Semi-online parameter identification methodology for maritime power lithium batteries

Abstract

Due to the special working environment of power lithium battery (P-LiB) on all-electric ships, the high-efficiency battery management system (BMS) is required. In this study, a novel semi-online parameter identification methodology integrated with large-scale global optimization algorithm is developed, in order to ensure the high-quality performance of subsequent BMS functions like the equalization control and state-of-charge estimation. Firstly, the P-LiB parameter identification model is established based on the first-order Thevenin equivalent circuit. Then, the evolution strategy of identification model is developed for dynamically updating the model along with the entire charging/discharging process of P-LiB. Considering that the model complexity increases exponentially with dimensionality, the AMCCDE algorithm developed in our previous work is employed to optimize the dynamic model repeatedly. Moreover, the semi-online operation mechanism for AMCCDE is proposed, in which the multiple context vectors are used to exchange information and inherit the optimal solution between each two adjacent semi-online cycles, and the identification solutions can be dynamically corrected and output at the end of each cycle. Finally, the developed semi-online identification methodology is verified using the USTC-DST and USTC-UDDS datasets. Experimental results show that the developed methodology can well balance the identification accuracy and timeliness, and dynamically output the accurate identification solutions in real-time.