Review on Li-ion Battery Parameter Extraction Methods

Electric batteries have gained attention with recent developments in the transport sector, especially with electric vehicles (EVs) technology and with the rapid development in the energy storage sector with application to the electricity grid. Lithium-ion batteries (LIBs) are particularly popular due to their high-power density, high energy density, low self-discharge rate, and performance. LIB systems are also widely utilized in extreme operating conditions and harsh environments, and the safe operation of any battery management system requires rapid detection and accurate diagnosis of faults. To have an effective fault diagnosis, the nonlinear behavior of battery systems has been studied in considering the battery real-time operation. In addition, accurate battery models are used to mimic battery physical processes and predict aging. The knowledge of battery model parameters plays a crucial role in accurately predicting performance and ageing. This paper critically reviews different battery models, such as electrochemical models, equivalent circuit models, and data-driven models. Then, the parameter extraction methods for the electrochemical model were discussed critically since it has been identified as the most promising battery model and also the techniques for the other battery models may rely on these approaches as they can be derived based on the electrochemical model parameters. According to the literature parameter estimation for electrochemical models was discussed under the categories of online, offline, and analytical methods. By the state-of-the-art review conducted, it has been identified that the mixed method that combines the online and offline methods shows good performance compared to using them separately. This paper also discusses some future research directions to obtain better parameter extraction methods for electrochemical models to facilitate battery fault diagnosis.

Automated summary

Electric batteries, especially lithium-ion batteries, have gained attention due to their high-power density, high energy density, low self-discharge rate, and performance. This paper critically reviews different battery models and discusses parameter extraction methods to accurately predict performance and aging. The mixed method that combines online and offline methods shows good performance for parameter estimation.