Browse Topic: Energy management

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Research on Economy Mode Control Algorithm for Pure Electric Tractor with Powered Trailer2026-01-0425To be published on 04/07/2026
This research presents a comprehensive investigation into the electrification development pathway and energy-saving optimization methodologies for commercial tractor vehicles. Given the pivotal role that commercial vehicles play in modern logistics and transportation sectors, their propulsion systems are undergoing a fundamental transition from traditional internal combustion engines toward more sustainable hybrid and pure electric architectures. Simultaneously, the emerging trend of trailer electrification has gained considerable attention, where conventional trailers are retrofitted with motorized axles to form cooperative powertrain systems, demonstrating substantial energy-saving potential as confirmed by multiple research studies. Utilizing an integrated Trucksim and MATLAB/Simulink co-simulation platform, this study developed sophisticated vehicle dynamics models, detailed electric drive system models, and advanced battery models, which were subsequently integrated to establish a
tong, zi yu, Zheng, Hongyu, Li, Gang
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
Energy Management Strategy for Hybrid Mining Trucks Based on Global SOC Planning and Neural Network Optimal Control2026-01-0446To be published on 04/07/2026
Hybrid mining trucks, as core equipment for mine transportation, face high energy consumption and significant fluctuations in power demand during cyclic operations due to prolonged exposure to demanding operating conditions characterized by heavy loads and variable working conditions. To address the issues of high energy consumption and significant fluctuations in power demand during the cyclic operation of mining trucks, this paper proposes a hybrid mining truck energy management strategy based on global SOC (State of Charge) planning and neural network optimization control. First, a powertrain model was developed for a typical operating cycle of a hybrid mining truck, and its accuracy was validated by comparing it with experimental data. Using dynamic programming algorithms to plan the SOC for single-cycle operations provides a rational reference for energy allocation across different operational phases of mining trucks during a single cycle. Next, using the powerful nonlinear
Yang, Jianyu, Zhao, Zhiguo, Chen, Huiyong, Li, Tao, ZHUANG, Wenyu, Shen, Peihong, Tang, Peng
ENERGY AND OPERATIONAL EFFICIENCY OF SHARED AUTONOMOUS FLEET2026-01-0464To be published on 04/07/2026
Electrifying shared autonomous fleets (Robotaxis) presents challenges in balancing decarbonization, service quality, and operational costs, given the limited driving range, long charging times, and suboptimal planning of charging infrastructure. This study develops an integrated energy management and fleet dispatching simulation framework to support cost-effective, low-carbon Robotaxi deployment. The proposed system models both battery electric vehicles (BEV) and internal combustion engine vehicles (ICEV) technologies, and is extensible to other powertrain types. The study also integrates a life cycle assessment module to evaluate well-to-wheel carbon emissions. A total of 1,440 scenarios are designed to test the performance of two service modes (ride-hailing vs. ride-pooling) in terms of energy consumption, emissions, service quality, and operational costs, across varying levels of trip demand and market penetration of different powertrain technologies. The test aims to verify the
Tang, Kang, Abdulsattar, Harith, Yang, Hao, Wang, Jinghui
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
Modelling of Le Mans Daytona Hybrid Hypercar Powertrain System2026-01-0270To be published on 04/07/2026
This study presents the development, validation, and application of a full-vehicle simulation model of the BMW M Hybrid V8 LMDh Hypercar, designed to investigate and optimise hybrid energy recovery strategies in compliance with FIA and IMSA 2025 technical regulations. With increasing regulatory and sustainability demands in endurance racing, the deployment and recovery of electric energy using MGU-K have become pivotal for both performance and fuel efficiency. A physics-based simulation model was constructed in GT-SUITE, incorporating a map based internal combustion engine, transmission, and 50 kW rear-axle hybrid system. Complementary to this, a novel telemetry extraction method using Optical Character Recognition (OCR) in MATLAB and Python was developed to generate real-world drive cycles from on-board race footage. The model was validated using telemetry data. Subsequently, multiple energy deployment strategies were evaluated under varying race scenarios to identify optimal control
Buysse, Alexander, Samuel, Stephen
Integrated Simulation Framework for Multi-Objective Electric Vehicle Powertrain Design: Energy, Cost and Environmental Impact Optimization2026-01-0508To be published on 04/07/2026
Eco-design represents a key challenge in vehicle development, as increasingly stringent regulations and rising customer performance expectations require the optimal combination of environmental considerations and efficiency. This work presents three integrated simulation tools for electric vehicle (EV) powertrain design, targeting energy consumption, cost optimization, and environmental impact assessment. Three complementary models were developed and validated: a dynamic energy consumption model, a comprehensive cost estimator, and a life cycle carbon footprint assessment tool. Validation against real-world data demonstrated residuals consistent with existing literature, with prediction errors under 9% for energy efficiency and cost models. This integrated approach enables designers to account for the complete life-cycle impact of the vehicle being designed while optimizing component sizing for specific behavioral usage patterns, thereby avoiding costly oversizing. As an example of
Maréchal, Bérénice, Carlos Da Silva, Daniel, AHNINE, bouchra, El Ganaoui-Mourlan, Ouafae, Couillandeau, Matthieu, Miliani, El Hadj, PETIT, Quentin
A System-Level Calibration Framework for Embedded Vision: Integration of Sensor, Firmware, and Software Enhancements2026-01-0013To be published on 04/07/2026
Embedded vision systems are essential for contemporary applications, including robotics, advanced driver assistance systems (ADAS), and intelligent surveillance; yet they frequently experience diminished image quality due to resource constraints, environmental variability, and inconsistent illumination conditions. Such degradations impact multiple visual attributes-sharpness, contrast, color accuracy, noise levels, and structural similarity-that are critical for reliable perception in safety- and performance-driven domains. This study introduces a comprehensive system-level calibration architecture that integrates three coordinated layers: sensor-level adjustment, firmware optimization, and adaptive software enhancements. At the sensor level, exposure control, gain tuning, and white balance adjustments mitigate luminance imbalance and color shifts under changing light conditions. Firmware optimization leverages image signal processor (ISP) parameters to reduce temporal and spatial
Indrakanti, Rama Kiran Kumar, Vishnoi, Nitin, KAMADI, VENKATA
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
Evaluating the Impact of Drag Coefficient Changes on Driving Energy Using a Probability Distribution Model of Yaw Angle Based on Empirical Data from North American Highways2026-01-0615To be published on 04/07/2026
This study estimates the impact on driving energy of differences in aerodynamic characteristics for yaw angle from natural wind during North American Highway mode driving. A previous study[1] clarified the potential to estimate the fuel consumption impact of natural wind by integrating the CD yaw characteristics and yaw angle occurrence frequency. The natural wind was measured on a vehicle while driving a representative North American Highway test course[2]. The driving energy is predicted from the obtained yaw frequency and CD yaw sweep data in a wind tunnel. Measurements were conducted every weekday for 8 hours in 2023, covering 70% of the traffic volume. The validity of the measurement period was evaluated by the deviation from the annual average of wind direction and speed. Since yaw frequency varies depending on the road environment, it is necessary to weight the road environment type frequency when calculating the driving energy. The frequency was calculated using machine
Onishi, Yasuyuki, Nucera, Fortunato, Nichols, Larry, Metka, Matt
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
Look-ahead Information based Predictive Cruise Controller for Energy Efficient Powertrain Operation of Heavy-Duty Electric Trucks2026-01-0039To be published on 04/07/2026
Heavy-duty electric trucks represent a growing innovation in the transport and logistics sector, aiming to reduce emissions and reliance on fossil fuels. A major challenge with battery electric trucks is the long recharging time which takes significantly longer than refueling conventional diesel trucks. This limitation highlights the importance of optimizing powertrain operations to reduce energy losses and maximize efficiency. One effective approach is implementing optimal speed control through a predictive cruise controller. By anticipating road conditions, traffic, and elevation changes, the predictive cruise controller can adjust the truck’s speed in real time to minimize energy consumption, enhancing the range and reducing the need for frequent charging. Many problem formulations for electric trucks focus primarily on minimizing the energy required at the wheels, often overlooking the impact of powertrain efficiencies. This simplification neglects critical factors such as the
Safder, Ahmad Hussain, Villani, Manfredi, Khuntia, Satvik, Nelson, James, Meijer, Maarten, Ahmed, Qadeer
Radar-Based Dynamic Pixel Activation in Camera Sensors for Optimized Image Processing, Power Efficiency, and Thermal Management2026-01-0047To be published on 04/07/2026
This paper presents a radar-based dynamic imager pixel activation method designed to optimize image processing efficiency, power consumption, and thermal management in automotive In-cabin monitoring systems (DMS and OMS). By integrating radar sensing with high-resolution cameras, the proposed approach selectively activates image sensor pixels only within radar-identified regions of interest, such as moving occupants or key cabin areas. This selective pixel activation substantially reduces the computational workload and data bandwidth required, as the camera transmits high-resolution data solely for relevant zones while maintaining lower resolution or deactivating pixels elsewhere. The fusion of radar and camera data enables adaptive sensor control that caters to distinct scenarios, including driver gaze tracking and passenger monitoring, thereby enhancing DMS and OMS functionality. The architecture supports mounting configurations such as headliner or rearview mirror installations
Kasarla, Nagender Reddy, Patel, Krunal, Balaraman, Nirmal Kumar
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
Enhancing Vehicle DMU Processes Through Artificial Intelligence (AI) (Revolutionizing Packaging Design)2026-01-0102To be published on 04/07/2026
Abstract- Digital Mock-Up (DMU) is a vital process in the automotive industry, enabling teams to visualize, analyze, and optimize vehicle designs before production. Artificial Intelligence (AI) in the Digital Mock-Up (DMU) processes used in vehicle development. DMU is a virtual representation of a vehicle or component that allows for design, analysis, and simulation without the need for physical prototypes. By introducing artificial intelligence into vehicle DMU, manufacturers can leverage advanced analytics, automation, and predictive modelling to streamline workflows and enhance design accuracy. AI in digital mock-up helps optimizing space, resulting in smaller car design. This explores how AI-driven approaches are transforming DMU processes, improving efficiency, and shaping the future of automotive innovation. Key Benefits of AI in Vehicle DMU: - Enhanced Design Analysis: AI-enhanced design analysis enables teams to rapidly assess complex automotive models, detecting issues early
Nyati, Mohit Ashok, pathak cEng, Amit
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
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
Time-of- Flight (ToF) estimation using the Discrete Logarithmic Frequency (DLF) method2026-01-0019To be published on 04/07/2026
When incorporated into the small, curved, and enclosed spaces of contemporary automotive lighting systems, conventional measurement tools like photodetectors, optical sensors, and laser-based systems frequently encounter difficulties. Physical space restrictions, light attenuation along curved surfaces, and the high energy consumption of conventional sensors—particularly in intelligent headlight modules and advanced driver-assistance systems (ADAS)—are some of these limitations. To address these issues, this work presents a novel method for Time-of-Flight (ToF) estimation based on the Discrete Logarithmic Frequency (DLF) approach. The suggested method improves the accuracy and dependability of ToF-based measurements by examining the time-frequency correlations of modulated light signals sent through optical channels in automotive systems. Applications like adaptive beam shaping, vehicle-to-vehicle (V2V) optical communication, and in-lens diagnostics benefit greatly from this. The
Chinni, Venkata Sai Sandeep
Predictive Battery Preconditioning Strategy Considering Charging Time, Battery Degradation, and Energy Consumption2026-01-0126To be published on 04/07/2026
During fast charging, battery temperature plays a critical role in determining charging time, battery degradation, and auxiliary energy consumption. If the battery is not within the optimal temperature range at the start of charging, performance can deteriorate significantly. To enable optimal performance, the battery must be thermally preconditioned before arriving at the charging station. This work presents an optimal battery preconditioning strategy that considers charging time, battery degradation, and energy consumption. The approach utilizes route-based velocity prediction and optimizes the battery thermal management strategy on the way to the charging station using nonlinear programming. The optimization is based on precomputed maps that represent charging time, degradation, and energy consumption as functions of battery temperature and state of charge at the start of charging. These maps are generated through fast charging simulations using a validated high-fidelity simulation
Acker, Lukas, Hofmann, Peter, Konrad, Johannes
Energy Management Optimization of Sweepers Based on Working Conditions2026-01-0505To be published on 04/07/2026
Against the backdrop of the accelerating global transition to low-carbon development, the electrification of sweepers holds significant importance. Range-extended sweepers have emerged as a vital branch of new energy sanitation vehicles. Influenced by their service characteristics, these vehicles typically exhibit distinct features such as relatively fixed routes, concentrated operational areas, and highly regular working cycles. However, current range-extended sweepers employing conventional energy management strategies often demonstrate shortcomings: upon completing tasks such as sweeping and transportation, the battery State of Charge (SOC) frequently remains at an elevated level, leading to redundant energy waste. Meanwhile, the frequent start-stop cycles of the range extender during operations not only compromise the stability and service life of the power system but also reduce the overall utilization efficiency of battery energy. To address these issues, this study focuses on
Dong, Chenchen, Tan, Gangfeng, Wei, Haoting, Liu, Chongjian, Liu, Dan zhong
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
BEV incremental CO2 emissions in a ranked order electric grid2026-01-0424To be published on 04/07/2026
Battery Electric Vehicles (BEV) have been promoted and required by many governments towards the need to reduce transportations CO2 emissions. BEV are not “Zero Emissions Vehicles” because they run on grid electricity, but could be ‘effectively ZEV’ if the incremental CO2 is ‘very small’. To answer this question at the national level, three metrics are needed and estimated: (1) ICEV fuel consumption, approximated by the total US gasoline consumption divided by the total fleet miles driven, 25 mpg or 350 g CO2/mi. For strong HEV it is about one third less, 240 g CO2/mi. (2) BEV energy consumption, using data from systematic on-road testing of a wide range of vehicles, estimated at 40 kWh/100 mi for a US sales mix. (3) Electric grid marginal CO2, with ranked order sourcing: zero-CO2 sources are prioritized and supplemented by fossil sources. IEA hourly data show that the US 48 contiguous states are nearly self-contained, with zero-CO2 sources providing about a third of total demand. The
Phlips, Patrick
Development of the Honda S+ Shift Utilizing e:HEV System2026-01-0418To be published on 04/07/2026
Honda is promoting mobility electrification to realize a carbon-neutral society by 2050. Hybrid vehicles will remain advantageous over electric vehicles in terms of manufacturing cost and driving range until renewable energy usage increases, charging infrastructure is sufficiently developed, and battery costs are reduced. In response to this situation, Honda has developed a new control system, “Honda S+ Shift”, which further enhances the “emotional value of driving pleasure” inherent to the e:HEV system and creates new value for hybrid vehicles. Honda S+ Shift synchronizes the engine and vehicle speed and selects a virtual gear position according to the driver's operation such as acceleration, cornering, and deceleration. Subsequently, the system achieves the required system output in cooperation with a dedicated energy management system. It also works with each vehicle system, such as drive force control, sound control, and meter cluster, to stimulate all five senses of the driver
Murata, Naoya, Narimoto, Ryosuke, Saito, Masatoshi, Ishida, Daichi, GUNJI, HIROKI, UKAI, YOHEI, Kurachi, Shinobu, SHIKI, KAZUKI, Nagakura, Akari, Maeda, Sadaharu, Mitogawa, Terumasa
Research on MPC-based Control of Electric Active Anti-roll Bar2026-01-0632To be published on 04/07/2026
To enhance vehicle lateral stability and anti-roll performance, a model predictive controller (MPC) is designed for an electrically driven active anti-roll bar. First, a vehicle roll dynamics model and an active anti-roll bar model based on motor drive are established. Then, model predictive controllers for the front and rear suspension anti-roll bars are designed to calculate the anti-roll moments for the front and rear suspensions. Finally, co-simulation using Carsim and Simulink is conducted to simulate typical vehicle driving scenarios. The simulation results of passive suspension, PID and MPC control are compared to validate the improvement in vehicle stability achieved by MPC control, and the sensitive parameters of the controller affecting anti-roll performance are analyzed. The research results show that, compared to passive suspension and traditional PID control, the maximum roll angle with model predictive control is reduced by 80% and 40%, respectively. The MPC controller
Shen, Leiming
A Real-Time Vehicle Detection and Counting System Using a GUI Interface.2026-01-0156To be published on 04/07/2026
The growing need for intelligent traffic monitoring and environmental control has fuelled the importance of automated vehicle detection and counting systems. Fuel consumption estimation, vehicular emissions calculations, and traffic pattern comprehension- all play a vital role in shaping sustainable environmental policies. Therefore, accurate identification and counting of vehicles is essential. In this paper, we have shown the development of a real-time vehicle detection and counting application that uses computer vision and deep learning techniques to effectively detect, categorise, and count several types of vehicles on Indian roads. The computer vision vehicle counting application uses the YOLO (You Only Look Once) deep learning model for real-time identification and categorisation of diverse vehicle types, including cars, buses, trucks, and motorbikes [1]. A tracking module based on the SORT (Simple Online and Real-time Tracking) algorithm ensures unique identification of vehicles
Gusain, Ashna, Maurya, Akshay, PATHAK, SUNIL, Khan, Tuhin
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
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)
A Comprehensive Modelling tool for Heavy-Duty Hybrid Fuel Cell/Battery Powertrains2026-01-0430To be published on 04/07/2026
The global transition towards sustainable transportation is driving the development of efficient, low-emission propulsion systems. Battery-electric solutions are effective in urban contexts but face limitations in heavy-duty and long-haul applications due to the size and weight of the required energy storage. Hybrid battery/fuel cell powertrains offer a promising alternative for such use case, reducing vehicle mass and charging times while maintaining high energy efficiency. This study presents a zero-dimensional MATLAB/Simulink model of a parallel hybrid fuel cell/battery system for heavy-duty vehicles. The model encompasses the main vehicle subsystems, including the fuel cell stack with auxiliaries, the battery pack, the electric drive, the transmission and vehicle longitudinal dynamics, coordinated through a rule-based energy management strategy. Two representative vehicle configurations were analysed: a Group 2 urban delivery vehicle, and a Group 5 long-haul truck. Model validation
Montecchi, Gianluca, Martoccia, Lorenzo, D'Adamo, Alessandro
Assessing Energy Use, Cost, and Emissions of Small Regional Rail Vehicles: Methodology and Case Study on a German Track2026-01-0419To be published on 04/07/2026
The decarbonization of regional rail remains a pressing challenge in the European Union, where nearly half of the 200,000 km network remains unelectrified. Lightweight, single-car railbuses historically provided efficient service on such lines, but modern replacements are scarce. This work evaluates a standardized 30-ton, 16 m railbus platform for unelectrified regional service, focusing on propulsion system design and trade-offs between range, cost, and emissions. A MATLAB/Simulink drive-cycle model was developed to simulate energy consumption under realistic operating conditions. The Erfurt–Rennsteig route in Germany (130 km round trip, gradients up to 6 %) was selected as a representative case study. The model incorporates sub-models for traction motors, lithium-ion batteries (LFP and LTO), fuel storage, fuel cells, and ICE gensets across multiple fuel options (diesel, gasoline, methane, ethanol, methanol, HVO, FAME, and hydrogen). Battery lifetime is estimated using a combined
Ahrling, Christoffer, Tuner, Martin, Gainey, Brian, Torkiharchegani, Amir, Scharmach, Marcel, Hertel, Benedikt, ALAKÜLA, Mats
Energy-Aware Traffic Signal Optimization for Sustainable Urban Networks Using Valid Action Policies2026-01-0462To be published on 04/07/2026
Optimizing signal control across urban networks is crucial for reducing energy consumption and improving traffic efficiency. Advances in Vehicle-to-Everything (V2X) technologies have enabled deep reinforcement learning (DRL) to demonstrate significant promise in traffic signal control. However, many existing studies focus primarily on agent performance while neglecting practical operational constraints, which can lead to fairness issues in traffic control. Moreover, prior work often treats energy optimization as a by-product of efficiency. To address these gaps, this study proposes a multi-agent DRL framework to mitigate peak-hour congestion and reduce excessive emissions in urban networks. Building on this framework, a variant of Independent Proximal Policy Optimization (IPPO), termed Global State Independent PPO (GS-IPPO), is developed to enhance IPPO's coordination capability. To further improve practical applicability and fairness, this study incorporates constraints imposed by the
Qian, Wangrui, Jiang, Shuyan, Qi, Hao, Ou, Shiqi(Shawn)
Improving EV Energy Forecast Accuracy via ML Residual Correction: The Role of Wind Speed in Data-Driven Predictions2026-01-0150To be published on 04/07/2026
The increasing demand for electrified transportation is leading to accelerated development of highly efficient hybrid and battery electric vehicles. A major concern for customers adapting to battery electric vehicles (BEV) is range anxiety due to low charging speeds, charging infrastructure not matching expectations and unreliable range estimations shown to the customers by their vehicles. Estimating the range more accurately has been difficult due to the sensitivity of vehicle’s energy consumption to real-world environmental and driving conditions. This paper aims to find out the effect of true wind in the road load experienced by BEVs in the real-world driving scenarios and how using a highly accurate wind speed measurement improves the energy consumption estimation better. On-road tests were conducted on public roads and in controlled test-track environments to collect reliable wind speed measurements using a wind probe. Additional coastdown tests were also conducted to find more
RAGHUPATHY, Vishnu Prasaad, Kim, Shinhoon, Evans, Nic, Niimi, Keisuke, Mochihara, Takahiro
Adaptive Eco-Driving Feedback for Battery Electric Vehicles: A Reinforcement Learning Approach Using Context-Aware Policy Optimization2026-01-0165To be published on 04/07/2026
Energy efficiency and range optimization remain critical challenges to the widespread adoption of battery electric vehicles (BEVs). As a result, there is a growing demand for intelligent driver assistance systems that can extend the operating range and reduce range anxiety. This paper presents an adaptive eco-feedback and driver rating system based on proximal policy optimization (PPO) reinforcement learning, designed to support drivers with the target to reduce energy consumption and maximize driving range. The system processes real-time driving data, such as velocity, acceleration and powertrain status. Map data of high quality is used to anticipate traffic events, including but not limited to speed limits, curves, gradients, preceding vehicles and traffic lights. This contextual awareness allows the system to continuously assess driving behavior and provide personalized, context-aware visual feedback alongside a dynamic driving behavior rating. A PPO agent learns optimal feedback
Stocker, Christoph, Hirz, Mario, Martin, Michael, Kreis, Alexander, Stadler, Severin
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
Enabling Sustainable E-Mobility: An Edge–Cloud Collaborative Framework for Lifecycle Health Management of Electric Vehicle Batteries2026-01-0460To 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)
Simultaneous Multi-Material Part and Reinforcement Topology Optimization With Structural Performance Objectives2026-01-0174To be published on 04/07/2026
Within the automotive industry, the demand for lightweight yet structurally sound systems continues to increase, driven by environmental regulations and performance targets. Furthermore, as manufacturers continue to introduce new electric vehicle platforms, overall vehicle energy efficiency is increasingly achieved through weight reduction. In recent decades, topology optimization (TO) has emerged as a leading design tool for generating high-performance structures that satisfy lightweighting requirements through efficient material distribution within a defined design space. In structural design, TO can be leveraged to yield optimal configurations of both a base structure and an applied surface reinforcement material, allowing additional design freedom in the pursuit of stiff, lightweight design solutions. However, conventional TO frameworks are limited in how they couple these two design domains and are not well suited to optimize both simultaneously without compromising solution
Hronowsky, Benjamin, Kim, Il Yong
Development of Markov-Chain Prediction Model for Work-Stealing Scheduler in Dynamic Scheduling for Multicore Embedded Software Systems2026-01-0066To be published on 04/07/2026
Designing embedded software that achieves effective utilization of the fast-growing multicore embedded hardware should help to reduce their execution time and power consumption and improve their reliability. AI and machine learning algorithms are making their way into such rapidly enhanced multicore embedded hardware. We have developed a Markov-chain prediction model and integrated it into a work-stealing scheduler within a dynamic scheduling runtime layer (DSRL). Dynamic scheduling with a work-stealing scheduler was adapted from MIT’s Cilk framework. Dynamic scheduling allows independent computations to be spawned so they can be scheduled dynamically and executed in parallel on available cores. Cilk used a random model in its work-stealing scheduler where an idle core randomly selects other cores to steal computations from them. However, our Markov-chain-based scheduler allows idle cores to make informed decisions about which cores are better to steal their computation to increase
Sadeh, Waseem, Ganesan, Subramaniam, Qu, Guangzhi, Rawashdeh, Osamah
Methodology to Quantify the Petroleum Equivalent Fuel Economy Impact of BEV Thermal System Energy Consumption2026-01-0132To be published on 04/07/2026
This paper will present research and 1D modeling results to support a proposed methodology to determine a fleet ambient weighted average Petroleum Equivalent Fuel Economy (PEFE) for the energy consumed by the thermal system of a BEV, including a proposal of how factor in the thermal system PEFE result into an OEM’s overall CAFE determination on an incentivized (not punitive) basis, within the existing regulatory framework. The topic is particularly important because EPA and NHTSA final rules issued in early 2024 eliminated A/C Efficiency Credits for BEVs that are currently in place for all classes of light duty vehicles. However, without some sort of regulatory incentivization, market forces will dictate thermal comfort system solutions that consume more energy – and with that more negative secondary effects such as higher greenhouse gas emissions and vehicle mass - than would otherwise be the case. Eliminating incentivization for BEVs is particularly concerning because the thermal
Taylor, Dwayne
The Impact of Extreme Ambient Temperatures on Electric Vehicle Performance in the Kingdom of Saudi Arabia2026-01-0133To be published on 04/07/2026
The rapid expansion of Saudi Arabia’s electric vehicle sector driven by manufacturers such as CEER Lucid Tesla and BYD necessitates evaluating vehicle performance under extreme thermal conditions. This study investigates the impact of high ambient temperatures on electric vehicle operation by analyzing a 60 kWh NMC battery pack under representative Riyadh summer conditions of up to 47 °C using MATLAB Simulink simulations. The results show that elevated temperatures accelerate state of charge (SOC) depletion and increase battery internal resistance. To maintain safe operating limits the BTMS must operate continuously consuming up to 20 percent of the available energy. Cooling demand increases nonlinearly with temperature exceeding 40 kW at 50 °C which leads to significant parasitic losses and a 15 to 20 percent reduction in driving range. At the system level this increased energy demand raises charging frequency and intensifies electricity load during peak summer periods thereby
Bedywi, Lama Mohammed A, AlJuhani, Haneen, Duan, Chen, AbdulNour, Bashar
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
Advanced Damping Force Modeling Using Machine Learning for Next-Generation Electric Vehicle Suspensions2026-01-0586To be published on 04/07/2026
The rapid evolution of electric vehicles (EVs) has necessitated innovative approaches to optimize ride comfort, handling, and overall suspension performance. Unlike conventional internal combustion engine vehicles, EVs introduce unique challenges due to their distinct weight distribution, powertrain dynamics, and noise characteristics. This paper presents an advanced damping force modeling methodology leveraging machine learning (ML) techniques to enhance the suspension design process for next-generation EVs. The study employs data-driven ML algorithms, such as Gradient Boosting, Random Forest, and Neural Networks, to model the nonlinear and frequency-dependent behavior of dampers under various operational conditions. A comprehensive dataset, generated through simulation and experimental testing, captures the effects of road profiles, vehicle dynamics, and damping settings. The proposed ML-based approach predicts damping forces with high accuracy, outperforming traditional analytical
Hazra, Sandip, Tangadpalliwar, Sonali, Khan, Arkadip
Wind-tunnel investigation of snow/ice drag on a 30%-scale DrivAer model2026-01-0604To be published on 04/07/2026
Wind-tunnel tests were conducted using a 30%-scale DrivAer model, in estateback and notchback rear-geometry configurations, to investigate aerodynamic performance changes associated with snow and ice buildup on passenger vehicles. Around 20 snow/ice accumulation patterns were tested, at a Reynolds number of 2.8 million based on model wheelbase, for each of the notchback and estateback variants. 5 additional patterns were tested on the estateback with roof-rack support bars. Snow accumulation was modelled with foam, while ice accumulation was simulated with aluminum tape hand-formed to the desired shape. A simulated full-scale snow thickness of 58 mm on the hood, roof and trunk increased the wind-averaged drag coefficient by 16% for both model variants. With 90 mm of snow, the drag of the estateback variant increased by 19%. Drag changes increased with, but were not proportional to, snow thickness. Chamfered front and rear edges, representing windblown shapes, reduced the drag penalty
de Souza, Fenella, McAuliffe, Brian
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
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
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
Lateral stability and energy-saving integrated control of distributed drive vehicles with four wheel hub motors2026-01-0635To be published on 04/07/2026
Abstract: In order to improve the lateral stability and energy optimization of four-wheel hub motor distributed drive vehicles under complex road conditions, a layered control strategy for yaw stability is proposed. The upper controller is designed based on the sliding mode control principle to create a yaw moment controller, establishing both a two-degree-of-freedom model and a seven-degree-of-freedom model for the vehicle, tracking the desired yaw rate, desired center of mass side slip angle, actual yaw rate, and actual center of mass side slip angle to obtain the corresponding additional yaw moment. Moreover, the state of the vehicle is analyzed using the phase plane method of the center of mass side slip angle and yaw rate, and the total additional yaw moment for the vehicle is computed based on weighted calculations according to the state. Additionally, an unscented Kalman filter observer is established to enhance the tracking accuracy of the actual yaw rate and actual center of
shi, cheng'ao, liu, bingsen, Zou, Xiaojun, Wang, Tao, Zhang, Ming
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
Development of a virtual test rig to evaluate thermal management of a battery-electric vehicle under battery heating-cooling modes2026-01-0416To be published on 04/07/2026
A battery-electric vehicle (BEV) has multiple powertrain components (battery, inverter, e-motor), a thermal management system (compressor, heat exchanger, cabin heating, ventilation, and air-conditioning), and a vehicle body, among others. Vehicle testing is time-consuming, and also changing powertrain components during the testing and design process is costly. Simulation models (aka virtual or simulation test rig) have been widely used for efficient vehicle design. This work presents a systematic approach to developing a virtual test rig to evaluate the thermal performance of battery-electric vehicles. A Tesla Model Y is tested in a chassis dynamometer, and the measured vehicle performance data are used as boundary conditions for the complete vehicle model. The detailed lithium-ion battery (LIB) pack model, including its cooling system, was developed and calibrated using various transient driving cycle data. The HVAC model uses a simplified controller to maintain the cabin temperature
Sok, Ratnak, Kusaka, Jin
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