Adaptive Multitimescale Joint Estimation Method for SOC and Capacity of Series Battery Pack

Aiming at the estimation timescale selection problem in the multistate joint estimation for the state of charge (SOC) and capacity at the cell level of the series battery pack, an adaptive multitimescale dynamic time-varying strategy (AMts-DtvS) is proposed. This strategy includes a triggered update strategy for the mean capacity of the battery pack, a time-varying polling update strategy for the differential SOC, and a triggered polling update strategy for the differential capacity. This strategy can adaptively adjust the timescale of multistate joint estimation throughout the entire lifecycle of the battery pack based on the operating conditions, the degree of consistency deterioration, and the change rate of capacity, achieving the goal of balancing complexity and estimation accuracy. Based on AMts-DtvS, an adaptive multitimescale H infinity filter (AMts-HIF) algorithm is formed to achieve joint estimation for cell SOC and cell capacity of the battery pack. In the experimental section, based on four different datasets, the proposed AMts-HIF is compared with three different fixed timescale series battery pack state estimation algorithms. Through comparative verification, it can be concluded that the proposed AMts-HIF based on AMts-DtvS can obtain comparable estimation accuracy with less computational complexity in the discharge natural temperature risk scenario, Li(NiCoMn)O2 battery natural aging scenario, and LiFePO4 battery natural aging scenario. In scenarios where capacity drop/consistency deterioration due to faults/low temperatures and so on, it is possible to obtain higher accuracy with comparable complexity.