Electric Vehicle System Research Articles

Electrochemical Performance and Microstructure Evolution of a Quasi-Solid-State Lithium Battery Prepared by Spark Plasma Sintering.

Solid-state lithium batteries are promising next-generation energy storage systems for electric vehicles due to their high energy density and high safety and require achieving and maintaining intimate solid-solid interfaces for lithium-ion and electron transport. However, the solid-solid interfaces may evolve over cycling, disrupting the ion and electron diffusion pathways and leading to rapid performance degradation. The development of solid-state lithium batteries has been hindered by the lack of fundamental understanding of the interfacial microstructure change over cycling and its relation to electrochemical properties. Herein, we prepared a quasi-solid-state lithium battery, 30%LiFePO4-55%Li1.5Al0.5Ge1.5(PO4)3-15%C| Li1.5Al0.5Ge1.5(PO4)3|Li, by spark plasma sintering, and employed it as a model system to reveal the microstructure evolution at the solid-solid interfaces with electrochemical performance of the batteries. The electrochemical assessment showed that the quasi-solid-state lithium battery exhibited a discharge specific capacity of about 150 mAh g-1 in the first 80 cycles and then experienced severe capacity attenuation afterward, accompanied by a gradual internal resistance increase. Scanning electron microscopy observation showed that more cracks were formed inside the solid-state electrolyte and at the solid-solid interfaces as the battery cycled from 10 to 67 and 157 cycles. Detailed microstructure and phase analysis by high-resolution transmission electron microscopy and selected area electron diffraction discovered that the crack formation and performance decay were mainly caused by (1) the volume change of the LiFePO4 composite cathode during cycling, (2) the grain expansion of the Li1.5Al0.5Ge1.5(PO4)3 solid-state electrolyte at its interface with lithium anode, and (3) the formation of a solid electrolyte interphase layer, comprising Li2CO3, LiF, and LiTFSI, at the cathode-solid-state electrolyte interface. These microstructure changes built up over repeated battery cycling, ultimately causing the structure collapse and battery failure. The microstructure evolution information is expected to guide the design of better structures and interfaces for solid-state lithium batteries.

Neural Sliding Mode Control of a Buck-Boost Converter Applied to a Regenerative Braking System for Electric Vehicles

This paper presents the design and simulation of a neural sliding mode controller (NSMC) for a regenerative braking system in an electric vehicle (EV). The NSMC regulates the required current and voltage of the bidirectional DC-DC buck–boost converter, an element of the auxiliary energy system (AES), to improve the state of charge (SOC) of the battery of the EV. The controller is based on a recurrent high-order neural network (RHONN) trained using the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) as the tools to train the neural networks to obtain a higher SOC in the battery. The performance of the controller with the two training algorithms is compared with a proportional integral (PI) controller illustrating the differences and improvements obtained with the EKF and the UKF. Furthermore, robustness tests considering Gaussian noise and varying of parameters have demonstrated the outcome of the NSMC over a PI controller. The proposed controller is a new strategy with better results than the PI controller applied to the same buck–boost converter circuit, which can be used for the main energy system (MES) efficiency in an EV architecture.

Design and analysis of battery management system in electric vehicle

The usage of electric vehicles is gaining momentum in recent time’s thus providing support to the growth in sales of electric vehicles. The Battery management system is the most important aspect to ensure the smooth functioning of an electric vehicle. This research highlights some key statements on the background of electric vehicles. The increase in the overall growing importance of electric vehicles has also been explained in this work. Battery management system has an importance in the functioning of electric vehicles, thus presenting the key highlights of this article. The finding presents the importance of batteries and their type used in EVs. The simulation results of the Lithium battery cell – 1 RC, 2 RC equivalent circuit parameters such as charging current, terminal voltage, state of charge, and battery current have been simulated and analysed in Matlab. The future scope of BMS and its development has been discussed.

An Ultra-Local Model-Based Control Method With the Bus Voltage Supervisor for Hybrid Energy Storage System in Electric Vehicles

An Ultra-Local Model-Based Control Method With the Bus Voltage Supervisor for Hybrid Energy Storage System in Electric Vehicles

A metal object detection system for electric vehicle wireless charging based on LC oscillating circuit and reverse-winding-incorporated detection coils

In magnetically coupled wireless charging systems for electric vehicles, it is common for metal objects to enter the charging system. In this case, the strong magnetic field of wireless charging causes an eddy current effect in the metal object, which generates a large amount of heat. This heat accumulation poses a risk of fire. In this paper, a novel metal object detection system designed for wireless charging of electric vehicles is proposed. The system utilizes the principle that the coil impedance and coupling coefficient change with the presence of metal objects, and a set of composite coils is designed to improve the sensitivity of detection. The detection coil and feedback coil in the composite coil are decoupled by reverse series connection. An LC oscillating circuit based on transformer feedback is used to detect metal objects, and the circuit can make good use of the parameter changes of the composite coil. Finally, an experimental platform for wireless charging metal detection in electric vehicles has been built to validate the proposed system. Through testing, it has been demonstrated that the system can detect the presence of metal objects in a timely and accurate manner.

ICNCS: Internal Cascaded Neuromorphic Computing System for Fast Electric Vehicle State-of-Charge Estimation

Accuracy and speed of Lithium-ion battery state of charge (SOC) estimation determine the reliability and stability of electric vehicle (EV), as well as promoting the development of smart home energy management system. Existing SOC estimation approaches embedded in commercial EVs still suffer from limitations of low precision, effectiveness, and relatively low robustness. To address these issues, we propose an internal cascaded neuromorphic computing system (ICNCS) via memristor circuits for EV SOC estimation. Specifically, we use three circuit modules to facilitate the design of the proposed ICNCS. Firstly, a bidirectional gated recurrent unit (Bi-GRU) circuit module is designed, enabling adequate feature extraction from the time-contextual battery information. Secondly, an attention circuit module is proposed to distinguish the useful and unimportant information related to SOC estimation. Thirdly, the Kalman filter module is constructed to eliminate the random noise caused by data transmission and processing. Finally, a series of experiments and analysis demonstrate that the proposed ICNCS has good performances in terms of accuracy (the lowest achievable RMSE and MAE are 0.853 and 0.711, respectively), time efficiency (approximately 16 20 times), and robustness (anti-noise capacity), indicating an advancement in consumer electronics applications.

A Two-Stage Optimization Method of High-Power DWPT System for Electric Vehicle

A Two-Stage Optimization Method of High-Power DWPT System for Electric Vehicle

Study of hybrid energy storage system with energy management for electric vehicle applications

This paper conducts an in-depth study on the on-board energy storage system for electric vehicles. We analyze the advantages and disadvantages of domestic and foreign energy storage systems including battery, flywheel, superconductor and supercapacitor. We propose a hybrid energy storage system composed of battery and supercapacitor as the on-board power supply system. It adopts a two-phase staggered parallel bidirectional DC-DC converter as the main control circuit of the storage system. And we adopt the power outer loop and current inner loop control strategy. Then the rate limiter prevents the sudden change of battery power and realizes that the instantaneous power is provided by the supercapacitor individually. The purpose is to extend the battery life and achieve the purpose of reasonable and efficient utilization of energy. Finally, the simulation verification is carried out by Matlab/Simulink. And the results show that the energy management strategy is more efficient for both power and energy management, which meets the design expectation and achieves better experimental results.

Design and Application of Flywheel–Lithium Battery Composite Energy System for Electric Vehicles

Design and Application of Flywheel–Lithium Battery Composite Energy System for Electric Vehicles

Nonlinear optimal control for the five-phase induction motor-based traction system of electric vehicles

PurposeThe purpose of this article is to treat the nonlinear optimal control problem in EV traction systems which are based on 5-phase induction motors. Five-phase permanent magnet synchronous motors and five-phase asynchronous induction motors (IMs) are among the types of multiphase motors one can consider for the traction system of electric vehicles (EVs). By distributing the required power in a large number of phases, the power load of each individual phase is reduced. The cumulative rates of power in multiphase machines can be raised without stressing the connected converters. Multiphase motors are also fault tolerant because such machines remain functional even if failures affect certain phases.Design/methodology/approachA novel nonlinear optimal control approach has been developed for five-phase IMs. The dynamic model of the five-phase IM undergoes approximate linearization using Taylor series expansion and the computation of the associated Jacobian matrices. The linearization takes place at each sampling instance. For the linearized model of the motor, an H-infinity feedback controller is designed. This controller achieves the solution of the optimal control problem under model uncertainty and disturbances.FindingsTo select the feedback gains of the nonlinear optimal (H-infinity) controller, an algebraic Riccati equation has to be solved repetitively at each time-step of the control method. The global stability properties of the control loop are demonstrated through Lyapunov analysis. Under moderate conditions, the global asymptotic stability properties of the control scheme are proven. The proposed nonlinear optimal control method achieves fast and accurate tracking of reference setpoints under moderate variations of the control inputs.Research limitations/implicationsComparing to other nonlinear control methods that one could have considered for five-phase IMs, the presented nonlinear optimal (H-infinity) control approach avoids complicated state-space model transformations, is of proven global stability and its use does not require the model of the motor to be brought into a specific state-space form. The nonlinear optimal control method has clear implementation stages and moderate computational effort.Practical implicationsIn the transportation sector, there is progressive transition to EVs. The use of five-phase IMs in EVs exhibits specific advantages, by achieving a more balanced distribution of power in the multiple phases of the motor and by providing fault tolerance. The study’s nonlinear optimal control method for five-phase IMs enables high performance for such motors and their efficient use in the traction system of EVs.Social implicationsNonlinear optimal control for five-phase IMs supports the deployment of their use in EVs. Therefore, it contributes to the net-zero objective that aims at eliminating the emission of harmful exhaust gases coming from human activities. Most known manufacturers of vehicles have shifted to the production of all-electric cars. The study’s findings can optimize the traction system of EVs thus also contributing to the growth of the EV industry.Originality/valueThe proposed nonlinear optimal control method is novel comparing to past attempts for solving the optimal control problem for nonlinear dynamical systems. It uses a novel approach for selecting the linearization points and a new Riccati equation for computing the feedback gains of the controller. The nonlinear optimal control method is applicable to a wider class of dynamical systems than approaches based on the solution of state-dependent Riccati equations.

Support vector machine based fault detection in inverter‐fed electric vehicle

AbstractInverters play a prominent role in the power train system of electric vehicles (EVs). Devices in EV connected power system are threatened by faults due to the continuous working and varying speed range of motors in EVs. Hence, in the EV connected application, the detection of fault is essential since it secures the system from severe damage and dangerous operating conditions. This paper deals with fault detection in inverter‐fed EV using a dual‐tree complex wavelet transform (DTCWT) based squeeze net (SN) and optimized support vector machine (SVM). Due to the simple structure and high power density, most EV models on the market are equipped with induction motors. In the proposed work, the voltage, current, and speed signals are measured at different faulty conditions, and then the features are extracted through the DTCWT‐based SN. Extracted data are processed and classified through the sucker‐vulture optimization algorithm (SVOA) based SVM. In the proposed SVOA, the exploration phase of remora optimization algorithm is used for the exploitation phase of African vulture optimization algorithm (AVOA). Thus, the convergence speed of AVOA is improved. The proposed method is implemented in MATLAB/SIMULINK, and the results are used for different scenarios. The accuracy and F1‐score for the proposed methodology are attained as 99.92441% and 99.92441%. From the obtained results, it is clear that the proposed DTCWT‐based SN effectively detects the faults in the inverter.

A novel 7-level reduced-switch MLI topology fed PMSM drive for electric vehicle system

The compact and efficient design motivates the renewable energy-powered permanent magnet synchronous motor drive for electric vehicle applications. The available renewable energy is interfaced with a power drive by employing an electronic commutator such as a conventional 3-level inverter. But, the multilevel inverter produces favorable merits by producing staircase output voltage from several input direct current (DC) sources. The cascaded H-bridge multilevel inverter plays a significant role in many applications, but it was developed only for limited voltage levels. The major problem in cascaded h-bridge multilevel inverter (CHBMLI) requires more switching devices for higher voltage levels, which increases the size, cost and space of the electric vehicle (EV). To overcome above-mentioned problems, a new objective has been developed by employing the novel Reduced-switch multilevel inverter topology for higher voltage levels. This improves the voltage quality and reducing the harmonic level, and common-mode voltage issues. The main contribution of this work is, developing the novel 5-level, 7-level reduced-switch multilevel inverter (RSMLI) topologies with reduced switching devices with favourable merits over CHBMLI topology. Finally, the performance of proposed novel 5-level and 7-level RSMLI topologies fed PMSM drive for EV system has been verified, by using MATLAB/Simulink computing tool, and simulation results are presented with comparisons.

Design and Analysis of Battery Management System for Electric Vehicles

Abstract: This article presents a design for both the hardware and software components of the BMS, enabling battery monitoring and management. The Battery Management System (BMS) is a fundamental component of electric vehicles, primarily utilized to ensure battery safety and enhance battery lifespan. The hardware component encompasses the design of voltage acquisition circuitry, second-order filtering circuitry, sampling and holding circuitry, CAN bus communication circuitry, and other relevant features. The software section comprises subroutines for battery information collection, equalization circuitry, SOC estimation, and other relevant features. The BMS developed in this study successfully collects voltage, temperature, current, and other relevant information, and accurately estimates SOC and other crucial parameters. Testing confirmed that the battery management system precisely collects battery voltage, current, and temperature information, while the SOC estimation achieves a relatively high degree of accuracy.

Evaluation of Electromagnetic Exposure in Wireless Power Transfer Systems for Electric Vehicles

In wireless power transfer (WPT) systems, the electromagnetic fields generated by a charging module may exceed the limits set by international safety guidelines. This is a matter of concern for the safety of users of high-power WPT systems, such as electric vehicles (EVs). To address this issue, this study designed a stationary WPT system for EV charging. Furthermore, the dosimetry of the system was evaluated for two exposure scenarios. Electromagnetic field data obtained using the electromagnetic field analysis tool were employed to derive the induced quantities in the human body using the impedance method. In addition, the obtained results were compared to the values recommended by international guidelines (International Commission on Non-Ionizing Radiation Protection).

Simplified control of TAB converter for scalable multibattery charging system

SummaryThe integration of distributed energy resources (DERs) into DC microgrids has become commonplace in many applications, including electric vehicle (EV) systems that use batteries, fuel cells, and supercapacitors as energy storage systems. To make a sustainable charging system for electric vehicles, DERs can be used in conjunction with the grid. In many cases, charging systems based on isolated DC‐DC converters, such as the dual active bridge (DAB) converter, with various controls, are typically used. However, integrating multiple DERs at the common input DC link can result in a loss of isolation in the DAB converter and also leads to problems with circulating current. To maintain isolation, multiple DAB converters can be used, but this increases the system's size. Alternatively, the multiport isolated DC‐DC converter can address such issues but introduces complexities such as cross‐coupling between power flow. This paper proposes a hardware decoupling control methodology for a triple active bridge (TAB) based isolated DC‐DC converter, with power routing features for simultaneously charging two EV's battery stack. Through hardware decoupling control, appropriate passive components are designed and selected to connect with the individual H‐bridges of the TAB for power decoupling. The paper discusses the cross‐coupling issue in the TAB and describes a methodology derived from mathematical expressions for achieving power decoupling. For validation of the control, a 5 kW system is modeled in MATLAB Simulink, and the results demonstrate how the proposed approach overcomes cross‐coupling issues. Further, a 500 W hardware prototype has been developed to validate the proposed approach.

Sensor based Battery Management System in Electric Vehicle using IoT with Optimized Routing

Sensor based Battery Management System in Electric Vehicle using IoT with Optimized Routing

Combined control methods of the steering and traction system of in-wheel electric vehicle for a double lane-change in a low friction coefficient environment

This paper proposed advanced design strategy of adaptive controller for steering and traction control system of in wheel EV is proposed. Proposed algorithm is highly recommended for the first step driver to drive car under unstructured environment. When people drive a car on low friction coefficient road such as snowy or icy road, slip could be happen which disturb safe driving. This paper proposed stable driving algorithm when driving on snowy or icy road. Proposed algorithm has been verified and demonstrated through the co-simulation between Carsim and Matlab.

Optimization model of battery electric vehicle charging facility layout towards embedded system and data mining algorithm

Abstract Battery electric vehicles have been developing rapidly in recent years due to their advantages of energy saving and emission reduction. However, the development of battery electric vehicles is still in its infancy due to the relatively immature charging technology and the lack of infrastructure development. In many researches, there have been many solutions to adopt remote monitoring systems in battery electric vehicles to monitor and collect them in real time for statistical analysis and fault diagnosis. However, the current monitoring systems are carried out statistically and it is difficult to extract much valuable information from the data. In this paper, after analyzing the construction mode of battery electric vehicle charging facilities, the battery electric vehicle charging facility system platform using embedded system was analyzed. Several functions were simulated to meet the problems encountered by users in using it, studying the battery electric vehicle charging facility layout optimization model based on data mining algorithm. Focusing on the coverage problem in the siting problem, the State-of-Charge (SOC) of the battery charge state was analyzed. Taking residential areas as an example and 4 weeks as a cycle, it was found that during the first to fourth weeks, the negative power at 1:00, 20:00 and 24:00 was higher. At the same type of time point, different charging thresholds had a significant impact on the charging load demand of users. The larger the charging threshold of battery electric vehicles appeared, the earlier the peak moment and the higher the peak load. The battery state of the battery electric vehicle in the work area is 60 % at 1:00, and it reaches a relatively full state of 90 % at 8:00. In different regions, the allocation of charging thresholds for battery electric vehicles was different, and the Markov theory was derived from the analysis of the coverage problem to enable fast and effective siting of charging piles while satisfying charging convenience and high utilization of charging facilities to complement the research on the optimization model of battery electric vehicle charging facility layout.

Design of lateral dynamic control objectives for multi-wheeled distributed drive electric vehicles

The lateral dynamic control system for electric vehicles aims to improve vehicle performance, making it critical to propose reasonable lateral dynamic control objectives. Therefore, in this study, a novel lateral dynamic control objectives is proposed for multi-wheeled distributed drive electric vehicles. Initially, utilizing the classical two-degrees-of-freedom (2DOF) vehicle model, two initial yaw rate references were established for handling characteristics of manoeuvrability and stability, respectively. The yaw rate references consider the vehicle velocity, steering wheel angle and road friction coefficient limitations. Considering the vital role of the sideslip angle in ensuring vehicle stability, a sophisticated weighting factor was developed based on a detailed analysis of the vehicle dynamics within the sideslip angle-sideslip angle rate phase plane. The weighting factor was determined based on the position of the vehicle state in the phase plane. This factor was adeptly applied to harmonise our dual control objectives, culminating in an integrated control aim that encapsulates the individual benefits of each. Finally, the lateral dynamic control objective was realised using a direct yaw moment control (DYC) system. The effectiveness of the proposed control objective was validated through hardware-in-the-loop (HIL) simulations.

Research on Metal and Living Foreign Object Detection Method for Electric Vehicle Wireless Charging System

In the electric vehicle wireless power transmission system, the high-frequency alternating magnetic field between the transmitter and receiver can have a certain impact on the health of living organisms and may even lead to lesions. In addition, metal foreign objects in an alternating magnetic field can cause their own heating or even cause fires due to the eddy current effect, so foreign object detection is an essential function in the wireless power transmission system of electric vehicles. In order to prevent metals and living organisms from entering the charging area and causing harm to the charging system and living organisms, this paper proposes a method for detecting living organisms and metal foreign objects. Firstly, the equivalent circuits for the detection systems of the living organism foreign objects and metal foreign objects are established, respectively, and the working theory of the detection system is analyzed by deriving equations. Secondly, the comb capacitor simulation model was constructed, and the comb capacitor electrode spacing, wire thickness, and capacitor spacing were designed based on the scale factor γ to explore the effects of the height and bottom area of the living organism’s foreign object on the comb capacitor. We constructed a simulation model of the detection coil and designed the inner diameter D, the number of turns N, and the wire spacing S of the detection coil according to the scale factor β. An arrayed detection coil and comb capacitor combination mode is proposed to realize the function of the simultaneous detection of metal and living organism foreign objects, and a compensation capacitor is introduced to keep the detection system in a resonant state. Lastly, a platform for foreign object detection experiments was set up to detect metal screws and beef chunks compared to the detection area without foreign objects. Metal screws entering the detection area cause a 20% voltage drop in the detection circuit resistor, and beef chunks entering the detection area cause a 30% voltage drop in the detection circuit resistor, so the detection method is effective in detecting both metals and living organisms. The feasibility of the combined mode of arrayed detection coils and comb capacitors was verified.