Jitter solution in parameter identification based on cross-time scale fusion algorithm of lithium-ion batteries

Abstract

Accurate state-of-charge (SOC) estimation is the core index of battery management system (BMS). When the battery equivalent circuit model (ECM) identifies the parameters under complex operating conditions, there is more jitter or even divergence, which will affect the estimation accuracy of battery SOC. To solve this problem, this paper proposes a new algorithm, namely the cross time scale fusion (CTSF) algorithm. Firstly, the cross-time scales Δt1 and Δt2 are determined, the number of cross-time cycles is calculated according to the total amount of complex operating condition data N. Then the ECM parameters are identified in Δt1 by using forgetting factor recursive least square (FFRLS), and the battery SOC is estimated in Δt2 based on the identified parameters, finally the battery parameters are identified and the SOC is estimated by cycling in the cross-time. The experimental results show that, no matter at the same temperature in different conditions or at different temperatures in the same condition, The proposed algorithm not only effectively solves the ECM parameter identification jitter problem, but also improves the accuracy of SOC estimation, the Mean Absolute Error (MAE) minimum of SOC result is 1.42% for different operating conditions at the same temperature and 0.25% for different temperatures at the same operating conditions, respectively.

Keywords

• Cross-time scale fusion algorithm
• Parameter identification
• SOC accuracy
• Jitter