Solid-State Batteries: Chemistry, Battery, and Thermal Management System, Battery Assembly, and Applications—A Critical Review

With the large-scale commercialization and growing market share of electric vehicles (EVs), many studies have been dedicated to battery systems design and development. Their focus has been on higher energy efficiency, improved thermal performance and optimized multi-material battery enclosure designs. The integration of simulation-based design optimization of the battery pack and Battery Management System (BMS) is evolving and has expanded to include novelties such as artificial intelligence/machine learning (AI/ML) to improve efficiencies in design, manufacturing, and operations for their application in electric vehicles and energy storage systems. Specific to BMS, these advanced concepts enable a more accurate prediction of battery performance such as its State of Health (SOH), State of Charge (SOC), and State of Power (SOP). This study presents a comprehensive review of the latest developments and technologies in battery design, thermal management, and the application of AI in Battery Management Systems (BMS) for Electric Vehicles (EV).

Lithium-ion (Li-ion) batteries are proven to be the best type of battery in the market especially for electric vehicle (EV) applications. However, Li-ion battery must be monitored properly to prevent operation outside the safe operating area (SOA). In using the Li-ion battery, the voltage, current and SoC must be monitored properly to avoid overvoltage, undervoltage, overcurrent, undercurrent, overcharge and overdischarge scenarios which could be harmful to both the Li-ion battery and to the users. Especially in dynamic systems such as EV in which the load supplied by the battery is not constant and could vary drastically. Thus, battery management systems (BMS) are often designed to perform such monitoring functions. BMS must be designed well to fit a specific application. In this study we design a battery management system that is capable of battery voltage and current monitoring, and SoC estimation. For SoC estimation we propose a point-to-point estimation technique which utilizes machine learning algorithm to estimate the SoC consumed as the EV is driven from one point to another.

The Battery Management System (BMS) serves as the heart of the electric vehicle system, in which estimating the state of charge (SOC) is the crucial part of the BMS to ensure the durability, reliability, and sustainability of the battery pack. Due to its nonlinear characteristics, accurately estimating the SOC for a slow degradation of the charge is highly cumbersome. The literature provides a series of machine learning algorithms (MLA) to estimate and predict the SOC of lithium-ion (Li-ion) battery systems for electric vehicle (EV) applications. The literature has proposed various MLA, coulomb counting, and different Kalman filter methods to address this challenge and estimate the SOC of Li-ion battery systems for EV applications. This research looks at the differences and similarities between the coulomb counting method, the unscented Kalman filter method, and a number of machine learning algorithms. These include linear regression, polynomial linear regression, support vector regression, decision trees, random forests, artificial neural networks (ANN), and long short-term memory (LSTM). The goal is to assess the MLAs’ accuracy in estimating battery SOC. Analyzing model errors optimizes the battery’s performance parameter. We identify ANN and LSTM as the two most efficient methods for accurate SOC estimation in an EV-operated BMS system by evaluating the performance indices of the aforementioned machine learning methodologies. Once again, the LSTM model for SOC estimation has proven to be highly accurate in analyzing the discrepancy between the actual and predicted traveling ranges of the designed prototype. We design a MATLAB/SIMULINK EV powertrain by collecting real-time data from the Li-ion battery pack, analyzing the SOC variation data, and using the previously mentioned MLA in the Python platform to estimate the SOC and its accuracy. It highlights the effectiveness of advanced MLAs in improving SOC estimation, thereby enhancing the performance and reliability of EV battery systems.

Electric Vehicle technology plays an important role in greenhouse gas limitation and carbon pollutions. Rechargeable batteries are used to deliver power to the auxiliary systems and motor in the electric vehicle applications. Various classifications of rechargeable batteries are available nowadays, for electric vehicle applications. Among all rechargeable batteries, Lithium Ion Batteries will give high efficiency for electric mobility because Li-Ion batteries have low self-discharge rate, wide operating range, maximum energy density and high life cycle. To improve the quality of battery and safe operation, battery management system is employed and it plays a vital role in application of Electric Mobility. This paper reviews the attributes of battery management system and its technology with advantages and disadvantages for electric vehicle application. This review includes the battery cell monitoring, state estimation, charging and discharging control, temperature control, fault analysis, data acquisition and protection schemes to improve the performance of battery for EV applications.

Lithium-ion battery packs are essential to the electrification of cars, especially electric vehicles (EVs), as they provide the required energy storage for longer driving distances and improved performance. The performance and design trends for Li-ion battery packs for electric vehicles are presented in this article in detail. This review paper examines the contemporary movements in the boundaries of Li-ion battery technology for EV applications, which involve a range of factors, such as design specifications of a battery pack and other safety measures, the importance of battery management systems (BMS), and thermal management systems (TMS). The design levels of Li-ion battery packs are discussed based on a selection of the right cells and application criteria, which include the weight of the battery in an electric vehicle (EV) and the placement of the battery in the vehicle, which can affect the handling and power efficiency of the vehicle. Moreover, the Li-ion battery intelligence system, referred to as the Battery Management System (BMS), is extensively discussed, including its architecture, functions, and conventional and advanced optimized algorithms to protect and mitigate battery failures. The reliability of precise estimation of state-of-charge and cell balancing is investigated, which integrates with artificial intelligence and machine learning techniques. In this article, the temperature consequences of Li-ion batteries during internal and external fault operating conditions investigated, and various advanced battery thermal management systems emphasized to prevent thermal runaway in EV applications. The research results provide a roadmap for academics, engineers, and other industry participants to comprehend and traverse the rapidly developing field of battery technology, fostering the development of effective, secure, and environmentally friendly energy storage options for electric vehicles.

Presently electric vehicles (EVs) are considered as most propitious solution for the replacement of internal combustion (IC) engine-based vehicle. The development of EV technologies is growing rapidly and the battery technology is an important concept for development of the electric vehicles. The EV performance mainly relies on the battery performance and battery management system (BMS). Recently, the Lithium-ion (Li-ion) battery is mainly used as a battery in EVs due to smaller weight, high energy density and capability of fast charging and discharging. Considering the dynamic performance, economy, safety friendliness to the environment of the EVs, the BMS is designed such a way to meet the challenges like the energy management of battery, reduction of heating-time at low temperature and enhancing remaining-useful life (RUL) with accuracy of prediction. The battery is managed and controlled by BMS and it is mainly focused to maintain the reliability and safety. The state estimation of the battery is essential for vehicle control and management of energy. The paper gives review on the strategies like battery modeling, state estimation and prediction. The state estimation for State of charge (SOC), State of power (SOP), State of health (SOH) and prediction of RUL are overviewed.

The nonlinear features of lithium-ion batteries make the lifetime performance, reliability assessment, and control of the battery more difficult. The battery management system (BMS) has been known as a key system for monitoring, controlling, and improving the lifespan and reliability of the Li-ion battery from the cell to pack levels in electric vehicles (EVs). To improve the abovementioned issue, the BMS should control and monitor the current, voltage, and temperature of the battery system during the lifespan of the battery. In this chapter, the BMS definition, SoH and SoC methods, and battery fault detection methods have been described as key aspects of the control strategy of Li-ion batteries for improving the reliability of the system. Moreover, the challenges and further work relating to the estimation of the state of function of the Li-ion batteries for EV applications have been investigated.KeywordsLithium-ion (Li-ion) batteries Electric vehicles (EVs)Battery management system (BMS)

Transportation is a significant contributor to the emission of greenhouse gases, primarily due to the combustion of fossil fuels. These emissions are pivotal in driving global warming and climate change. In response, significant research and development efforts are being directed towards sustainable commercial products and transportation solutions. Electric Vehicles (EVs) are at the forefront of this shift, leveraging electric power to operate, which is provided by advanced battery systems. This paper delves into the characteristics and technological advancements of Lithium-Ion (Li-Ion) batteries, their role in the operation of EVs, and the critical functionality of Battery Management Systems (BMS). We explore the bidirectional power flow in EVs during operation and braking, the categorization of EVs, and the implementation of BMS to enhance battery performance and safety. This comprehensive review compares different battery technologies with a focus on Li-Ion batteries due to their high energy density, low self-discharge rate, and minimal memory effects, and discusses the management techniques essential for optimizing EV applications.

Transportation is a significant contributor to the emission of greenhouse gases, primarily due to the combustion of fossil fuels. These emissions are pivotal in driving global warming and climate change. In response, significant research and development efforts are being directed towards sustainable commercial products and transportation solutions. Electric Vehicles (EVs) are at the forefront of this shift, leveraging electric power to operate, which is provided by advanced battery systems. This paper delves into the characteristics and technological advancements of Lithium-Ion (Li-Ion) batteries, their role in the operation of EVs, and the critical functionality of Battery Management Systems (BMS). We explore the bidirectional power flow in EVs during operation and braking, the categorization of EVs, and the implementation of BMS to enhance battery performance and safety. This comprehensive review compares different battery technologies with a focus on Li-Ion batteries due to their high energy density, low self-discharge rate, and minimal memory effects, and discusses the management techniques essential for optimizing EV applications.

Electric vehicles (EVs) are becoming more popular and have gained better customer acceptance in the past few years due to the improved performances, such as high acceleration rate and long driving distance from a single charging. Recent research also shows some promising benefits from integrating EVs with power grid. One of these is to use EV batteries as distributed energy storage. As a result, the excessive electricity generated from renewable resources can be stored in EVs and release to the power grid when needed. However, compared to traditional Nickel-cadmium and lead-acid batteries, Li-ion battery only can be operated in a narrow window, and needs to be properly monitored, managed and protected. This issue becomes severe when it is deployed for large applications, such as EVs and centralised electricity storage, where a large number of Li-Ion cells are interconnected to provide sufficient voltage and current. The solution mainly relies on a robust and efficient battery management system (BMS). This paper presents a brief review on the features of BMS, followed by a practical guide on selecting a commercial BMS from the market and designing a custom BMS for better control of functionalities. A Lithimate Pro BMS from Elithion is used to demonstrate the effectiveness of BMS in managing and protecting Li-Ion cells during the charge and discharge phases.

Electric Vehicle (EV) technology has groomed the automotive industry and is becoming a more reliable source of mass transit. These battery-based vehicles are performing extraordinarily in developing pollution-free environment and better ecological surroundings. Battery Management System (BMS) is the backbone of the EVs that can undoubtedly increase the performance, operation, safety and lifetime of a vehicle’s battery or storage system. Li-ion batteries are performing outclass to provide a more sophisticated storage system for EVs. They have better characteristics than lead-acid or other types of batteries, hence being highly utilized in EV applications. This paper presents the comprehensive review of the BMS for the EV’s. It gives a detailed overview of the Battery Management System (BMS) for the EVs and detailed study of important aspects which comes under consideration during management of batteries. It further provides key factors and extrinsic issues e.g., battery modeling, state estimations and validation profiles etc. which are troubling in the proper and safe operation of the batteries in EV’s, and the algorithms and schemes designed and proposed for their betterment are presented.

Research in the field of Electric Vehicles (EV) is significantly increasing. EV is powered by the battery pack. Therefore, an improvement in the accuracy of the battery model is required. Batteries have several operational challenges, which required a proper Battery Management System (BMS) to achieve optimal performance. This paper provides an analysis of EV batteries and a study on battery management systems in EV applications. This review focuses on many issues, challenges, and problems of Electric Vehicles. This review enhances the development effort of the advance Battery Management system. The purpose of modelling is to detect internal variables such as state of charge (SOC) and internal resistance, calculated based on external variables such as battery voltage, current, and temperature. BMS is responsible for safe operation, performance and quality of life under various charge/discharge, and environmental conditions. When designing a BMS, cell voltage, temperature, state of charge (SOC), state of health (SOH) should be controlled and maintained. Accurate battery models are required for the proper design and operation of battery-powered systems. This paper shows a battery model based on the Simulink and simscape of MATLAB, to build and parameterize the Li-ion battery model. The performance of the battery model is tested in the Urban Dynamometer Driving Schedule (UDDS) cycle. The test results obtained from a Li-ion polymer battery were compared with data provided for the models.

Development of battery management system for nickel–metal hydride batteries in electric vehicle applications

Intelligent algorithms and control strategies for battery management system in electric vehicles: Progress, challenges and future outlook

A critical review of battery cell balancing techniques, optimal design, converter topologies, and performance evaluation for optimizing storage system in electric vehicles