ABSTRACT
The chapter provides a comprehensive review of state estimation techniques specifically applied to lithium-ion batteries used in electric vehicle (EV) applications. State estimation refers to the process of determining key battery parameters, such as state of charge (SOC), state of health (SOH), and remaining useful life (RUL), which are crucial for predicting battery behaviour and ensuring optimal performance. The chapter explores the design methods for state estimation and covers electrochemical models, which describe the complex electrochemical processes occurring within the battery, as well as equivalent circuit models, which provide a simplified representation of the battery’s behaviour. Furthermore, the chapter addresses the challenges associated with state estimation techniques for lithium-ion batteries. These challenges include model complexity, parameter identification, and real-time implementation requirements. The chapter proposes potential solutions to overcome these hurdles, taking into account advancements in modelling techniques, parameter estimation algorithms, and computational capabilities. Moreover, the chapter explores future trends in state estimation for lithium-ion batteries in EVs. It discusses the potential use of machine learning techniques, which have shown promise in improving the accuracy of state estimation. Sensor fusion, combining data from multiple sensors to enhance estimation accuracy, is also discussed as a potential avenue for future research. These emerging trends have the potential to significantly impact battery performance and safety in EV applications.
