Abstract
Lithium-ion battery is an appropriate choice for electric vehicle (EV) due to its promising features of high voltage, high energy density, low self-discharge and long lifecycles. The successful operation of EV is highly dependent on the operation of battery management system (BMS). State of charge (SOC) is one of the vital paraments of BMS which signifies the amount of charge left in a battery. A good estimation of SOC leads to long battery life and prevention of catastrophe from battery failure. Besides, an accurate and robust SOC estimation has great significance towards an efficient EV operation. However, SOC estimation is a complex process due to its dependency on various factors such as battery age, ambient temperature, and many unknown factors. This review presents the recent SOC estimation methods highlighting the model-based and data-driven approaches. Model-based methods attempt to model the battery behavior incorporating various factors into complex mathematical equations in order to accurately estimate the SOC while the data-driven methods adopt an approach of learning the battery's behavior by running complex algorithms with a large amount of measured battery data. The classifications of model-based and data-driven based SOC estimation are explained in terms of estimation model/algorithm, benefits, drawbacks, and estimation error. In addition, the review highlights many factors and challenges and delivers potential recommendations for the development of SOC estimation methods in EV applications. All the highlighted insights of this review will hopefully lead to increased efforts toward the enhancement of SOC estimation method of lithium-ion battery for the future high-tech EV applications.
Highlights
The battery energy storage system (BESS) has been progressing speedily for the last decades due to the rapid growth of renewable energy-based power generation, development of smart grid technology, expansion of electric vehicle (EV) production and reduction of CO2 emission [1], [2]
The results demonstrate that the Adaptive neuro-fuzzy inference system (ANFIS) model is dominant to back propagation neural network (BPNN) and Elman neural network with State of charge (SOC) error below 1% in diversified drive cycles
The results show that the proposed method is robust and has achieved promising outcomes in comparison with common BPNN and radial basis function neural network (RBFNN) methods with root mean square error (RMSE) being less than 1% under diversified EV drive cycles
Summary
The battery energy storage system (BESS) has been progressing speedily for the last decades due to the rapid growth of renewable energy-based power generation, development of smart grid technology, expansion of electric vehicle (EV) production and reduction of CO2 emission [1], [2]. The data-driven approach relies extensively on analyzing data from the process; it does not require practitioners to develop a deep, domain-specific understanding of the background process [44] This approach may be useful to develop a SOC estimation model with limited prior information about battery internal characteristics and chemical reactions. In this light, the data-driven approach requires lesser time and knowledge to model a complex system compared to the model-based approach. LSTM can examine SOC precisely by only monitoring battery measurements such as current, voltage and temperature, does not require information about battery internal chemistry, complex reactions, and model parameters estimation [46]. The upcoming section reviews some of the most recent and prominent SOC estimation methods
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