Review on Li-ion Battery Parameter Extraction Methods

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