Browse Topic: Vehicle charging

Items (1,183)
This paper presents Stochastic Gradient Pulse Adaptation (SGPA), a real-time adaptive pulse-charging system for rechargeable electrochemical batteries that dynamically adjusts charging aggressiveness based on the battery's internal response, as opposed to predetermined CC–CV or fixed pulse profiles. SGPA is different from traditional charging methods that use static current de-rating and conservative voltage limits. Instead, SGPA uses gradient-based feedback from terminal voltage behaviour, temperature changes, internal resistance changes, and state of charge to continuously adapt pulse amplitude and duty cycle. This algorithm boosts the charging intensity when the electrochemical circumstances are good. It lowers the pulses slowly when signs of thermal or impedance-related stress show up. Simulation-based proof-of-concept experiments on a heavy-duty multi-battery system show that charging time is less than with multi-CCCV charging, while still keeping the current distribution across packs balanced. The suggested SGPA method adds an adaptive charging algorithm that is easy to understand and ready to use. It makes fast charging more efficient without lowering voltage and thermal safety limits.
Prakashkumar, BalagopalMannar, Vignesh
This paper investigates the electromagnetic and circuit-level performance of an inductive power transfer (IPT) system for dynamic wireless charging of electric vehicles (EVs). Key design parameters affecting power transfer efficiency (PTE) are examined through a simplified Series–Series (SS) compensated IPT model using a Double-D coil geometry with shielded ferrite backing, developed in MATLAB. The framework evaluates the effects of air gap, lateral misalignment, load resistance, and operating frequency on overall system efficiency. Results show that PTE is highly sensitive to spatial alignment, with significant efficiency losses at air gaps greater than 10 cm and misalignments beyond 15 cm. A combined 3D surface plot confirms the compounded nonlinear influence of both parameters. Load resistance analysis identifies an optimal range of approximately 10–15 Ω, while frequency analysis indicates peak performance near 85 kHz, consistent with standard guidelines. These findings validate trends reported in previous literature and highlight the importance of early-stage IPT system evaluation for dynamic wireless charging applications.
Abdelrahman, MarwanSodre, Jose Ricardo
Current lithium-ion batteries should generally only be charged above 0 °C, as charging below this temperature can promote lithium plating and irreversible degradation. However, conventional pack-level heating elements increase system mass and design complexity. In addition, heat is transferred from outside into the cell, causing the temperature inside the cell to rise slowly. This study evaluates internal Joule heating of cylindrical Li-ion cells using a zero-mean square-wave current excitation and quantifies the associated aging impact. LG INR21700-M50L cells were tested at 0 °C, −10 °C, and −20 °C with three excitation frequencies (50 Hz, 1 Hz, 10 mHz) at 5 A amplitude. Each cycle consisted of 30 min heating followed by 60 min cooling; reference capacity-based state of health (SOH) was assessed every 50 cycles up to 400 cycles. A maximum surface temperature rise of 14.3 K was achieved, with larger temperature rise at lower ambient temperature and lower excitation frequency. Capacity fade remained below approximately 1% for most conditions; however, at −20 °C and 10 mHz a pronounced SOH decrease to 87% was observed, indicating a critical operating regime. The results provide practical guidance for pulse-heating parameter selection and highlight the need for safeguards and further diagnostics in extreme low-frequency excitation at very low temperatures. This heating approach is particularly suitable for simpler battery-electric applications without thermal management, such as e-bikes or power tools. However, it may also be relevant for applications with existing thermal management systems, as it simplifies battery pack design.
Raiber, StefanAllmendinger, FrankDegler, DavidParschau, Anke
The widespread adoption of electric vehicles is currently hindered by long charging durations and limited infrastructure. While fast-charging technologies address these issues, they impose significant thermal loads on high-voltage components. Within this architecture, the Battery Disconnect Unit plays a critical role as it monitors and controls the connection between the battery, powertrain, and charging system. However, the high currents required for fast-charging often drive these units' temperatures beyond safe operating limits, necessitating advanced thermal solutions that do not require extensive redesigns of the vehicle's electrical layout. To address this challenge, this study proposes a passive thermal management solution using Phase Change Material heat transfer devices to enhance the thermal robustness of the component. The methodology employs a dual approach involving initial experimental testing to pinpoint specific thermal hotspots under high-power conditions, followed by detailed numerical simulations using GT-Power software to predict system behavior. Furthermore, the paper provides a comparative analysis of various configurations, assessing their impact on temperature reduction, response time, and thermal uniformity. The results demonstrate that appropriately designed passive solutions significantly improve thermal performance, effectively enabling higher charging power capabilities while minimizing system complexity and integration effort. This innovation provides a scalable and efficient path for improving overall vehicle performance and safety during rapid energy transfer events.
Salameh, GeorgesGoumy, GuillaumeFrecinaux, AnthonyRatajczack, ChristellePalluel, MarlèneNoiseau, PascalLardeux, Sébastien
The rapid adoption of electric vehicles (EVs) with longer driving range demands high-power charging solutions that are efficient, scalable, and reliable. This work introduces a comprehensive simulation framework for megawatt-scale charging systems, focusing on the integration and control of multiple DC/DC converters. With the primary objective of maximizing overall system efficiency during megawatt-scale charging operations. A multi-agent adaptive control strategy is implemented to dynamically optimize operating points and allocate charging currents across converters in real time so that each participating converter operates at its optimal operating point where the maximum possible efficiency is delivered. This multi-agent adaptive control strategy allocates not only the individual optimal operating points of the multiple DC/DC converters but rather determines the optimal number of participating DC/DC converters at each time instance during the charging session. In addition to that, the strategy provides the option of delivering the optimal charging current during each time instance, so that maximized system efficiency is guaranteed during the charging process. Simulation results demonstrate that even a small efficiency improvement of 0.5% can yield substantial environmental benefits at a scale, where a 10 MW charging park avoids nearly 0.9 GWh of energy use and more than 350 t of CO₂ emissions over 10 years. By fully passing these efficiency gains to customers, charging becomes more affordable without compromising service provider margins, while the resulting climate benefits scale directly with utilization, installed capacity, electricity prices, and system lifetime. The proposed approach enables intelligent supervisory control for next-generation high-power charging stations, combining efficiency, cost-effectiveness, and sustainability. These findings support the development of modular, resource-efficient infrastructure for future EV ecosystems.
Salah, AliaAbu Mohareb, Omar
This SAE Surface Vehicle Technical Information Report, SAE J2836/4, establishes diagnostic use cases between plug-in electric vehicles (PEV) and the electric vehicle supply equipment (EVSE). As PEVs are deployed and include both plug-in hybrid electric (PHEV) and battery electric (BEV) vehicle variations, failures of the charging session between the EVSE and PEV may include diagnostics particular to the vehicle variations. This document describes the general information required for diagnostics and SAE J2847/4 will include the detail messages to provide accurate information to the customer and/or service personnel to identify the source of the issue and assist in resolution. Existing vehicle diagnostics can also be added and included during this charging session regarding issues that have occurred or are imminent to the EVSE or PEV, to assist in resolution of these items.
Hybrid - EV Committee
This SAE Information Report SAE J2836/6 establishes use cases for communication between plug-in electric vehicles and the EVSE for wireless energy transfer as specified in SAE J2954. It addresses the requirements for communications between the on-board charging system and the wireless EV supply equipment (WEVSE) in support of detection of the WEVSE, the charging process, and monitoring of the charging process. Since the communication to the charging infrastructure and the power grid for smart charging will also be communicated by the WEVSE to the EV over the wireless interface, these requirements are also covered. However, the processes and procedures are expected to be identical to those specified for V2G communications specified in SAE J2836/1. Where relevant, the specification notes interactions that may be required between the vehicle and vehicle operator, but does not formally specify them. Similarly, communications between the on-board charging sub-system and the on-board vehicle electronics is not formally specified in this document. This document will be published as a set of steps. The intent of step 1 was to record as much information on “what we think works” and publish. The intent of step 2 is to provide refinement and missing pieces to step 1, with a an eye to early testing. This version is step 2, with the aim of providing a communication protocol for home chargers.
Hybrid - EV Committee
The increasing electrification of vehicles means that heating, ventilation and air conditioning systems have a broader range of tasks and a different priority assessment. In electric cars, air conditioning systems are not only responsible for cooling the passenger compartment, but also for controlling the battery temperature, particularly during rapid charging, which represents a high-load operating point. Furthermore, achieving high thermodynamic efficiency is desirable, as this directly impacts the range of electric cars. The elimination of the combustion engine as a major source of noise prioritizes the noise, vibration and harshness behavior of the refrigerant compressor for product selection. To investigate the vibration and acoustic behavior, as well as the fluid dynamic forces resulting from the cyclic compression principle of an electric refrigerant compressor, a test rig was developed that allows compressors to be operated and measured in isolation in an anechoic chamber under various defined operating conditions. This test rig has been expanded in two ways within the scope of this work. Firstly, the compressor can be either rigidly attached to a dead mass using a VDA mount or measured while suspended freely. Secondly, a new R744-compatible refrigeration circuit has been added to the test rig, enabling compressors operating with the environmentally friendly refrigerant CO₂, which has so far only been used by a few manufacturers in selected models, to be tested. Measurement results obtained using this test rig provide valuable insight into the vibration behavior and sound spectra of the refrigerant compressor's fluid, structural, and airborne noise when operating at different points.
Beer, GabrielSaur, LukasSchwarz, ManuelZemsch, StefanBecker, Stefan
The deployment of high-power DC charging infrastructure for electric vehicles introduces new challenges in managing noise, particularly in public environments where acoustic comfort and regulatory compliance are essential. Noise emissions from both charging stations and vehicles during charging are a concern for operators of charging parks regarding customer experience and noise immission regulations. AVL employed a structured three-step approach to develop a non-expert tool for assessing the noise radiation of charging stations and vehicles during the charging phase. In a first step, AVL characterized the noise emissions with sound power measurements. Secondly, the measurement results were transferred to the virtual domain. To achieve this, the vehicles and charging station were characterized in the simulation with multiple monopole sources supported by transfer function measurements. This simulation model was validated against the sound power measurement results. After successful correlation of the simulation model, AVL implemented a user-friendly noise mapping tool for predictive planning of charging parks moving from a 3D FEM simulation approach to a simplified noise radiation model according to ISO 9613. Measurements were conducted on a representative high-power DC charging station and two electric vehicles. The vehicle cooling fan was identified as the dominant noise source on the vehicle side, while internal cooling fans of the charging station were the primary contributors to the noise radiated by the charger. Noise emissions were found to increase with higher charging currents, indicating a strong dependency on thermal load. The project established a reusable workflow for acoustic source identification enabling the creation of a scalable database. The noise mapping tool allows easy prediction of noise radiation from multiple charging stations and vehicles during the planning phase of charging parks and supports the design of countermeasures to meet regulatory requirements.
Gojo, JosefPolanz, MarkusGraf, BernhardLangjahr, PacoMehrgou, Mehdi
The EU funded innovation project High-Voltage fast-charging Efficient electric vehicle Powertrains (HiVEP) develops innovative technologies for mass-market electric vehicles (EVs) by advancing architectures operating above 800 V. These architectures integrate silicon carbide (SiC)-based power electronics, rare-earth-free electric machines with active winding reconfiguration, high C-rate batteries, and optimized thermal management systems. HiVEP aims to enable fast charging in less than ten minutes, reduce energy consumption by at least 25%, extend the driving range by 20%, and cut system costs by up to 20% in volume production. This article deals in detail with the project objectives, the methodological approach, and the expected key innovations, as well as the technical, environmental, and social impacts. The discussion situates HiVEP within the European research and innovation landscape, emphasizing its role in accelerating adoption of sustainable mobility solutions.
Schernus, ChristofNada, ShadyNeuhaus, ChristophEwald, JensSwierc, DanielKallur-Krishnamoorthy, RajeshVasiliadis, Harilaos
Vehicle fleet decarbonization is a key objective for the coming years, with electrification representing the primary pathway to achieving the targets set by the European Union. The share of battery electric trucks in new registrations has been gradually increasing especially in light and medium size trucks. The replacement rate of diesel long-haul trucks with zero emission trucks is still low due to challenges posed by added complexity and limitations of battery charging. Depot overnight charging is not sufficient to cover the energy needs of a truck covering large distances and careful planning of the route using public charging infrastructure is crucial for an optimized route minimizing extra costs and range anxiety. The current work aims to develop a methodology to propose the optimal charging locations for a given route of a battery electric truck based on nearby stations along the route. Our study uses an open-source optimization algorithm for the fixed route vehicle charging problem coupled with a powertrain simulation model that is used to calculate the energy consumption and the electric range of the vehicles. A variety of constraints, such as initial State of Charge, lowest allowed State of Charge threshold, maximum trip duration, distance deviation, have been implemented in different scenarios from real world locations with a goal to investigate the impact of planning constraints and charging infrastructure in the optimal planning of electric truck routing. The results of our analysis indicate that the integration of an accurate energy consumption calculation model to a route and charging optimisation algorithm can be proven beneficial for minimizing the time penalty due to charging.
Perdikopoulos, MichailDoulgeris, StylianosLivitsanos, GeorgiosKazakis, ThomasMellios, GiorgosNtziachristos, Leonidas
Decarbonization efforts achieved through electrification in nonroad mobile machinery can realize a reduction in fuel consumption of more than 20%, thanks to concepts familiar to light-duty passenger vehicles. This case study compares the results of a hybrid-electric material handler to its conventional counterpart, utilizing machine-specific drive cycles presented in part one of this paper series. The hybrid prototype features an extended-range electric vehicle (EREV) powertrain that demonstrated substantial energy efficiency improvements. Specifically, there was a reduction in equivalent fuel consumption of 75% when operating in electric-only mode, and 33% when maintaining the battery by charging with an on-board generator. Together, the efficiency improvements can be extrapolated over a low-intensity, 8-h shift characterized by significant idle time and highly dynamic engine load for a 47% reduction in net energy consumption. Key technologies that led to this improvement included engine downsizing and decoupling, regenerative braking, and an electrohydraulic pump unit with advanced controls. This study explains details of the powertrain architecture and subsystems that were implemented on a demonstration vehicle, control strategies used to meet project goals, and an analysis of energy consumption from testing on a closed course. Also included in this study is a discourse on comparison metrics that can be used for quantifying the energy consumption differences between hybrid-electric and conventional diesel powertrains in nonroad mobile machinery.
Czarnecki, AlexanderGoodenough, BryantWorm, JeremyRobinette, DarrellLaTendresse, PhilWestman, JohnSubert, DavidHeath, MatthewKiefer, DylanBlack, Andrew
As an emerging innovative mode of public transportation, electric modular buses (EMBs) offer a novel solution to the problems of existing public transportation systems, due to the coupling-decoupling processes. In this paper, we study the energy consumption characteristics of EMBs by joining vehicle-to-vehicle (V2V) charging and reduction in aerodynamic drag due to coupling. For the pursuit of energy economy, ride comfort, and operational efficiency, we constructed an optimization scheme based on the simulated annealing (SA) algorithm to facilitate the coupling-decoupling process. The simulation results show that EMBs can meet 82.5 % of service requests compared with 61.8 % for the benchmark group, and V2V presents a significant contribution to energy efficiency, especially at low battery state of charge (SOC). Additionally, sensitivity analysis is conducted to study the impact of initial SOC, operation interval, and route type. The results provide insights for optimizing EMBs’ operations and emphasize the potential role of EMBs in supporting low-carbon and sustainable urban mobility systems.
Liao, PengGuo, JiaheNing, DonghongLi, SijiaWang, Tao
To address the issues of battery overcharge damage caused by voltage imbalance and excessive grid-connected inrush current when high-rate charge-discharge energy storage batteries are connected to the DC side of cascaded energy storage converters, this paper proposes a three-stage pre-charging control strategy considering battery characteristics. This strategy achieves rapid charging and voltage balancing control of energy storage modules through the orderly connection of three stages: “uncontrolled rectification - sorting and voltage balancing - balancing maintenance”. In the first stage, an uncontrolled rectification method with series soft-start resistors is adopted to reduce the inrush current at power-on. In the second stage, based on the FPGA parallel full-comparison sorting algorithm, the DC-side voltage of each sub-module is quickly balanced by switching sub-modules. In the third stage, the number of fixed sub-modules to be cut off is maintained to continuously optimize the state of charge (SOC) balance of energy storage modules until the grid-connection conditions are met. To verify the effectiveness of the strategy, a three-phase 7-module star-connected cascaded energy storage simulation system is built on the Simulink platform. The simulation results show that the control strategy can stabilize the DC-side voltage of sub-modules to 44V and 53V sequentially within 1.2s, significantly attenuate the AC-side inrush current, and finally realize the non-impact grid-connection of the energy storage system. The research results provide technical support for the safe and stable operation of cascaded energy storage grid-connected systems, and improve the charging efficiency and reliability of the system.
Gu, CongWu, RuiZhou, WenCai, WenjieTian, YunxiangYang, Zhiqing
Currently, with the continuous development of electric vehicles, DC microgrids have attracted widespread attention due to their flexible access methods and high energy transmission efficiency. However, since the distributed secondary control of DC microgrids relies on information exchange through communication networks, false data injection (FDI) attacks on these networks may cause control algorithms to fail, leading to voltage deviations, output current imbalance, and in severe cases, system instability. This study focuses on DC microgrids based on parallel DC–DC buck converters and proposes a distributed secondary control strategy based on a sliding mode observer to address FDI attacks. By treating the system's FDI attack signals as an extended state, an extended sliding mode observer is designed to track the attack signals. Based on the observed attacks, a control algorithm is proposed that compensates the control inputs through the observer, ensuring proportional sharing of bus voltage and converter output currents. The stability of the system under the proposed control method is proven using the Lyapunov method and verified through MATLAB simulations. Simulation results show that the sliding mode observer (SMO) can quickly and accurately estimate FDI attack signals under various types of attacks, including periodic and step disturbances, and under load changes, while the system maintains stable bus voltage and current sharing. This research provides a potential technical approach to ensure the safe and stable operation of DC systems in future smart charging stations and grids with high renewable energy penetration.
Sun, WeiChen, JingYu, JinzhuYuan, WeiboPeng, BoLin, Fei
SAE TOMORROW TODAY - SAE Standards: Building Consensus for Moving Mobility Forward135634/16/2026
Standards aren't flashy ... but they make modern mobility possible by enabling emerging technologies to scale safely. Listen in as we sit down with SAE International experts Christian Thiele, Senior Director of Global Vehicle Ground Standards, and David Franks, Standards Specialist Engineer for Aerospace, for a wide‑ranging conversation on how SAE standards quietly enable trust, interoperability, and scale across automotive and aerospace. This discussion spans EV charging, wireless roads, automated driving, advanced air mobility, hydrogen propulsion, and the growing role of artificial intelligence. Go behind the scenes to learn how these standards are developed, the importance of industry consensus, and why they often exceed regulatory safety requirements. Are you interested in shaping the standards behind next-gen mobility technology? Get involved at sae.org/standards/development. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen--and give us a review on your preferred podcasting platform. Follow SAE on LinkedIn, Instagram, Facebook, X, and YouTube. Follow host Grayson Brulte on LinkedIn, X, and Instagram.
Patterson, Lori
Accurate prediction of electric vehicle charging time is critically hindered by dynamic, non-linear factors including battery aging which is indicated by the State of Health (SOH), substantial power diversion to thermal management systems in extreme temperatures, fluctuating user-defined accessory loads, and hardware limitations of the charging infrastructure. Traditional estimation methods, reliant on static models or predefined calibrations, fail to adapt to these real-world variables, leading to inaccurate predictions and user dissatisfaction. This paper presents a novel data-driven estimation framework utilizing a tailored feedforward neural network architecture specifically designed for this complex task. The model processes a sensitive set of inputs—including initial State of Charge (SOC), SOH, battery temperature, charging station power level and user-selected target SOC—to effectively capture the intricate, non-linear interdependencies governing the charging process. The network is trained offline using the Levenberg-Marquardt algorithm, which optimizes network complexity and mitigates overfitting, ensuring robust generalization without reliance on explicit electrochemical equations. A cornerstone of this invention is its continuous offline learning and update strategy; new field data from diverse charging scenarios is aggregated to periodically retrain and rigorously validate improved network parameters. These updated models are deployed seamlessly to vehicles via Flash-Over-The-Air updates, enabling the system to adapt to battery degradation and evolving usage patterns throughout the vehicle's lifespan. Validation under a wide range of conditions demonstrates a substantial increase in prediction accuracy compared to conventional model-based and calibration-based approaches. This solution, engineered for real-time deployment in vehicle control units, significantly enhances charging transparency, reliability, and overall user satisfaction by providing consistently accurate remaining charge time estimates.
Xie, ZhentaoShojaei, SinaWeslati, Feisel
Direct Current (DC) fast charging enables supply of megawatt (MW) scale DC power to the large battery systems of Heavy-Duty Electric Vehicles (HDEVs), such as electric trucks, buses, ferry and construction machinery. This contrasts with Alternating Current (AC) charging, which is limited by the capacity of the On-Board Charger (OBC) that converts AC to DC to charge the battery. In DC fast charging, however, the Electric Vehicle Supply Equipment (EVSE) delivers DC power directly to the HDEVs, bypassing the OBC. The feasibility of fast DC charging has been driven by advancements in semiconductor technology offering higher voltage and current handling capabilities as well as improvements in battery energy density. Ongoing research indicates continued growth in both semiconductor power handling and battery storage capacity, further strengthening the case for fast DC charging. Key benefits include significantly higher charging efficiency, drastically reduced charging times, and lower driver fatigue. However, unlike AC systems, DC based charging infrastructure presents unique protection challenges. These challenges arise from the absence of natural current zero-crossings of DC current and the limited commercial availability of pure DC breakers. This paper presents a concise review of existing protection technologies applicable to DC fast-charging infrastructure, identifying critical gaps in current approaches and evaluating potential solutions for Low Voltage (LV, <1.5 kV) and Medium Voltage (MV, 1.5–35 kV) DC applications. Then a downsized 10 kW prototype of an Ultra-Fast Active Resonance Current Source-Based Hybrid DC Circuit Breaker (UFRDCB) has been developed and experimentally validated as a proof of concept. The prototype successfully interrupts a 1 kA continuous DC current in less than 500 μs, and the corresponding test results are presented and discussed. Finally, the paper outlines a forward-looking roadmap for advancing protection technologies that are critical to the safe and reliable operation of megawatt-scale DC fast-charging infrastructure for HDEVs in the United States and globally.
Rahman, Md Rakib-UrDobrzynski, Daniel
Due to changed requirements compared to conventional propulsion concepts, electromobility demands new and innovative strategies for energy-efficient vehicle motion control. For example, the challenge in purely rear-wheel drive (RWD) electric vehicles (EVs) is to achieve a maximum of regenerative braking power in order to increase energy recovery and to ensure, that this does not impair the braking stability. Within this conflict between energy efficiency and braking dynamics, it is necessary to design an intelligent strategy to optimise recuperation. This paper presents such a strategy, which improves an existing approach formerly presented by the authors, but specifically optimised to overcome weaknesses. The previous approach had two major limitations: First, the efficiency map of the in-wheel machines (IWMs) was not considered. Second, there was no possibility of switching flexibly between different brake force distributions to guarantee both, maximized recovery potential and high braking stability, in fulfilment of legislative requirements. The new strategy addresses these shortcomings by introducing a speed-dependent torque limit for the electric drive motors to avoid inefficient operating and uses two independent factors to manipulate the brake force distribution along the axles and vary the distribution between the actuators. In addition, various scenarios were analysed and incorporated into the new strategy in order to achieve optimal torque distribution in every driving situation. The developed approach was implemented into a real vehicle and extensively tested in driving trials on closed-off terrain and on public roads. The results of the investigation demonstrate the ability to ensure stable vehicle control and a 45.3 % increase in energy recovery in comparison to the established benchmark.
Mitsching, ThomasHeydrich, MariusIvanov, Valentin
With the rapid advancement of electric vehicle (EV) fast charging technology, battery thermal management faces increasingly critical challenges due to elevated heat generation and stringent safety requirements. Conventional indirect cooling methods often struggle to provide sufficient heat removal under fast charging conditions, leading to potential safety risks. Immersion cooling has emerged as a promising solution because of its superior heat dissipation capability and uniform temperature distribution. In this study, an electrochemical-thermal coupled simulation framework is developed to evaluate indirect and immersion cooling performance under high-power charging conditions. A Pseudo-two-dimensional (P2D) electrochemical EV battery model is developed in GT-SUITE and validated against vehicle charging data. An immersion cooling system is also modeled and integrated into the battery framework to allow comparison with a conventional indirect cooling system under high-power DC fast charging scenarios. Simulation results indicate that immersion cooling achieves a maximum module temperature of 37.5 °C under 250 kW fast charge, which is 4 °C lower than the indirect cooling system. Furthermore, the immersion-cooled pouch cell battery pack can be charged from 10% to 80% SoC within 22 min, 11 min faster than using the indirect cooling system with a temperature limit of 42 °C. These findings demonstrate the potential of immersion cooling to enhance thermal safety, improve charging efficiency, and extend battery life in next-generation EVs.
Guo, YuyangRockstroh, TobyOezdag, ErdalHaenel, PatrickBodemann, BasilToghyani, Somayeh
Predictive Battery Preconditioning Strategy Considering Charging Time, Battery Degradation and Energy Consumption2026-01-01264/7/2026
Electric vehicles (EVs) play a key role in reducing greenhouse gas emissions, yet their widespread adoption remains limited due to long charging times and concerns about battery degradation. To address these challenges, this paper presents a predictive battery preconditioning strategy to optimally prepare the battery before fast charging, with the goal of minimizing either charging time, battery degradation, or energy consumption. The proposed approach employs route-based velocity prediction together with a longitudinal vehicle dynamics model to predict the battery load, ambient temperature, and arrival time at the charging station. Based on this predictive information, the optimal battery temperature trajectory is determined using nonlinear programming with precomputed maps derived from a high-fidelity vehicle model and an electrochemical battery model including physics-based degradation mechanisms. The optimized temperature trajectory is then realized through a nonlinear model predictive controller (NMPC) for the thermal management system. The control-oriented models used for optimization and control, as well as the high-fidelity vehicle model, are parameterized and validated using measurement data. Simulation results demonstrate that the predictive preconditioning strategy enables a reduction in charging time of up to 8.9% or a reduction in battery degradation of up to 6.2% compared to no preconditioning, while outperforming a rule-based preconditioning strategy. Furthermore, the results show that energy consumption cannot be reduced through active preconditioning. Overall, the findings highlight the potential of predictive battery preconditioning to improve charging performance and battery longevity in electric vehicles.
Acker, LukasHofmann, PeterKonrad, Johannes
This paper explores the application of an Improved Enhanced-Boost Quasi-Z-Source Inverter in AC-connected extreme fast charging (XFC) stations for electric vehicles (EVs), aiming to reduce conversion stages and enhance system efficiency. AC-connected XFCs offer superior reliability compared to DC-connected systems due to better fault tolerance and reduced sensitivity to power fluctuations but traditionally suffer from increased complexity and reduced efficiency due to multiple conversion stages. The proposed inverter addresses this by combining DC-DC and DC-AC conversion into a single stage, simplifying the system, decreasing losses, and improving efficiency. Furthermore, this research investigates the use of Spiking Neural Networks (SNNs) for generating the precise pulse width modulation (PWM) signals required for the Quasi-Z-Source Inverter. SNNs offer potential advantages in terms of dynamic response and adaptability compared to traditional PWM techniques, allowing for optimized inverter control under varying load conditions. By operating at a higher modulation index, the inverter reduces switch stress, making it suitable for high-power applications. Analytical results and simulations demonstrate the inverter's potential, enhanced by SNN-based pulse generation, in advancing next-generation fast charging solutions by simplifying infrastructure and improving charging efficiency, positioning it as a promising technology for the future of electric vehicle charging.
Saliesh, DileepSanaboyina, PrudhviChhagar, RohnitsinghSatyanarayan, Swapna
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 (2.53 miles/kWh) conditions, though aggressive driving showed the highest energy recovery. Regenerative energy recovery was most efficient in the 10 – 30 mph range, with the rear motor regenerating all the energy while the front motor used a small amount of power. Standard regen mode achieved 57% greater deceleration during coast down compared to Low Regen mode and showed a much lower variability during different simulated uphill and downhill conditions. Standard mode collected more energy than Low mode in all cases apart from simulated downhill tests where Low mode performed better. These results provide an overview of the Tesla Model 3 regenerative braking behavior and delineate operating regimes that maximize efficiency and quantify trade-offs between deceleration stability and energy recovery across driver-selectable modes. The results provide a rigorous, reproducible baseline and measurement protocol that can enable cross-vehicle benchmarking, validate vehicle/software-in-the-loop models, and inform future controller calibration and the design of on-road and track experiments
Pierce, Benjamin BranchDi Russo, MiriamDas, DebashisZhan, LuStutenberg, Kevin
Electric vehicles (EVs) are central to sustainable transport, yet battery service life remains a limiting factor for cost and adoption. Distinct from traditional laboratory-based simulations that often fail to capture the complexity of field conditions, this study investigates how EV user behavior—including driving style and charging demands—influences capacity using large-scale, real-world operational data from daily EV usage. A data-driven framework is developed to quantify driving and charging behaviors through multidimensional feature extraction at the vehicle level and estimate battery State-of-Health (SOH) trajectories, enabling direct linkage between individual behavior patterns and degradation outcomes. Results reveal substantial heterogeneity in aging rates explicitly driven by diverse user behaviors: under identical urban conditions, vehicles with a radical driving style exhibit approximately 81% faster SOH decline per 20,000 km than those with a moderate style; regarding charging intensity, in controlled comparison scenarios, increasing fast-charge counts from the baseline interval of 90–120 to the elevated interval of 150–180 is associated with a ~2.1% reduction in median SOH when holding other factors constant; and similarly, increasing deep charge–discharge events from 160–180 to 220–240 corresponds to an additional ~2.0–2.3% cumulative SOH loss. These findings quantify the behavioral determinants of capacity fade in the field and demonstrate that aggressive driving, frequent fast charging, and deep discharge habits materially accelerate battery degradation. The framework provides actionable evidence for adaptive charging guidance and personalized driving strategies, extending battery longevity and enhancing the sustainability of electric mobility systems.
Liu, TianyiJing, HaoZhu, JiankuanChen, YongjianOu, ShiqiQian, Xiaodong
This study presents a torque distribution control strategy for EVs with e4WD powertrain to overcome the trade-off between ensuring vehicle acceleration and deceleration responsiveness and mitigating backlash shock in the driving system. The deterioration of the drivability which occurs from the intrinsic hardware characteristics of the drivetrain is prevented by designing a response-priority drive mode in which neither front or rear motor torque is allowed to change its sign. Instead, in such drive mode, the front motor torque is only allowed to perform regenerative braking while the rear motor torque is only allowed to produce positive acceleration torque. In order to avoid sacrificing the maximum acceleration by applying such strategy, the mode transition function is implemented as well. In addition, in order to prevent backlash impact due to drivetrain compliance, variable offset torque based on drivetrain compliance model is evaluated in real time and applied to each motor command generation strategy. The enhancement of vehicle drivetrain responsiveness directly leads to improved track driving performance, particularly for the neutral-balance phase during harsh cornering. The effectiveness of the suggested driveline torque distribution method is verified using an actual vehicle driven on the race track, and the vehicle responsiveness followed by track driving performance indices are numerically assessed for comparison.
Oh, JIWONLee, Ho Wook
Electrification is rapidly entering all vehicle classes, including light- and heavy-duty trucks designed for heavy towing capabilities. Still, the quantitative impact of towing on battery-electric vehicle (BEV) energy use and range remains under-characterized. We conducted controlled towing tests with a Ford F-150 Lightning using two trailers of different sizes and varying payloads to isolate aerodynamic and mass effects and to span the full range of towable payloads within the vehicle’s rated capacity. The vehicle was instrumented at the CAN bus level, capturing motor power, torque, speed, and related internal signals from different control modules. On-road testing consisted of repeated back-and-forth passes on level, straight road segments at set speeds focusing on highway operation, where aerodynamic drag is stronger and real-world towing use cases occur. From these data, we extracted road load equations and dynamometer coefficients for each trailer combination, then reproduced equivalent conditions on a four-wheel drive chassis dynamometer across several standard cycles. Results were consistent across runs, showing a significant increase in the vehicle’s overall energy consumption and a corresponding range penalty. Additional impacts on vehicle systems due to towing, including thermal management of the motors and battery, were quantified. Dynamometer tests of varying characteristics (highway, urban, steady state speeds and accelerations) allow isolation of specific behaviors in functions like regenerative braking operation and torque-split strategy. Dynamometer results aligned with on-road measurements, enabling repeatable laboratory evaluation of towing scenarios. These findings provide a validated methodology and dataset to quantify towing impacts on BEVs, inform range prediction and route planning, support labeling and consumer guidance, and characterize sustained, high load real world operation of vehicle components.
Timermans Ladero, Inigo
Developing efficient fast-charging infrastructure along highway corridors is critical for reducing range anxiety and promoting long-distance electric travel. However, traditional static location approaches often fail to account for the stochastic interactions between continuous traffic flows and the stochastic variability of remaining driving ranges. To address these methodological gaps, this study develops a demand-driven optimization framework that integrates an improved Genetic Algorithm with the flow-capturing location-allocation model (GA-FCLM). Unlike static facility location approaches, the flow-capturing location-allocation component is specifically selected to maximize the interception of continuous traffic flows under strict range constraints, while the genetic algorithm efficiently navigates the high-dimensional discrete search space of simultaneous siting and sizing decisions. By synthesizing segment-level traffic flows with Monte Carlo simulations of state of charge (SOC) trajectories, the model accurately reconstructs corridor-level charging demand. For the Beijing-Hong Kong-Macao Expressway, the optimization identifies a robust layout with twenty active stations and 204 fast chargers, requiring a capital investment of 32 million Chinese Yuan (CNY). This configuration achieves a 99% aggregate coverage ratio, effectively eliminating long uncovered segments. Sensitivity analysis reveals that while profit increases linearly with pricing, infrastructure capacity exhibits a nonlinear response to rising electric vehicle (EV) penetration, necessitating strategic spatial rebalancing. The proposed GA-FCLM framework thus provides a scalable and methodologically superior tool for balancing investment costs, coverage continuity, and spatial equity on national highway networks.
Guo, HaifengZhang, JingzhongLian, Jintao
Improving the energy efficiency of electrified vehicles remains a central objective in modern electric powertrains. Multi-level converters (MLCs) are widely recognised for lowering conversion losses relative to two-level inverters and improving total harmonic distortion (THD) in the sinusoidal supply to motors with a consequent reduction in motor losses. Despite this, sustained production-oriented validation at the integrated system level remains limited. This work introduces a multi-level converter architecture of the Battery Integrated Modular Multi-Level Converter (BIMMC) topology using Cascaded H-Bridge (CHB) architecture. It offers improvements in all key metrics of performance, cost, package size, mass and robustness compared to the current state-of-the-art two-level inverter system with distributed functions for charging available in the market today. The overall solution is highly functionally integrated. It supports four major functions required in electric vehicles without the need for additional hardware. Firstly, supply to and control of a three-phase electric motor without the need for a separate, standalone inverter. Secondly, all usual battery management system (BMS) functionality including energy and State of Charge (SOC) management to module level enabling usable energy and robustness improvements. Thirdly, the ability to charge from both alternating current (AC) (single phase and three-phase) and direct current (DC) sources without the need for separate on-board charger (OBC) hardware whilst also enabling an innovative pulse charging approach which benefits both charging time and battery ageing compared to conventional DC charging. Finally, the ability to deliver a controlled DC supply to non-traction loads on the vehicle with high efficiency and redundancy. The BIMMC topology proposed has been designed, built at prototype level and tested in order to collect performance data to empirically validate empirical study of the performance and functional benefits of the approach for traction motor drive, battery stored energy management and charging. Measured results demonstrate that improved inverter waveform quality correlates with lower motor harmonic losses and measurable drive-cycle efficiency gains, consistent with prior MLC assessments. Battery SOC depletion can be managed actively within the complete battery yielding increased usable energy and further driving range gains. Pulse charging shortens charge time whilst maintaining battery health metrics within acceptable limits, aligning with experimental evidence on pulse-based fast charging. The topology has also demonstrated the ability to pulse charge cells in a complete battery pack whilst consuming incoming DC supply current from a standard commercially available DC charger (Electric Vehicle Supply Equipment - EVSE). This potential to offer the benefits associated with pulse charging without requiring change to existing deployed charging infrastructure. Overall, proposed CHB-BIMMC architecture offers a practical blueprint for next-generation electric vehicles (EVs), and is compatible with ongoing integration trends that converge traction, charging and battery management functions within a unified power electronics and control platform.
Bao, RanKalaiselvan, PrashanthRener, KristofHallam, PhilipShi PhD, KaiYue, WilliamMa, HeGrimshaw, AndrewPatel, Simon
This paper presents research and digital twin modeling results to support work on a methodology to properly account for the energy consumed by the thermal system of a BEV, for use within both existing Petroleum-Equivalent Fuel Economy (PEFE) calculations, and the proposed addition of hot and cold weather range values to the consumer-facing Monroney label [1]. Properly accounting for thermal system impacts would incentivize minimizing energy consumption of these systems, since 1) BEV PEFE is a direct input to an OEMs overall CAFE performance, and 2) the values on the Monroney label has some impact on consumer vehicle choice. The impetus for this work was Final Rules issued by the EPA and NHTSA in early 2024 eliminating A/C Efficiency Credits for BEVs from the 2027 MY, thus eliminating regulatory incentives to minimize energy consumption of these systems. Higher energy consumption will produce a number of negative secondary effects, including higher real-world greenhouse gas emissions, reduced vehicle range, greater strain on the nation’s electrical grid, and higher vehicle mass leading to reduced vehicle safety - should OEMs opt to merely install larger batteries to address cold and hot weather range impacts instead of implementing lower energy-consuming technology. The results from the analysis, which ideally would be confirmed with follow-up vehicle tests, show that for a baseline, PTC-heat based system, thermal system energy consumption represents 19.2% of the total energy consumed by a BEV on an annual basis, using an ambient-VMT weighted approach. It seems to be the technical equivalent of “straining at a gnat while swallowing a camel” to focus so much time and energy on identifying incremental improvements in energy consumption from the propulsion-portion of a BEV, while by comparison ignoring the system that according to this analysis can account for nearly 20% of the total on an annual basis.
Taylor, Dwayne
Accurate modeling of battery temperature rise during fast charging is challenging due to uncertainty around cell heat generation and the thermal characteristics of the materials and interfaces which make up the battery pack. High fidelity thermal models are critical to attaining the best battery pack design, since they enable a multitude of cooling and packaging approaches to be considered prior to building a prototype. In this study, a 3D finite element analysis (FEA) thermal model of a production fast charging battery module is created. A loss model is parameterized as input for the FEA model. A key part of the loss model is the entropic heating coefficient (EHC), which is the change of open circuit voltage with respect to temperature. The EHC is measured by waiting for the cell voltage to reach steady state at various temperatures in 5% and 10% state of charge intervals over its capacity. This is then corrected numerically by accounting for unwanted discharge or rebounds. The EHC is used to calculate reversible loss, and irreversible loss is calculated using terminal voltage measured from the cell. Thermal parameters of the pouch cell are estimated through experimental thermal gradients and comparisons to similar cells. An FEA model of the module, which utilizes edge cooling, is created based on physical measurements. The combined loss and FEA model was found to estimate peak temperature with an error of 3 °C or less for 0.5, 1, and 1.5 C charge rates and 8 °C for a multistep fast charge.
Thornton, JackKollmeyer, PhillipPanchal, SatyamGross, Oliver
Towing imposes substantial efficiency penalties on both battery-electric vehicles (BEVs) and internal combustion engine (ICE) vehicles, reducing range by 30-50%. This paper presents a proof-of-concept embedded control architecture for distributed trailer propulsion that actively regulates drawbar force to reduce towing loads. Unlike proprietary e-trailer systems requiring specialized hardware, the proposed implementation demonstrates feasibility using commercial off-the-shelf (COTS) components and open-source software. The distributed architecture employs dual Raspberry Pi 4B single-board computers communicating via ROS 2 at 20 Hz. The trailer-mounted controller executes a Simulink-generated control node coordinating load cell acquisition (HX711 ADC), motor CAN bus telemetry, and throttle commands to a 5 kW BLDC traction motor powered by a 5 kWh LiFePO4 battery pack. A vehicle-mounted controller logs OBD-II/CAN validation data. The control pipeline implements cascaded EWMA/Hampel digital filtering with intentional phase lag for hitch-force regulation. The system was validated through on-road testing with an ICE towing vehicle pulling a 1,000-lb trailer over standardized 2.1 km segments following SAE J1321 Type II procedures. Preliminary trials demonstrated stable control performance with drawbar force regulation with no oscillatory behavior. Fuel consumption measurements showed promising improvements (9.4% lower fuel consumption in assisted vs. baseline conditions), though limited sample size precludes definitive causal claims. The primary contribution is establishing technical feasibility of cost-effective COTS implementation (USD 5,000 hardware cost) for trailer propulsion control, providing a foundation for expanded validation studies and commercial deployment pathways.
Joshi, GauravAdelman, IanLiu, JunDonnaway, Ruthie
Battery modules operate under diverse and complex conditions, such as driving cycles and fast charging. In these scenarios, effective thermal management is critical to ensuring safety and extending the battery's lifespan. Fast-charging scenarios present a particular challenge due to the complex current control strategies that strongly influence cell temperature distribution, making thermal uniformity a key concern. Existing studies focus more on drive cycles, but not sufficient for fast charging. This study presents a coupled electrochemical-thermal simulation framework based on the DCIR (Direct Current Internal Resistance) model to examine heat generation and temperature responses during fast charging. The model incorporates heat conduction pathways and the structural layout of the module, enabling the evaluation of thermal mismatch risks and the optimization of module design and thermal management strategies. The findings offer practical insights for battery thermal management and the development of advanced control strategies.
Xiao, FangzhiChen, GuijieMa, ShihuHu, XiaoSong, ShujunWakale, Anil Bhaurao
Plug-in Hybrid Electric vehicles (PHEVs) have the capability to effectively utilize electricity from the grid as an energy source for powering an appreciable portion of the total vehicle miles travelled (VMT), thereby reducing greenhouse gas (GHG) emissions, since the Carbon Intensity (CI) of electricity is often less than that of liquid fuels in many parts of the world. Several real-world usage factors can affect the fraction of VMT electrified, with the frequency of charging being one of the most influential factors. Studies in recent years have attempted to characterize the real-world performance of PHEVs based on long-term average fuel consumption and/or other data flags in the readout from vehicle On-Board Diagnostics (OBD), but such approaches are unable to infer accurate estimates for the occurrence of charging events. This paper adopts an approach that relies on analysis of highly granular (trip by trip) information obtained from vehicles equipped with a data communication module (DCM) to infer the occurrence of charging events from change in the battery state of charge (SoC) between trips. Analysis of data obtained from a large sample of PHEVs (one full calendar year for hundreds of vehicles) in the US and Canada reveals three distinct patterns: i) vehicles that are consistently charged, ii) vehicles that are consistently not charged, and iii) vehicles with temporally varying frequency of charging. Unlike some other studies about PHEVs in other parts of the world, results of our sample for PHEVs in North America show that the majority are consistently charged, but with various frequency levels that are regionally dependent.
Hamza, KarimLaberteaux, Kenneth
Driven by the dual-carbon goals of “peak carbon emissions” and “carbon neutrality,” improving energy efficiency in electric construction machinery has become a key focus. This study proposes an energy-saving torque control strategy for the traction motor of electric wheel loaders, aiming to reduce drive system energy consumption. The innovation lies in coupling parameter optimization of the pedal–torque mapping and regenerative braking to enhance overall efficiency. An electric model was built using Cruise and validated against real-world V-cycle test data, showing good agreement with an average relative error of 4.08%. Based on the model, two optimized control strategies were developed and evaluated through simulations and field tests. The results showed energy savings of 7.08% and 16.18% in simulation, and 6.83% and 15.51% in tests, respectively, demonstrating the effectiveness and practical value of the proposed method.
Ming, QiaohongWang, YangyangWang, Feng
Currently, a persistent concern arises regarding the management of retired Li-ion batteries from electric vehicles (EVs). A potential solution is to repurpose these batteries for less demanding applications, such as energy storage systems. Such repurposed batteries are commonly referred to as second-life batteries (SLBs). In this work, we explore the economic feasibility of implementing SLBs in Stanford University’s EV bus charging station via previously developed technoeconomic decision support model. The model simulates battery aging behaviors across various usage conditions, optimizing the operational parameters of SLBs. The estimated lifetime is expected to be 10 years in an optimal using condition. In addition, an economic sensitivity analysis explores the influences of various factors. Furthermore, we calculate the cost savings of total $82,500 over its second lifetime, which is derived from the adoption of SLB instead of new batteries.
Zhuang, JihanChueh, WilliamOnori, SimonaBenson, Sally M.
The integration of electric vehicle charging station (EVCS) and renewable distribution generation (RDG) in the grid affects the grid voltage, power losses, and system instability in the distribution system, therefore the article presents an approach for optimal placement and sizing of EVCS and RDG using an optimization approach named as modified particle swarm optimization (MOPSO) in radial distribution network (RDN). The efficacy of the optimization approach is demonstrated under both balanced and unbalanced dynamic load conditions in the IEEE 33-bus system. The influence of EVs and RDG on the RDN is analyzed by considering the maximum possible cases, e.g., 13 different scenarios, which replicate real-world scenarios. These results are validated using DIgSILENT Power Factory Software. The proposed research also covers Techno-Economic Assessment using HOMER software, which may enhance visibility of the renewable distribution generation importance in the current scenario.
Kumar, SonuAgarwal, Ruchi
Lithium-ion batteries suffer from capacity degradation, lifespan attenuation, and power decline at low temperatures. Alternating-pulsed-current (APC) heating method is an effective solution for improving the low-temperature performance of batteries, but it still faces challenges in terms of low heating efficiency and energy consumption. This work proposes a pulsed-charging-current (PCC) heating method to address these issues. The effect of the PCC under various conditions, including frequency and amplitude, is investigated through experiments. According to the experimental results, the battery can be heated from -20 °C to above 7.5 °C within 15 minutes using the proposed PCC method, with a heating rate of 1.83 °C/min. Compared with the traditional APC heating method, the heating rate of the PCC method increases by 7.9%. During the 15-minute heating process, the battery capacity increased by 131.9 mAh on average, and the charging efficiency can be achieved 95% above. The proposed method provides an effective solution for the low-temperature, low state of charge (SOC) application scenario in electric vehicles.
Xiao, YuechanHuang, XinrongWu, ZeZhang, YipuMeng, Jinhao
This study systematically investigates methods to enhance the fast-charging capability of lithium-ion batteries through advanced simulation. The electrochemical reaction mechanism, heat generation mechanism, and lithium plating mechanism are analyzed in detail, and an electrochemical–thermal coupled model incorporating a lithium plating sub-model is established. A hybrid parameter identification strategy, combining random search, grid search, and manual adjustment, is employed to calibrate the model across different operating conditions, thereby improving its accuracy in reproducing real battery behavior. Lithium plating is selected as the primary indicator to evaluate fast-charging performance. Based on simulation results, the effects of both operational parameters and structural parameters on lithium plating are thoroughly analyzed. The results indicate that lower charging rates, elevated charging temperatures, higher electrode porosity, and reduced tortuosity are favorable for suppressing lithium plating. These conditions improve the uniformity of lithium deposition while alleviating concentration gradients of lithium ions, thus offering valuable insights for battery material design and practical applications. Furthermore, optimized charging protocols are developed on the basis of conventional strategies and their associated impacts on battery behavior. Two novel approaches—the group-based optimized charging protocol and the adaptive optimization-based charging protocol—are proposed by dynamically adjusting the charging rate according to real-time electrochemical states. Validation on the developed electrochemical–thermal model confirms that the proposed protocols can achieve high-rate charging without inducing lithium plating. As a result, charging time is significantly reduced while ensuring safety and reliability. Overall, this research not only provides a comprehensive methodology for modeling and parameter identification but also offers practical strategies for protocol optimization. With solid-state batteries regarded as a promising future technology, the present work provides a potential basis for their advancement.
Zhao, PeiqiangZhan, WenweiQi, JiYi, Yong
Lithium-ion batteries represent a complex and nonlinear voltage behaviour on various time scales. Battery models are needed to analyze and estimate the battery behaviour and determine their suitability for practical applications. Battery model simulations in previous studies were mainly based on pulse charge and discharge cases. The current amplitude used in the test cases was limited, and the temperature factor of the battery model was neglected. The simulation conditions above were significantly different from those in practical applications. In this paper, an equivalent circuit model considering the temperature factor is developed to simulate the practical applications of lithium-ion batteries. Experimental tests for parameterization are applied to the commercially available 189 Ah lithium iron phosphate battery cells under a wide range of experimental conditions. The parameters are obtained through experimental tests and are used to build the equivalent circuit model of the battery. The parameterized model is modified to fix the voltage error in both low and high state-of-charge levels before model verification. Constant-current charge test and step charge test are both applied to the proposed model and the comparative model without temperature factor to verify and compare the model accuracy. Simulation results are compared against laboratory experimental results under the same conditions. For most of the state-of-charge levels, the voltage error of the proposed model lies below 0.5% for the constant-current charge test and 1% for the step charge test. Compared with the model without the temperature factor, the proposed model demonstrates the average reductions in RMSE and MAE of 49.89% and 47.74% under constant-current charge test, and 59.72% and 71.04% under step charge test, respectively. Increased accuracy is obtained for practical applications compared with previous studies.
Chang, AnWang, ShengweiZhou, Kai
Accurate estimation of the state of health (SOH) of lithium-ion batteries is essential for ensuring the safety, reliability, and performance optimization of electric vehicles. In practical operating environments, however, data quality is often compromised by noise interference, frequent fluctuations in load conditions, and the inherently non-stationary nature of battery degradation features. These challenges reduce the effectiveness of conventional modeling approaches, which often struggle to maintain both high prediction accuracy and strong generalization capability. To address these issues, this study develops a comprehensive SOH estimation approach encompassing data quality enhancement, degradation feature extraction, and hybrid deep learning-based modeling. In the first stage, multi-stage anomaly detection techniques are applied to remove noisy or inconsistent measurements. A week-based indexing strategy is introduced to generate temporally coherent labels, ensuring that time-series dependencies are preserved. This procedure ensures a minimum per-vehicle valid-data ratio of 87.59%, thereby guaranteeing consistent data availability across all vehicles and significantly improving both reliability and temporal alignment. In the second stage, a set of degradation-sensitive health indicators is extracted from raw sensor measurements, including voltage, current, and temperature profiles. These features are then aggregated at a weekly resolution to enhance stability and reduce short-term variability. In the third stage, a hybrid deep learning model is constructed by combining a Temporal Convolutional Network (TCN) for local pattern extraction, a Bidirectional Gated Recurrent Unit (BiGRU) for long-term dependency modeling, and an attention mechanism for adaptive feature weighting. Experimental results under 10-fold cross-validation show that the proposed approach achieves a root mean square error of 1.30% and a mean absolute error of 1.03% on the test set, outperforming single-model baselines in both accuracy and robustness. The proposed framework offers a practical and scalable solution for high-precision SOH estimation in real-world scenarios and provides a strong basis for deployment under diverse and extreme operating conditions.
Wang, SijingJiao, MeiyuanHuang, WeixuanLin, YitingLiu, HonglaiLian, Cheng
As electric vehicles adoption becomes more common, power grid operators are facing new challenges in managing the unpredictable and varying energy demands in the existing electrical infrastructure. Moreover, the cost of Electric vehicle is high when compared to fuel vehicle it has limited access to charging infrastructure along with the driving range that act as a key barrier preventing the drivers from making shift to EVs. When the EV usage integrates with blockchain, it mitigates the limitation in charging station infrastructure along with the former problem discussed. The lack of trust exists between EV owners and charging station providers can be solved through secure and transparent payment processing possible by blockchain based smart contract. Building charging station on blockchain will ease the automated payment through the use of smart contract and create more efficient EV charging network. Also, the blockchain-based charging system would enable EV owners know if they are being charged in excess and Prosumer know if they are being underpaid. The high initial cost is another prominent issue within the market place. To address this issue the introduction of sharing economy to the EV industry showcases another innovative solution that blockchain offers. The blockchain enabled sharing economy platform allows individuals to access collaboratively with the prosumer and the consumer. This provides alternative to traditional ownership while reduces individual financial barriers and maximizing electric vehicle utilization across the network. The EV users have great opportunity worldwide to take a stake in the future of EV adoption on blockchain. Therefore, this work demonstrates the sharing economy while designing, building, and customizing smart contracts for prosumers and consumers by enabling decentralized payment systems. Our research aims to develop decentralized charging electronic payment systems using blockchain and customized smart contracts to build and design the application. For blockchain Solidity programming language is used. The application displays the charging process, payment system, and charging history information.
Govindasamy, DhivyaR, Rajarajeswari
The growing awareness about sustainability and environmental concerns are accelerating the adoption of electric vehicles. They play a promising role due to their potential to significantly reduce greenhouse gas emissions, improve air quality and lessen reliance on fossil fuels. However, one of the primary concerns for potential buyers is the charging process and infrastructure. Traditional wired charging systems for electric vehicles face limitations such as user inconvenience, wear and tear of connectors and challenges in automation. A wireless electric vehicle charging offers more user-friendly, automated and contactless method by eliminating the need for physical connectors. However, wireless inductive charging suffers from relatively low efficiency due to higher energy losses. Whereas resonant coupling significantly improves efficiency by using electromagnetic resonance to transfer power more effectively over short distances. This paper mainly focuses on design and implementation of a resonant coupling system using series capacitance for achieving resonance, instead of a traditional frequency oscillator. This approach simplifies the circuitry and has shown promising results in maintaining high efficiency. Investigations have been carried out by aligning the transmitter and receiver coils at different distances and load conditions. In the proposal model, resonant wireless power transfer precisely tunes the transmitter and receiver coils to resonate at a shared frequency of 60 kHz, minimising inductive losses and achieving efficiencies of up to 89.74%. The findings showed the potential for resonant wireless power transfer systems to support the next generation of electric vehicle infrastructure. This paper also presents a review on various wireless electric vehicle charging approaches.
Shaik, AmjadGudipati, Ravi Sai HemanthB, Vikranth ReddyAnudeep, D B S SVarshith, Dasari
The growing adoption of electric vehicles (EVs), particularly those utilizing High-Voltage battery systems, demands fast-charging infrastructure that ensures high efficiency and power quality. The proposed GJO algorithm is employed to optimize the control and switching parameters of the Vienna rectifier, thereby improving harmonic performance and conversion efficiency without altering the converter hardware. This paper focuses solely on control optimization of the Vienna rectifier topology and does not include DC–DC isolation or galvanic separation. Filter components are modeled with equivalent series resistance (ESR) to account for incremental losses. Simulation results demonstrate that the Golden Jackal optimization (GJO) based control reduces input current THD to 2.09%, has a power factor of 0.998, and achieves an efficiency of 98.53%, representing a fractional but consistent improvement over conventional control methods such as SSA, ALO, and PSO. These findings highlight the effectiveness of GJO in enhancing the performance of vienna rectifier-based chargers, establishing it as a promising solution for next-generation high-voltage EV fast-charging infrastructure. However, since the vienna rectifier is a unidirectional converter, the proposed system is limited to grid-to-vehicle operation and does not support reverse power flow (vehicle-to-grid).
R, Mohammed AbdullahN, Kalaiarasi
Due to the rapid transformation of EVs and the battery storage system, the battery management system (BMS) is essential to ensure optimal performance of the battery storage piles. A BMS monitors and controls parameters such as SOC, voltage, current, and temperature. A traditional BMS has a minimum support of analytics, and it’s limited to local processing. However, when the battery information is uploaded to the internet, it becomes easier to manage maintenance and track the battery’s performance from anywhere in the world. This Cloud-based system is easy and made earlier, thereby giving a system alarm before the issue becomes big. Managing many batteries at once saves a significant amount of money in places like EV charging stations and Energy Storage Systems (BESS). Software updates to the system can also be sent remotely. Also, a BMS connected to the cloud can be used to support weaker grids in an instant if it needs the reactive power support. Cloud integration of BMS with the grid network will help in better planning of energy management at load dispatch centers. A BMS managing a pack of batteries at a renewable energy system can help to understand power demand and decide when the best time is to charge or discharge. So, this can monitor all the batteries without being near them. Further, identifying the problems is work that focuses on an ML-RL-based battery management system connected to the cloud to control and monitor the Voltage, temperature, Cell balancing, SOC, SOH, and fault identification. This BMS system has easy scalability to thousands of batteries connected. As the demand for EVs and clean energy soars, this cloud-integrated BMS would play an important role in managing the batteries that are part of that system, making it smarter, efficient, and reliable. The proposed Q-learning–based Cloud BMS achieves 96.5% energy efficiency, 3.2% SOC RMSE, and zero safety violations across 75,000 simulated samples, using an adaptive 6,000-state Q-learning agent validated through real-time cloud integration.
R, RajarajeswariN, KalaiarasiFrancis, Elgin Calister
The growing global adoption of electric vehicles (EVs) has resulted in a spike in the number of EV charging stations. As EVs have become more and more popular worldwide, a large number of EV charging stations are opening up to accommodate their demands. During grid failures, an EV charging station can also serve as a flexible load connected to the grid to balance out voltage fluctuations. An EV charging station when powered using a separate source, such as solar or wind, can function as a powerhouse, bringing electricity to the grid when it's needed. Therefore, instead of installing more equipment to sustain voltage, the current EV charging station can be efficiently used to meet the grid's needs during failures. These stations have the potential to be dynamic, grid-connected assets for sustainable cities and communities in addition to their core function of vehicle charging (SDG 11). Because of their dual purpose, they can serve as adaptable loads that reduce voltage variations during grid outages, making it easier for people to obtain dependable electricity (SDG 7). By making use of the current EV infrastructure, a low-carbon energy transition is promoted, and resource efficiency (SDG 13- Climate Action) is supported, while lowering the demand for additional grid-support devices.
R, UthraRangarajan, RaviD, SuchitraD, Anitha
This study presents the design and implementation of an advanced IoT-enabled, cloud-integrated smart parking system, engineered to address the critical challenges of urban parking management and next-generation mobility. The proposed architecture utilizes a distributed network of ultrasonic and infrared occupancy sensors, each interfaced with a NodeMCU ESP8266 microcontroller, to enable precise, real-time monitoring of individual parking spaces. Sensor data is transmitted via secure MQTT protocol to a centralized cloud platform (AWS IoT Core), where it is aggregated, timestamped, and stored in a NoSQL database for scalable, low-latency access. A key innovation of this system is the integration of artificial intelligence (AI)-based space optimization algorithms, leveraging historical occupancy patterns and predictive analytics (using LSTM neural networks) to dynamically allocate parking spaces and forecast demand. The cloud platform exposes RESTful APIs, facilitating seamless interoperability with user-facing mobile and web applications. These interfaces provide end-users with real-time visualization of parking availability, intelligent navigation to optimal spaces, and digital payment integration, thereby minimizing search time and enhancing user convenience. From an administrative perspective, the system delivers comprehensive analytics dashboards, including heatmaps of space utilization, anomaly detection for unauthorized parking, and predictive maintenance alerts for sensor nodes. Field trials conducted across a multi-level parking facility demonstrated a 32% reduction in average vehicle search time and a 21% improvement in space utilization efficiency compared to conventional systems. The end-to-end solution adheres to robust cybersecurity standards (TLS 1.2 encryption, role-based access control) and is designed for modular scalability, supporting integration with smart city infrastructure and electric vehicle charging stations. This research establishes a scalable, intelligent framework for urban parking management, contributing significantly to reduced congestion, optimized resource allocation, and enhanced urban mobility.
Deepan Kumar, SadhasivamS, BalakrishnanDhayaneethi, SivajiBoobalan, SaravananAbdul Rahim, Mohamed ArshadS, ManikandanR, JamunaL, Rishi Kannan
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