Current pulses are convenient to be actively implemented by a Battery Management System (BMS). However, the Short-Term Features (STF) from current
pulses originate from various sensors with uneven qualities, which hinders one powerful and strong learner with STF for the battery SOH estimation. This paper thus proposes an optimized weak learner formulation procedure for Lithium-ion (Li-ion) battery SOH estimation, which further enables the automatic initialization and integration of the weak learners with STF into an efficient SOH estimation framework. A Pareto Front-based Selection Strategy (PFSS) is designed to select the representative solutions from the non-dominated solutions fed by a Knee point driven Evolutionary Algorithm (KnEA), which guarantees both the diversity and accuracy of the weak learners. Afterwards, the weak learners, whose coefficients are obtained by Selfadaptive Differential Evolution (SaDE), are integrated by a weight-based structure. The proposed method utilizes the weak learners with STF to boost the overall performance of SOH estimation. The validation of the proposed method is proved by LiFePO4/C batteries under accelerated cycling ageing test including one mission profile providing Primary Frequency Regulation (PFR) service to the grid and one constant current profile.
Automatic weak learner formulation
lithium-ion (Li-ion) battery
state of health (SOH) estimation