Electric Vehicle System Research Articles

A comprehensive scheme for power management of FC/SC/battery, and solar-roof PV source in electric vehicle systems.

This paper proposes a new energy management system to combine Fuel Cells (FC) and photovoltaic (PV) panels as primary power sources. Also, battery and Super Capacitor (SC) banks are considered as secondary energy systems. The high power density and the fast dynamics of SCs combined with the high energy density and medium dynamics of batteries would be an ideal combination for FC vehicles. The high step-up DC/DC converter is used along with the implementation of the maximum power point tracking algorithm (MPPT) to adapt the low power of PV panels with the high power of DC-Link. Also, an evolutionary optimization algorithm is proposed along with the FOPI controller to improve the output of the energy management algorithm and control the sources more efficiently. The simulation results are validated for a standard urban driving cycle. It indicates for the case of the sustained presence of a PV source during a typical driving cycle, the source delivers about 55% of its maximum power.

Efficient Hybrid Electric Vehicle Power Management: Dual Battery Energy Storage Empowered by Bidirectional DC–DC Converter

ABSTRACTThis work offers a fuel cell power system with the ability to distribute power to the load from the electrical source and charge an auxiliary battery utilizing regenerative power flows created by the load. The approach is established on a bidirectional closed‐loop DC converter. A bidirectional DC–DC converter is presented as a means of achieving extremely high voltage energy storage systems (ESSs) for a DC bus or supply of electricity in power applications. This paper presents a novel dual‐active‐bridge (DAB) bidirectional DC–DC converter power management system for hybrid electric vehicles (HEVs). The proposed system makes it possible to charge an additional battery with regenerative power flows and distributes power from the electrical source to the load efficiently. The two main stages of the DAB converter, which are the focus of this work, are an interleaved buck/boost converter on the battery and a three‐phase wye‐wye series resonating converter on the DC bus. Each switch's current stress is greatly reduced by this design, which lowers transmission losses and enhances thermal performance. The interleaved buck conversion on the battery allows for lesser current stress in each switch, resulting in lower transmission loss. The increasing complexity and power of automotive embedded electronic systems have made the use of more potent power electronic converters in automobiles necessary. In recent years, many dual volt (42 V/14 V) bidirectional inverter topologies for automotive systems have been presented. However, the majority of them are either inefficient or use a huge number of transistors and magnetic devices in both parallel and series arrangements. As a result, in this study, a bidirectional high‐efficiency inverter with fewer components is provided. The design, modes of operation, and performance metrics of the DAB converter are examined, emphasizing its ability to achieve zero‐voltage switching (ZVS) and zero current switching (ZCS) throughout its operating range. The suggested system seeks to maximize EV power management, guaranteeing high dependability and efficiency. To test all of the aforementioned qualities, an evaluation version was created, with an average efficiency of 97.5%. This research could have a substantial impact on the advancement of power electronic converters for automotive applications, leading to better EV power management, increased system reliability, and increased overall efficiency.

An Optimized Strategy for the Integration of Photovoltaic Systems and Electric Vehicles into the Real Distribution Grid

The increasing spread of photovoltaic systems for private households (PVs) and electric vehicles (EVs) in order to reduce carbon emissions significantly impacts operation conditions in existing distribution networks. Variable and unpredictable PVs can stress distribution network operation, mainly manifested in voltage violations during the day. On the other hand, variable loads such as EV chargers which have battery storage in their configuration have the ability of storying a surplus energy and, if it is necessary, support a distribution network with energy, commonly known as vehicle-to-grid concept (V2G), to help voltage stability network enhancement. This paper proposes an optimal power flow (OPF)-based model for EV charging to minimize power exchange between the superior-10 kV grid and the observed distribution feeder. The optimization procedure is realized using the co-simulation approach that connects power flow analysis software and optimization method. Three different scenarios are observed and analysed. The first scenario is referred to as a base case without optimization. The second and third scenarios include optimal EV charging and discharging patterns under different constraints. To test the optimization model, a 90-bus unbalanced distribution feeder modelled based on real-life examples is used. The obtained results suggest that this optimization model does not only significantly reduce the power exchange between an external network and the distribution feeder but also improves voltage stability and demand curve in the distribution feeder.

AI-Driven Route Planning and Scheduling for Electric School Buses

This review paper explores the current state, recent advancements, challenges, and future perspectives of AI-driven approaches for route planning and scheduling of Electric School Buses (ESBs). The integration of artificial intelligence into energy management systems for electric vehicles has gained significant attention, particularly in optimizing school transportation. This study examines various AI techniques, including genetic algorithms, reinforcement learning, and game-theoretic approaches, applied to ESB management. Key focus areas include energy consumption estimation, battery capacity optimization, and Vehicle-to-Grid (V2G) strategies. The paper also addresses critical challenges such as data integration, security concerns, and operational constraints. Future perspectives highlight the potential of advanced AI techniques, smart grid integration, and personalized transportation solutions. By synthesizing current research and identifying key areas for future development, this review contributes to ongoing efforts to improve the efficiency, sustainability, and overall quality of school transportation systems through AI technologies.

Long‐Term Estimation of SoH Using Cascaded LSTM‐RNN for Lithium Batteries Subjected to Aging and Accelerated Degradation

ABSTRACTAccurate estimation of state of health (SoH) of the battery over long‐term is a critical challenge for the battery management systems in electric vehicles. This is due to the challenges in accurately modeling the accelerated aging and degradation phenomena caused by diverse operating conditions of the battery. This paper presents a cascaded recurrent neural networks (RNN) with long short‐term memory (LSTM) to estimate the internal resistance and SoH, taking account of various abnormal operating conditions of the battery. A datasheet‐based degradation model of the battery is developed using fade equations. The training and validation data set for LSTM‐RNN are generated by subjecting the battery model to various factors that cause accelerated degradation, such as fast charging, varying operating temperatures, overutilization, and cell imbalance. The cascaded LSTM‐RNN is trained to estimate SoH only once after the completion of every charge–discharge cycle. The training error index parameters of the proposed SoH estimator are well within 1%, demonstrating the reliability and robustness of the estimator to diverse operating conditions of the battery.

Solar Wireless Electric Vehicle Charging System

This project outlines the design and implementation of a solar-powered electric vehicle charging station that addresses the dual challenges of high gasoline prices and harmful emissions. The number of countries adopting electric vehicles continues to rise, as they not only promote environmental sustainability but also significantly lower transportation costs by substituting expensive fuels with more affordable electricity. In this context, developing an electric vehicle charging infrastructure offers an innovative and effective solution. This system enables electric vehicles to charge while in motion, eliminating the need for stationary charging stops. Powered entirely by solar energy, it requires no additional power supply. The system incorporates solar panels, batteries, transformers, regulator circuits, copper coils, AC/DC converters, Atmega328P controllers, and LCD displays. This technology demonstrates the feasibility of an on-road solar-powered wireless charging system for electric vehicles.

Current development, optimisation strategies and future perspectives for lead-free dielectric ceramics in high field and high energy density capacitors.

To meet the United Nations' sustainable development goal of affordable and clean energy, there has been a growing need for low-cost, green, and safe energy storage technologies. High-field and energy-density capacitors have gained substantial attention from academics and industry, particularly for power electronics, where they will play a key role in optimising the performance of management systems in electric vehicles. The key figure of merit, energy density (Wrec), for high-field applications has dramatically increased year-on-year from 2020 to 2024, as evidenced by over 250 papers, demonstrating ever larger Wrec values. This review briefly introduces the background and principles of high energy density ceramics, but its focus is to provide constructive and comprehensive insight into the evaluation of Wrec, Emax, ΔP, and η, and more importantly, the normalised metrics, Wrec/Emax and Wrec/ΔP in lead-free dielectric ceramics. We also present several optimisation strategies for materials modification and process innovation that have been recently proposed before providing perspectives for the further development of high-field and high-energy density capacitors.

Data-Driven Approaches for State-of-Charge Estimation in Battery Electric Vehicles Using Machine and Deep Learning Techniques

One of the most important functions of the battery management system (BMS) in battery electric vehicle (BEV) applications is to estimate the state of charge (SOC). In this study, several machine and deep learning techniques, such as linear regression, support vector regressors (SVRs), k-nearest neighbor, random forest, extra trees regressor, extreme gradient boosting, random forest combined with gradient boosting, artificial neural networks (ANNs), convolutional neural networks, and long short-term memory (LSTM) networks, are investigated to develop a modeling framework for SOC estimation. The purpose of this study is to improve overall battery performance by examining how BEV operation affects battery deterioration. By using dynamic response simulation of lithium battery electric vehicles (BEVs) and lithium battery packs (LIBs), the proposed research provides realistic training data, enabling more accurate prediction of SOC using data-driven methods, which will have a crucial and effective impact on the safe operation of electric vehicles. The paper evaluates the performance of machine and deep learning algorithms using various metrics, including the R2 Score, median absolute error, mean square error, mean absolute error, and max error. All the simulation tests were performed using MATLAB 2023, Anaconda platform, and COMSOL Multiphysics.

Energy Management System and Control of Plug-in Hybrid Electric Vehicle Charging Stations in a Grid-Connected Microgrid

In the complex environment of microgrid deployments targeted at geographic regions, the seamless integration of renewable energy sources meets a variety of essential challenges. These include the unpredictable nature of renewable energy, characterized by intermittent energy generation, as well as ongoing fluctuations in load demand, the vulnerabilities present in distribution network failures, and the unpredictability that results from unfavorable weather conditions. These unexpected events work together to disturb the delicate balance between energy supply and demand, raising the alarming threat of system instability and, in the worst cases, the sudden advent of damaging blackouts. To address this issue, a fuzzy logic-based energy management system has been developed to monitor, manage, and optimize energy consumption in microgrids. This study focuses on the control of diesel generators and utility grids in a grid-connected microgrid which manages and evaluates numerous energy consumption and distribution features within a specified system, e.g., building or a microgrid. An energy management system is suggested based on fuzzy logic as a swift fix for complications with effective and competent resource management, and its presentation is compared with both the grid-connected and off-grid modes of the microgrid. In the end, the results exhibit that the proposed controller outclasses the predictable controllers in dropping sudden variations that arise during the addition of sources of renewable energy, supporting the refurbishment of the constant system.

Intelligent Robust Controllers Applied to an Auxiliary Energy System for Electric Vehicles

This paper presents two intelligent robust control strategies applied to manage the dynamics of a DC-DC bidirectional buck–boost converter, which is used in conjunction with a supercapacitor as an auxiliary energy system (AES) for regenerative braking in electric vehicles. The Neural Inverse Optimal Controller (NIOC) and the Neural Sliding Mode Controller (NSMC) utilize identifiers based on Recurrent High-Order Neural Networks (RHONNs) trained with the Extended Kalman Filter (EKF) to track voltage and current references from the converter circuit. Additionally, a driving cycle test tailored specifically for typical urban driving in electric vehicles (EVs) is implemented to validate the efficacy of the proposed controller and energy improvement strategy. The proposed NSMC and NIOC are compared with a PI controller; furthermore, an induction motor and its corresponding three-phase inverter are incorporated into the EV control scheme which is implemented in Matlab/Simulink using the “Simscape Electrical” toolbox. The Mean Squared Error (MSE) is computed to validate the performance of the neural controllers. Additionally, the improvement in the State of Charge (SOC) for an electric vehicle battery through the control of buck–boost converter dynamics is addressed. Finally, several robustness tests against parameter changes in the converter are conducted, along with their corresponding performance indices.

Multi-Step Temperature Prognosis of Lithium-Ion Batteries for Real Electric Vehicles Based on a Novel Bidirectional Mamba Network and Sequence Adaptive Correlation

The battery systems of electric vehicles (EVs) are directly impacted by battery temperature in terms of thermal runaway and failure. However, uncertainty about thermal runaway, dynamic conditions, and a dearth of high-quality data sets make modeling and predicting nonlinear multiscale electrochemical systems challenging. In this work, a novel Mamba network architecture called BMPTtery (Bidirectional Mamba Predictive Battery Temperature Representation) is proposed to overcome these challenges. First, a two-step hybrid model of trajectory piecewise–polynomial regression and exponentially weighted moving average is created and used to an operational dataset of EVs in order to handle the problem of noisy and incomplete time-series data. Each time series is then individually labeled to learn the representation and adaptive correlation of the multivariate series to capture battery performance variations in complex dynamic operating environments. Next, a prediction method with multiple steps based on the bidirectional Mamba is suggested. When combined with a failure diagnosis approach, this scheme can accurately detect heat failures due to excessive temperature, rapid temperature rise, and significant temperature differences. The experimental results demonstrate that the technique can accurately detect battery failures on a dataset of real operational EVs and predict the battery temperature one minute ahead of time with an MRE of 0.273%.

Modelling Analysis of Propylene Glycol as a Cooling Media for Battery Thermal Management System in Electric Vehicles

Modelling Analysis of Propylene Glycol as a Cooling Media for Battery Thermal Management System in Electric Vehicles

Driving Control Strategy and Specification Optimization for All-Wheel-Drive Electric Vehicle System with a Two-Speed Transmission

Equipping electric vehicles with a two-speed gearbox allows for achieving high torque and maximum speed through appropriate gear ratio adjustments. Additionally, tuning motor operating points to efficient zones, considering energy efficiency, significantly enhances the vehicle’s overall performance. This paper presents an AWD system configuration method, integrating a two-speed transmission to improve energy efficiency and driving performance through front and rear motor torque distribution and powertrain specification optimization. Based on vehicle simulations conducted using MATLAB/Simulink, a strategy for torque distribution between the front/rear axles was established using fuzzy logic, considering energy efficiency and driving stability. Furthermore, a multi-objective optimization was performed using a surrogate model trained through MATLAB parallel simulations. When the optimization results were applied to various vehicle specifications, it was observed that energy efficiency was improved, and acceleration performance was increased compared to a baseline vehicle without optimization.

Molecular Simulations of Thermal Transport across Iron Oxide-Hydrocarbon Interfaces.

The rational design of dielectric fluids for immersion cooling of batteries requires a molecular-level understanding of the heat flow across the battery casing/dielectric fluid interface. Here, we use nonequilibrium molecular dynamics (NEMD) simulations to quantify the interfacial thermal resistance (ITR) between hematite and poly-α-olefin (PAO), which are representative of the outer surface of the steel battery casing and a synthetic hydrocarbon dielectric fluid, respectively. After identifying the most suitable force fields to model the thermal properties of the individual components, we then compared different solid-liquid interaction potentials for the calculation of the ITR. These potentials resulted in a wide range of ITR values (4-21 K m2 GW-1), with stronger solid-liquid interactions leading to lower ITR. The increase in ITR is correlated with an increase in density of the fluid layer closest to the surface. Since the ITR has not been experimentally measured for the hematite/PAO interface, we validate the solid-liquid interaction potential using the work of adhesion calculated using the dry-surface method. The work of adhesion calculations from the simulations were compared to those derived from experimental contact angle measurements for PAO on steel. We find that all of the solid-liquid potentials overestimate the experimental work of adhesion. The experiments and simulations can only be reconciled by further reducing the strength of the interfacial interactions. This suggests some screening of the solid-liquid interactions, which may be due to the presence of an interfacial water layer between PAO and steel in the contact angle experiments. Using the solid-liquid interaction potential that reproduces the experimental work of adhesion, we obtain a higher ITR (33 K m2 GW-1), suggesting inefficient thermal transport. The results of this study demonstrate the potential for NEMD simulations to improve understanding of the nanoscale thermal transport across industrially important interfaces. This study represents an important step toward the rational design of more effective fluids for immersion cooling systems for electric vehicles and other applications where thermal management is of high importance.

Analysis and Design of a Battery and Supercapacitor‐Based Propulsion System for Electric Vehicles

ABSTRACTThis work deals with the design and development of a dual source based propulsion architecture for electric vehicles. The converter utilized for the propulsion system is derived from a conventional CuK converter, which operates in a bidirectional power flow mode. The proposed propulsion system has two energy sources: a battery and a supercapacitor (SC). The proposed system has continuous input and output currents at low and high voltage sides, which improves the cycle life of hybrid energy storage (SC and battery) and the DC‐link capacitor. Further, the proposed converter has magnetic reduction compared to a conventional two‐input bidirectional CuK converter. Moreover, an effective and simpler control strategy (only one switch is pulse width modulated (PWM) operated at a time) has been developed for energy management between sources during charging (regenerative braking) and discharging (propulsion), which is effectively verified by simulation and experimental results. Further, a frequency domain (bode plot)‐based technique is used for controller design and stability analysis of the proposed system.

Deep reinforcement learning-based energy management system enhancement using digital twin for electric vehicles

Compared to conventional engine-based powertrains, electrified powertrain exhibit increased energy efficiency and reduced emissions, making electrification a key goal for the automotive industry. For a vehicle with a hybrid energy storage system, its performance and lifespan are substantially affected by the energy management system. Reinforcement learning-based methods are gaining popularity in vehicle energy management, but most of the literature in this area focuses on pure simulation, while hardware implementation is still limited. This paper introduces the digital twin methodology to enhance the reinforcement learning-based energy management system for battery and ultracapacitor electric vehicles. The digital twin model can exploit the bilateral interdependency between the virtual model and the actual system, which improves the control performance of the energy management system in real-time control. The physical model is established based on a hardware-in-the-loop simulation platform. In addition, battery degradation is also considered for prolonging the battery lifespan to reduce operating costs. The validation results of the trained reinforcement learning agent illustrate that the digital twin-enhanced Q-learning energy management system improves the energy efficiency by 7.08 % and reduces the battery degradation by 25.28 %.

Optimization of power system parameters for multi-mode hybrid electric vehicles based on regional demand

The performance of the vehicle power system for multi-mode hybrid electric vehicle (M-MHEV) can be improved through meticulous parameter matching and optimization. This paper developed the powertrain parameter design and dynamic performance calculation program based on the structure design, parameter matching calculation, and powertrain system selection. A HOQM of M-MHEV is formulated to ascertain the weight coefficient of vehicle performance indicators according to different regional requirements and a parameter matching and optimization method for power system, employing the Particle Swarm Optimization (PSO) algorithm, is suggested to assess and harmony the vehicle’s performance. Firstly, a house of quality model of M-MHEV is constructed to ascertain the weight coefficient of vehicle performance indicators derived from different regional demands. Additionally, a method is introduced for optimization of power system parameters of M-MHEV based on regional demand, the PSO algorithm is employed to optimize the characteristic parameters. And sensitivity analysis of characteristic parameters is conducted relying on user needs in different regions. The simulation results show that users in the northern and southern regions have different final weight coefficients for vehicle performance indicators and the parameters not only exert a substantial individual influence but also exhibit an interaction effect on the vehicle performance. Finally, a series of comparative simulations, are carried out with two optimization parameters respectively, the simulation outcomes demonstrate that the solution obtained through optimized design parameters solution could markedly enhance the technical parameters of the vehicle. The efficacy and viability of the proposed method based on user needs in different regions are verified. The conclusion provides a useful reference for differentiated design.

Adaptive fuzzy PI control for the dynamic operation of the transcritical CO2 thermal system in electric vehicles

Process control is essential for ensuring the stable and efficient operation of EVs’ transcritical CO2 thermal management systems. Traditional PI control generally shows an insufficient dynamic response for the variable and complex operating conditions and then causes fluctuant process control. This study proposed an adaptive fuzzy PI control strategy to provide real-time varying PI gains for fast-tracking response and fluctuation suppression, considering the compressor discharge pressure, coolant, and air outlet temperature. A high-fidelity transcritical CO2 thermal model in EVs was first built in the Amesim platform to simulate real-time operation, and the adaptive fuzzy PI control algorithm was then developed to optimize the process control. The co-simulation is conducted to validate the effectiveness and adaptability of the proposed control methodology by comparing it with a benchmark traditional PI control strategy. The results indicated that the proposed adaptive fuzzy PI could provide a 9.22 % overshoot suppression for discharge pressure, a maximal 22.29 % overshoot inhibition and 86 s settling time reduction for the air outlet temperature, and a 25 % settling time reduction for the coolant temperature control. In addition, the fuzzy PI improved the start-up characteristics by avoiding excessive response. Such findings demonstrate that the proposed fuzzy PI control algorithm can effectively optimize the dynamic operation of the transcritical CO2 thermal system in EVs.

Design of motion control system for unmanned wheeled electric vehicle

Abstract The unmanned wheeled electric vehicle is a weapon platform that draws significant attention in current and future battlefields, and research on its motion control is fundamental. This paper introduces the design of a 6×6 unmanned wheeled electric vehicle and discusses the motion control methods applicable to this vehicle. The electric drive system of this vehicle comprises six independent asynchronous motors and corresponding motor controllers. A comprehensive controller designed based on DSP connects the six motor controllers. As it is an experimental platform, the motion control algorithms are deployed on an onboard computer, which connects to the comprehensive controller. Using the Backstepping method, the kinematic control algorithm for the 6×6 unmanned wheeled electric vehicle is designed. The global asymptotic stability of the control system is proven through the Lyapunov function, and its control performance is verified via simulation experiments. Finally, the system is deployed and tested on the actual vehicle. Experimental results show that the motion control system designed in this paper enables the unmanned wheeled electric vehicle to achieve effective motion control. The control laws derived from the Backstepping method are relatively simple and have low requirements for the sampling period. In practical systems, output speed magnitude and rate of change must be limited to stay within allowable system parameters.

Design of Novel Double-Stator Switched Reluctance Motor Drive System for Electric Vehicles

Design of Novel Double-Stator Switched Reluctance Motor Drive System for Electric Vehicles