Browse Topic: Energy conservation

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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 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
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
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
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
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
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
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
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
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
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
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
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
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
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
Modelling of 2D Optical Sensor for Monitoring Structural Health in Automobiles2026-01-0206To be published on 04/07/2026
As modern space crafts and automobiles systems become more complex, advanced monitoring of systems are essential for maintaining their performance. A significant challenge is detecting mechanical problems early, such as engine knocking, worn bearings, or unbalanced wheels. If not addressed promptly, these issues can lead to costly repairs and diminished driving comfort. To reduce this, Fiber Optic Vibration Sensors have been developed as a solution to identify and analyze mechanical vibrations generated by a vehicle's components. These sensors, typically based on fiber optic technology, detect the variations in transmitted optical signals, allowing real-time monitoring of the vehicle's condition. By placing these sensors in key components such as the engine block, transmission, suspension, or wheels, unusual vibration patterns can be detected, enabling early diagnosis and preventive action. This analytical method not only helps in avoiding significant failures but also improves fuel
P, Geetha
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.
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
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
Development of Plug-in Hybrid Motorbike and Analysis towards Sustainable Urban Mobility2026-28-0111To be published on 02/12/2026
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 fueled 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 as a way 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
Kannan, Prashanth, Shaik, Amjad, Talluri, Srinivasa Rao
Enhanced Solar Desalination Using Natural Dolomite-Integrated PCM and MWCN Thermal Storage: An Experimental Study2026-28-0082To be published on 02/12/2026
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 particle were mixed with PCM at 1% of total volume of the PCM and a system that incorporates powdered natural dolomite into the PCM-based TESU. Each of the four configurations was created, tested simultaneously, and thoroughly examined. In comparison to the traditional system, the experimental findings showed that the adoption of PCM-based TESUs increased daily cumulative water output by 11.41%, 14.8% with addition of MWCNT and the addition of dolomite powder further increased productivity by 18.60%. Furthermore, the energy efficiency increased by16.1% with PCM, PCM with MWCNT is increased by 17.43% and 18.18% with the
R L, Krupakaran, Petla, Ratnakamala, Anchupogu, Praveen, P, Umamaheswarrao, Satya Meher, R, Dunna, Vijay
Design evolution of novel rear sub frame structure for multilink suspension and electric vehicle platform2026-28-0007To be published on 02/12/2026
As there is a major shift in customer demand for energy efficient transportation electric vehicle development has taken immediate significance worldwide as they provide pollution free, noise free mobility environment. In the current work, the authors evaluate novel Rear sub-frame assembly structure with multilink suspension and further for a born electric vehicle platform with increased rear axle weight contribution due to rear motor and battery weight. The Sub Frame is the important structural component of the chassis system. Hence designers face the challenge of providing a best-in-class optimized Rear sub-Frame in terms of cost and weight within the available vehicular boundary. Rear sub-Frame assembly is designed with unique cross member structure and novel motor mounting arrangement on the side member with the given complex hard points or attachments of the suspension linkages, to meet the electric vehicle packaging requirement. Sub frame is designed with light weight to withstand
Nidasosi, Basavraj Maruti, J, Ramkumar, Nayak, Bhargav, Mani, Arun, M, Sudhan
AI Smart Timer-Based Power Control and Energy Monitoring System for Home Appliances2026-28-0060To be published on 02/12/2026
This paper presents an intelligent home automation system that combines timer-based power control, real-time energy monitoring, and cloud-based AI analytics for individual appliances. The system is built using a Wi-Fi-enabled embedded controller connected to sensing circuits that measure voltage, current, and power consumption. Electromechanical relays are used to control appliance power based on user-defined timers. Users can configure either a fixed runtime or a scheduled cutoff, enabling automated energy-saving without manual input. Alongside local control, the system collects energy consumption data and transmits it to a cloud platform for storage and analysis. A dedicated mobile or web app provides live monitoring, device status, and remote control access. A major innovation is the integration of a cloud-based AI engine that processes historical and real-time data to offer predictive insights. The AI module forecasts energy use, detects anomalies, classifies appliances by behavior
D, Anitha, D, Suchitra, Jain, Utsav, Maity, Souvik, Dinda, Atish
Wheel Corner Module Energy Losses and Potential Improvements2026-26-00961/16/2026
With the inevitable shift of automotive industry towards E-mobility and mandatory fuel efficiency targets, there is a need to evaluate the energy losses in the vehicle & identify potential areas of improvement. Energy losses are calculated for different components in the corner module system of a passenger car. Contribution of losses (resistances) from respective component are depicted using simple analytical models. Potential energy saving improvements were identified and analyzed basis emerging technologies in respective areas.
Raghatate, Kumar Shreyas, Vedartham, Raghavendra, Khanger, Rakesh, Bisht, Arun
Combustion Noise Quality Improvement in a Common rail Diesel Engine at Low Idle using Taguchi DOE2026-26-03291/16/2026
Noise quality at idle condition is an important factor which influences customer comfort. Modern diesel engines with stringent emission norms together with fuel economy requirements pose challenges to noise control. Common rail engine technology has advantage of precise fuel delivery and combustion control which needs optimization to achieve the conflicting requirements of noise, emission and fuel efficiency. Engine noise at low idle condition is dominated by combustion noise which depends on rate of pressure rise inside the cylinder during combustion. The important parameters which influence cylinder pressure rise are fuel injection timing, pilot injection quantity and its separation, rail pressure and EGR valve position. The study on effect of these parameters at varying levels demand large no of experiments. Taguchi design of experiments is a statistical technique which can be used to optimize these parameters by significantly reducing no of experiments needed to achieve the desired
P, Priyadarshan, Chavan, Amit, A, Kannanswamy, Patil, Sandeep, Chaudhari, Vishal V
A Review on ‘Sensor Technologies’, its Application, Regulatory Standards & Emerging Trend Towards Safe Hydrogen Economy2026-26-01111/16/2026
In a developing country like India, the growing energy demand across all sectors underscores the urgent need for clean, sustainable, and efficient energy alternatives. Hydrogen stands out as a promising fuel, offering virtually zero emissions and helping to reduce greenhouse gas (GHG) emissions, which directly contributes to mitigating global warming, ensuring a cleaner environment, and lowering dependency on fossil fuels. In line with Sustainable Development Goal 7 (SDG 7), which seeks to guarantee that everyone has access to modern, cheap, and sustainable energy, hydrogen is well-positioned to be a major player in India's energy transformation. However, hydrogen has unique properties such as its wide flammability range, high reactivity, and high energy content present significant challenges in terms of safety, particularly in its storage, transportation, and usage. Improper handling or inadequate safety measures can lead to hazardous incidents, making robust testing, certification
Pawar, Yuvraj, Dekate, Ajay Dinkar, Thipse, S, Belavadi Venkataramaiah, Shamsundara
Evaluation of Intercooler Effectiveness with No Fan, Single Fan, and Dual Fan: A Simulation and Experimental Study2026-26-05791/16/2026
Turbochargers play a crucial role in modern engines by increasing power output and fuel efficiency through intake air compression, thereby improving volumetric efficiency by allowing more air mass into the combustion chamber. However, this process also raises the intake air temperature, which can reduce charge density, lead to detonation, and create emissions challenges—such as smoke limits in diesel engines and knock in gasoline spark-ignited (GSL) engines. To mitigate this, intercoolers are used to cool the compressed air. Due to packaging constraints, intercoolers are typically long and boxy, limiting their effectiveness, especially at low vehicle speeds where ram air flow is minimal. This study investigates the use of auxiliary fans to enhance intercooler performance. Two methodologies were adopted: 1D simulation using GT-Suite and experimental testing on a vehicle under different fan configurations—no fan, single fan, and dual fans (positioned near the intercooler inlet and outlet
Patra, Somnath, Hibare, Nikhil, Ganesan, Thanigaivel, Gharte, Jignesh Rajendra
Optimizing Busbar Design by Investigating Critical Parameters and their Influence on Electrical and Thermal Behaviors for Reliable Electric Vehicle Battery Packs2026-26-04741/16/2026
With the rise of EVs, researchers are focusing on optimizing busbar design to meet the demands of high energy density, fast charging, and compact battery packs. The busbar design starts by selecting the material and the cross-sectional area required based on the rated current requirement. The width matches or may exceed the battery cell terminal size, whereas the length is optimized such that it is packaged within the given space constraints. The research also highlights the risk of busbars to oxidation and corrosion, which increases resistance and decreases conductivity for which plating/coating techniques are applied to improve the surface finish, overall durability, conductivity and in some cases the surface hardness, while minimizing the heat loss. Using simulations and experimental validation, the study examines three key design parameters: the weld diameter for busbar welded joints, electrical resistance, and contact resistance. A detailed analysis investigates how the weld
Nogdhe, Yogesh, Singh, Shobit Kumar, Paul, Jibin, Mishra, Mukesh, Menon, Praveen
Effect of Flow Forming Process on the Mechanical Properties and Microstructure of GDC & LPDC Cast Aluminium Alloy Wheels2026-26-02981/16/2026
Aluminum alloy wheels have become the preferred choice over steel wheels due to their lightweight nature, enhanced aesthetics, and contribution to improved fuel efficiency. Traditionally, these wheels are manufactured using methods such as Gravity Die Casting (GDC) [1] or Low Pressure Die Casting (LPDC) [2]. As vehicle dynamics engineers continue to increase tire sizes to optimize handling performance, the corresponding increase in wheel rim size and weight poses a challenge for maintaining low unsprung mass, which is critical for ride quality. To address this, weight reduction has become a priority. Flow forming [3,4], an advanced wheel rim production technique, which offers a solution for reducing rim weight. This process employs high-pressure rollers to shape a metal disc into a wheel, specifically deforming the rim section while leaving the spoke and hub regions unaffected. By decreasing rim thickness, flow forming not only enhances strength and durability but also reduces overall
Singh, Ram Krishnan, Medaboyina, HarshaVardhan, G K, Balaji, Gopalan, Vijaysankar, Sundaram, Raghupathi, Paua, Ketan
Advanced Thermal Management for High-Tonnage EVs: Increasing Coolant Flow and Reducing Energy Losses2026-26-01961/16/2026
With the increasing tonnage of electric heavy commercial vehicles, there is a growing demand for higher power and torque-rated traction motors. As motor ratings increase, efficient cooling of the EV powertrain system becomes critical to maintaining optimal performance. Higher heat loads from traction motors and inverters pose significant challenges, necessitating an innovative cooling strategy to enhance system efficiency, sustainability, and reliability. Battery-electric heavy commercial vehicles face substantial cooling challenges due to the high-pressure drop characteristics of conventional traction system cooling architectures. These limitations restrict coolant flow through key powertrain components and the radiator, reducing heat dissipation efficiency and constraining the operating ambient temperature range. Inefficient cooling also leads to increased energy consumption, impacting the overall sustainability of electric mobility solutions. This paper presents a novel approach of
Dixit, Sameer, Patil, Bhushan, Ghosh, Sandeep
Numerical Study of a Hydrogen Ejector with Regulated Inlet for Extended PEMFC Operating Range in Automotive Application2026-26-02491/16/2026
Hydrogen recirculation is a primary requirement for improving fuel efficiency and anode stability in Proton Exchange Membrane Fuel Cell (PEMFC) systems, particularly in automotive applications. Effective hydrogen recirculation is critical for maintaining high efficiency and fuel utilization. A hydrogen recirculation ejector equipped with a regulated pressure inlet, which eliminating the need for mechanical pumps while maintaining optimal hydrogen utilization. The passive operation of the ejector eliminating the need for rotary components which significantly improves system reliability and reduces failure modes associated with moving parts. This work presents a numerical investigation of a hydrogen recirculation ejector featuring a regulated pressure inlet, with the objective of extending its operating range across varying fuel cell power levels. A combination of 1D system-level modelling and 2D multi-species Computational Fluid Dynamics (CFD) simulations was employed to evaluate
Khot, Ranjit Uttreshwar, T P, Muhammad, Chougule, Abhijeet, Achanur, Mallappa
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