A novel data-driven method for online parameter identification of an electrochemical model based on cuckoo search and particle swarm optimization algorithm

Previous studies have successfully applied empirical and equivalent circuit models (ECM) in battery management system (BMS) to perform highly accurate state estimation and other critical functions. However, these models struggle to meet the increasingly harsh requirements from modern electric vehicles, such as timely fault diagnosis that may require high-precision and detailed knowledge of battery cells. This paper starts with a comprehensive review to identify potential of applying electrochemical model, followed by proposed methods to solve problems induced from practical applications including data sampling precision and frequency. Considering the distribution of discharge currents in real driving scenarios, an improved Single Particle Model is introduced to simplify the complex electrochemistry model, and its practical applicability is verified. Moreover, the impact of sparse data collection frequencies under real vehicle conditions on parameter sensitivity is investigated, on top of which a novel optimization algorithm that combines Cuckoo Search with Particle Swarm Optimization has been proposed, facilitating grouped identification based on sensitivity analysis. The results of the virtual battery simulation verification indicate that the average absolute error of the algorithm in identifying parameters is 4.013%. The verification with actual vehicle data shows that the voltage fitting error of the algorithm is 13.05 mV.