BATTERY MANAGEMENT SYSTEM FOR ELECTRIC VEHICLES

The demand for electric vehicle is growing rapidly on a global scale, in order to solve the concerns of carbon emissions and global warming. Presently, electric vehicles are the most environmental friendly option for both personal and public transportation. The vehicle industry has made significant progress in recent years towards improving the safety of passengers, due to several technical developments. However, the increased number of automobiles on the road is responsible for rise in pollution levels in metropolitan areas. According to the European Union, the transport industry is responsible for around 27% of all CO2 emissions, out of which vehicle transportation is responsible for more than 70%. Electric vehicles (EVs), which can reduce environmental pollution, conserve fossil fuels, reduce carbon emissions and address global warming challenges have gained a lot of attention.

The development of EV and hybrid electric vehicle (HEV) technology has accelerated in recent years. EVs and HEVs are commonly recognised as the most promising alternatives to traditional internal combustion engine-based vehicles. Due to high energy density, low environmental pollution and long cycle life, batteries are frequently used as the power source for EVs and HEVs. However, in EV applications, batteries need to be handled with special caution. A battery’s ageing process will be noticeably speed up by improper operations like over-current, over-voltage, or over-charging/discharging and can also result in fire or explosion.

Lithium-ion batteries currently have a leading position in the EV battery industry, as it has high power and energy densities, high voltage, long life cycles and low self-discharge rates. In order to monitor battery current, voltage and temperature; analyses battery charge, energy and health; equalise voltage among cells, control temperature and pinpoint the problem, a battery management system (BMS) is required. Battery modelling, internal state estimates and battery charging are important technologies in EV BMS. In battery behaviour analysis, battery condition monitoring, real-time controller design, temperature management and for fault diagnostics, an appropriate battery model is essential.

Several internal battery states, such as state of charge (SOC), state of health (SOH), and internal temperature, cannot be detected directly yet are crucial for monitoring the functioning of batteries, they must be monitored using accurate estimation techniques. The availability of EVs is negatively impacted by slow charging, whereas charging too quickly can result in significant energy waste and temperature increase. A battery’s service life will eventually be reduced by overheating or super cooling, which is caused by large temperature variations. Main functions of an effective BMS are maintaining battery temperatures within the safe range, performs fault diagnosis, fault prognosis, fault handling, balancing the voltage, charge and capacity among battery cells, correctly estimating and assesses battery statuses such as state of charge, state of energy, state of health and remaining useful life.

A good BMS is essential in maintaining the safe and dependable operation of batteries, which have been widely used in in a variety of high-power applications such as electric vehicles and hybrid electric vehicles. However, inadequate battery storage system monitoring and safety measures can result in serious problems such as battery overcharging, deep-discharging, overheating, unbalanced cells, thermal runaway and dangerous fire.It is necessary to have precise monitoring, charging-discharging control, heat management, battery safety and protection. This book contains a wealth of information about BMS and its application in EVs. It covers a wide range of topics, and after reading it, readers will have a good grasp of understanding about BMS, fundamental ideas; this book can be a great resource to for the beginners who are starting their research studies on BMS. The information in this book is extensive, and it is written such that everyone may understand it. The goal of this book is to provide a solid foundation for people looking to learn about basics of battery managements system towards electric vehicle applications.

The BMS in EVs consists of a wide range of circuits, parts, power electronics, sensors, actuators, diodes, capacitors, inductors, transformers, switches, converters and safety equipment all of which are managed by a wide range of algorithms, models and control signals. The creation of the proper algorithms for BMS has been the subject of extensive research. Model-based and intelligent methods are two of the methods which are used in the BMS most frequently. The complex, dynamic and nonlinear features of lithium-ion batteries can be addressed by the intelligent algorithm-based methods.

This book is an outcome of our experience of conducting research activities on BMS for EVs. It presents and discusses about the most recent innovations, trends, concerns as well as practical challenges encountered and solutions adopted in this field.It can be useful for researchers, practitioners and educators. This book includes a total of fourteen chapters. It covers contents about introduction, architecture and functionalities of BMS, battery modelling, SOC estimation, SOH estimation, battery internal temperature estimation, battery remaining useful life prediction, thermal management system, cell balancing techniques, battery charging techniques, battery fault diagnosis, standards for battery monitoring and management, commercially available BMS products, and concluding remarks, future outlook and research directions.

A thorough evaluation of the usefulness of adaptive methods for precise battery state estimates, including SOC, SOH, RUL and SOH are carried out. Different controller designs for battery balancing, fault finding, and thermal management were specifically reviewed. The main problems and difficulties with intelligent controllers and algorithms for BMS were explored. The BMS was given specific future paths for improvement in order to increase accuracy, adaptability and robustness. The different intelligent techniques of SOC, SOE, SOH and RUL estimation in BMS are thoroughly examined in this paper. According to feedforward algorithms, time-series based learning, hybrid optimization algorithms, and statistical algorithms, the intelligent algorithms are categorised. Accordingly, their configuration, characteristics, estimated error, advantages, flaws and research gaps are given. The various controller’s roles in battery pack equalisation, fault diagnostics and thermal management are described, with emphasis on the types, traits, goals, contributions, strengths and weaknesses of each. In-depth discussion is given to the different current problems and obstacles related to algorithm complexity, implementation issues, data variety, data integrity, structure, and uncertainty. The analysis concludes with some insightful recommendations for BMS’s potential future growth and possibilities.