A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations
Introduction
The world is moving towards some serious consequences such as global warming, greenhouse gas (GHG) emission caused by extensive use of diesel, petrol in vehicle operation, which emits tons of CO2 every year [1], [2], [3]. Besides, the rising crude oil price also causes serious setback of the automobile industry and urges the necessity to develop alternative fuel-driven vehicles. To address the problems, the implementation of EV has gained huge attention and become attractive choices for academic researchers and automobile specialists due to their promising features in reducing GHG [4], [5], [6], [7].
Implementation of rechargeable battery in EV application has become very popular in recent years [8], [9], [10] since renewable energy sources such as solar energy, wind energy, are intermittent in nature and could not be applicable where continuous and reliable supply is required [11]. Various energy storages, such as lead acid, NiMH, lithium-ion batteries have been used in an EV [12]. Among them, lithium-ion battery is widely accepted due to its high energy density, long lifespan and high efficiency [13], [14]. Because of its lucrative features, a lot of investments have already been made to enhance the stability and robustness of lithium-ion battery [15]. Even though of high primary cost, market growth of lithium-ion battery has been increasing steadily and is expected to continue its growth [16].
An effective BMS using the lithium-ion battery is compulsory so that battery can operate safely and reliably, prevent any physical damages, and handle thermal degradation and cell unbalancing [17], [18]. Moreover, different states of the battery such as the SOC, state of health (SOH) can be assessed through an efficient battery management system, which can sense temperature, measure voltage and current, regulate safety alarm to avoid any overcharging/over discharging. Furthermore, a BMS is essential for controlling and updating data, detecting faults, equalizing battery voltage that are the important factors for achieving a good accuracy of SOC and SOH.
SOC in battery management system is considered as one of the critical and important factors, which have been researched in recent decades. Battery SOC does the similar operation of the fuel gauge in a gasoline-driven vehicle which indicates how much energy is left inside a battery to power a vehicle [19]. Accurate estimation of battery states not only helps to provide information about the current and remaining performance of the battery but also gives assurance of a reliable and safe operation of the EV. However, battery SOC estimation is one of the main challenges for the successful operation of EVs. Due to non-linear, time-varying characteristics and electrochemical reactions, battery SOC cannot be observed directly [20]. Furthermore, the performance of the battery is highly affected by aging, temperature variation, charge-discharge cycles which make the task of estimating an accurate SOC very challenging [21].
Very few literature have been found which provide a detailed explanation of all the methods to estimate SOC of EV [22], [23], [24], [25]. The literature has demonstrated some common methods to estimate SOC; however, each method has shortcomings in terms of accuracy and lack of data. In addition, complex calculation and high computation cost are the others concerns which make the estimation process very difficult. Hence, the academics, researchers, scientists have performed an extensive research to enhance the accuracy of battery SOC. Nevertheless, the issues in estimating an accurate SOC have not resolved yet. Besides, the challenges in estimating the SOC have not been identified. Thus, this research paper fills up the gap by exploring different existing methodologies and addressing the key issues and challenges for the estimation of SOC. This research will be very helpful for the automobile manufacturers and engineers in terms of deciding the appropriate method and identifying challenges.
This paper briefly discusses the lithium-ion battery state of charge estimation and management system in EV applications. The main concern is to develop an efficient SOC estimation method/algorithm of lithium-ion batteries. In addition, there are some issues and challenges regarding its estimation methodologies. This paper reviews the published articles to gain knowledge on SOC estimation methods in order to propose the most efficient model/algorithm. A detailed SOC estimation methods with its benefits and drawbacks is briefly elaborated. The issues and challenges of implementing various SOC methods along with possible solutions are also addressed to provide information and knowledge to the vehicle manufacturer. This knowledge will be important for future development of implementing new SOC methods or upgradation of earlier SOC methods.
Section snippets
Status of lithium-ion battery
There are many energy storages, such as lead acid, NiMH, lithium-ion batteries, which have been used widely for EV application. However, among them, lithium-ion batteries have been an attractive choice among automobile engineers in spite of its high capital cost [26]. Due it its promising performance in the application of automobile, cellular phone, notebook computers [27], a significant research and development have been performed to enhance the performance of lithium-ion batteries in terms of
Overview of battery management systems (BMS)
Since the lithium-ion battery is effective and efficient in achieving better performance during their long lifespan, special attention must be paid to their operating conditions to avoid any physical damage, aging and thermal runaways. Therefore, there is an urgent need to build an efficient BMS, which can precisely measure, estimate and regulate the battery SOC.
Presently, BMS has been widely used by various automobile companies, colleges and universities. BMS products have been developed by a
State of charge (SOC)
There has always been a big concern to estimate the SOC for all energy storage devices. SOC estimation with high accuracy not only gives us information about remaining useful energy, but also it evaluates the reliability of batteries. In addition, an accurate and efficient SOC estimation gives an idea about charging/discharging strategies, which have a significant impact on battery application where each cell may have different capacities due to aging, temperature, self-discharge and
SOC estimation methods
Different kinds of literature have presented the classification of SOC in a different manner. This paper divides the SOC estimation methods into the five categories, which are shown in Fig. 5. The conventional method uses the physical properties of the battery, which includes voltage, discharge current, resistance, and impedance. The adaptive filter algorithm uses various models and algorithms to calculate the SOC. The learning algorithm requires a large amount of training data and heavy
Issues and challenges
Developing and deploying lithium-ion battery management with SOC estimation in EV application has become major challenges due to its complicated electro-chemical reactions and performance degradation over time caused by various internal and external factors. Furthermore, most of the defined experiments of the battery are conducted in a laboratory environment with standard voltage, current limits, and low-temperature variation. However, very few research have been found on battery operating in
Conclusion and recommendations
The lithium-ion battery management system with a focus on various methods to estimate SOC and highlight related challenges in EV applications are critically reviewed in this paper. The lithium-ion battery is highly recommended for vehicle operation because of its high voltage generating capability, long life cycle, and high energy density. The paper also describes lithium-ion battery mechanism and configuration. The importance of battery management system (BMS) is explained for achieving safe
Acknowledgement
The authors gratefully acknowledge Universiti Kebangsaan Malaysia for the financial support under research grant DIP-2015-012.
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