMo framework. Firstly, high-fidelity 3D CFD data are generated using first-principles solvers on 100 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
Energy-Efficient Predictive Cruise Control in Heavy-Duty Electric Trucks with Optimization of the Penalty Factor as a Function of Payload2026-01-0042To be published on 04/07/2026
Heavy-duty Class 8 battery electric trucks not only offer a significant reduction in greenhouse gas (GHG) emissions compared to conventional diesel trucks but can also provide significant savings in fuel costs. To further enhance the energy and freight efficiency, Predictive Cruise Control (PCC) algorithms can be developed that generate optimal acceleration profiles for the vehicle by minimizing a cost function which combines both energy consumption and deviation from the desired velocity. A critical component of the cost function is the penalty factor, which governs the trade-off between energy use and travel time, which are two conflicting objectives in freight logistics. Selecting an appropriate penalty factor is essential, as freight deliveries are time-sensitive but minimizing energy consumption remains a priority. Moreover, variations in payload significantly affect vehicle dynamics and energy usage, making it critical to adapt the penalty factor to different payload conditions
Safder, Ahmad Hussain, Villani, Manfredi, Wang, Eric, Khuntia, Satvik, Nelson, James, Meijer, Maarten, Ahmed, Qadeer
Methodology to Quantify the Petroleum Equivalent Fuel Economy Impact of BEV Thermal System Energy Consumption2026-01-0132To be published on 04/07/2026
This paper will present research and 1D modeling results to support a proposed methodology to determine a fleet ambient weighted average Petroleum Equivalent Fuel Economy (PEFE) for the energy consumed by the thermal system of a BEV, including a proposal of how factor in the thermal system PEFE result into an OEM’s overall CAFE determination on an incentivized (not punitive) basis, within the existing regulatory framework. The topic is particularly important because EPA and NHTSA final rules issued in early 2024 eliminated A/C Efficiency Credits for BEVs that are currently in place for all classes of light duty vehicles. However, without some sort of regulatory incentivization, market forces will dictate thermal comfort system solutions that consume more energy – and with that more negative secondary effects such as higher greenhouse gas emissions and vehicle mass - than would otherwise be the case. Eliminating incentivization for BEVs is particularly concerning because the thermal
Taylor, Dwayne
A Novel Power-Split Hybrid Electric Architecture for Off-Road Tracked Vehicles: Design and Performance Evaluation.2026-01-0258To be published on 04/07/2026
Tracked off-road vehicles operate at low speeds with high tractive effort and frequent skid-steer maneuvers, conditions that push torque and power demand to extremes and exacerbate powertrain efficiency losses. Electrification can improve energy conversion and mobility for such duty cycles. This paper introduces a novel power-split hybrid electric architecture for a tracked vehicle and benchmarks it against three designs: a conventional mechanical driveline, a series hybrid, and a P2 parallel hybrid. To enable fair, architecture-agnostic comparisons, a supervisory controller based on Stochastic Dynamic Programming (SDP) schedules engine operation and power flow across all layouts under representative off-road scenarios, including skid-steer events, with varying terrain and power-demand profiles. Results show higher energy conversion efficiency (lower fuel use) for the proposed power-split architecture, followed by the parallel, then series, and lastly conventional configuration across
Ghate, Atharva, Sundar, Anirudh, Zhu, Qilun, Prucka, Robert, Figueroa-Santos, Miriam, Barron, Morgan, Castanier, Matthew P.
Application of topology optimization to production-ready passenger seat components design2026-01-0498To be published on 04/07/2026
Lightweighting of components has become a key challenge in the development of modern transportation systems. 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 design. However, most existing studies focus on simplified academic models, with limited demonstration in real-world applications. This paper presents a practical implementation of TO in the design of three structural components in the real-world aerospace field: seatback frame, seat fuselage mount, and seat spreader. The optimization process incorporates realistic loading conditions and boundary constraints, followed by necessary adjustments to ensure manufacturability and ergonomic compatibility, providing production-ready designs. In addition, strategies for improving stress distribution in the
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
Novel Air System for 300 kW Heavy Duty Fuel Cell2026-01-0434To be published on 04/07/2026
Hydrogen fuel cell powered vehicles for heavy duty trucks is a potential path for reducing heavy duty vehicle emissions in the future. The air handling system delivers the proper amount of air (oxygen) to react with fuel (hydrogen) in the fuel cell to produce power. Air delivery requires significant power and is the largest parasitic loss for a 300 kW fuel cell. Today’s systems use an electric motor to power an air compressor that supplies oxygen to the fuel cell stack. In addition to parasitic power loss, hydrogen fuel cell systems often have reliability issues associated with the air handling system. Reliability is of significant concern for heavy duty applications (especially long-haul applications). This project aims to improve both the electrical power consumption and reliability of hydrogen fuel cell air handling systems to meet the needs of heavy duty on-highway vehicle applications. The air handling is provided by a twin vortices series (TVS) compressor in addition to adding a
Reich, Evan, Swartzlander, Matthew, Wine, Jonathan, McCarthy, James, Miller, Eric, Akhtar, Saad, Reddy, Sharan, Lawy, TJ
System-Level Performance of Torque Vectoring Controllers for Electric Vehicles with In-Wheel Motors10-10-04-00293/13/2026
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 Environments12-09-03-00212/28/2026
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 Rate03-19-02-00082/26/2026
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 HarmonicsAIR5387 (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 Results03-19-01-00032/12/2026
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
Development of Plug-In Hybrid Motorbike and Analysis towards Sustainable Urban Mobility2026-28-01112/12/2026
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 Study2026-28-00822/12/2026
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
Design evolution of Novel Subframe for a Multilink Rear Suspension of an e-SUV2026-28-00072/12/2026
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
Intelligent Steering System to Improve Fuel Efficiency in Heavy Duty Vehicles2026-28-01032/12/2026
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 Appliances2026-28-00602/12/2026
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 System2026-28-00532/12/2026
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
TwinAV: Valve Timing Strategy for Divided Exhaust Periods in Turbocharged Spark-Ignition Engines: A Simulation-Based Evaluation03-19-01-00022/6/2026
Turbocharging is a common and simple method to utilize the exhaust heat of an internal combustion engine. However, conventional turbocharging exhibits the drawback of exhaust gas backpressure and thus increased residual gas mass in the cylinder. A promising concept to increase optimum efficiency is found in the TwinAV concept, which assigns divided exhaust valve cam timing and exhaust manifold configuration. This concept is hypothesized to reduce the static backpressure in the gas exchange loop and the residual exhaust gas amount in the gas exchange phase. In this article, a 1D simulation model was adapted to an existing 4-cylinder gasoline TC engine. Subsequently, the engine concept was applied to this engine model, whereas the focus was to achieve an engine layout for the entire engine speed range applicable for use in passenger vehicles. The results were compared at the full RPM range. Also, a load variation was conducted and benchmarked. The found results show an additional
Gotter, Andreas, Gotter, Alexander
Rewriting the engineer's playbook: What OEMs must do to spin the AI flywheel26AUTP02_042/1/2026
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
Plasma Surface Activation for Stronger, More Durable CFRP Bonds in Aerospace26AERP02_012/1/2026
Carbon fiber-reinforced polymers (CFRPs) have become essential in modern aerospace structures, from fuselage skins and wing components to nacelles, interior structures, and a growing range of primary load-bearing parts. Their high strength-to-weight ratio delivers major benefits in fuel efficiency, payload capacity, and fatigue performance. Yet achieving reliable adhesive bonds on CFRP surfaces remains a persistent engineering challenge. The low intrinsic surface energy of composites - particularly under thermal cycling, vibration, and moisture exposure - limits bond durability unless surfaces are properly prepared. Plasma surface treatment has emerged as a pivotal solution, offering a fast, controllable, and non-destructive way to increase surface energy, improve wettability, and enhance adhesion across complex geometries. This is especially important as the aerospace industry transitions from thermoset to thermoplastic composites (TPCs), which enable faster processing, lower
Curvature-Continuous Trajectory Refinement for Autonomous Vehicles Using a Pure Pursuit Based Path Smoothing Framework2026-26-01351/16/2026
Path planning is a key element of autonomous vehicle navigation, allowing vehicles to calculate feasible paths in challenging environments for applications like automated parking and low speed autonomous driving. Algorithms such as Hybrid A*, Reeds-Shepp, and Dubins paths are widely used and can generate collision-free paths but tend to create curvature discontinuities. These discontinuities result in sudden steering transitions, which create control instabilities, higher mechanical stress, and lower passenger comfort. To overcome these issues, this paper suggests a path-smoothing technique based on the pure-pursuit algorithm to produce smoothed curve paths appropriate for real-world driving. This method utilizes the practical approach of the original path, but removes sudden transitions that destabilize control. By ensuring smooth curvature, the vehicle undergoes fewer jerky steering actions, improved energy efficiency, less actuator wear, and improved high-speed tracking. This paper
S, Shriniyathi, A, Josana, Anto Edwin J, Joel, T, Akshayaa, M, Senthil Vel, Kumar, Vimal
Enhancing Range Prediction Accuracy for the BEV: Bridging the Gap between Simulation and Real-World Testing2026-26-05211/16/2026
Accurate range estimation in battery electric vehicles (BEVs) is essential for optimizing performance, energy efficiency, and customer expectations. This study investigates the discrepancies between physical test data and simulation predictions for the BEV model. A detailed range delta analysis identifies key contributors to the observed deviations, including regenerative braking inefficiencies, increased propulsion demand, auxiliary loads, and estimated drivetrain losses within the Electric Drive Module (EDM) during traction and regen. Results indicate that the test vehicle exhibits lower regenerative braking efficiency, higher traction forces and lower regen energy than predicted by simulations, primarily due to EDM inefficiencies and friction brake usage during regeneration. The study underscores the importance of refining simulation methodologies by integrating real-world, test based EDM loss maps to improve accuracy and better align predictive models with actual vehicle
Mahajan, Prasad, Kesarkar, Sidhesh, Ali, Shoaib
Effect of E20 Fuel on Vehicle Performance Parameters of 694CC Gasoline Engine for Commercial Vehicle Application2026-26-05651/16/2026
In alignment with its carbon reduction commitments, India is transitioning towards higher ethanol-blended fuels, with E20 set for nationwide implementation by 2025. Ethanol is a renewable, domestically produced biofuel produced through fermentation of biomass such as sugarcane, corn. It possesses a higher octane rating and oxygen content compared to conventional gasoline, making it a favorable additive for improving engine performance and reducing emissions. This study investigates the impact of E20 fuel on performance parameters of a 694 cc MPFI , water-cooled, twin-cylinder gasoline engine. For deriving maximum benefits of increased Octane rating of E20, compression ratio was increased to 12.5:1. Experimental analysis was conducted to assess the changes in combustion behavior, brake specific fuel consumption (BSFC), torque output, engine out emissions and thermal efficiency when operating on E20 compared to baseline gasoline (E10). Base results indicate that E20 promotes more
Kulkarni, Deepak, Malekar, Hemant A, Thonge, Ravindra, Kanchan, Shubham
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