The State of Health (SOH) of a battery is an important indicator for measuring the performance degradation of batteries. In view of the deficiencies of existing SOH estimation methods in feature processing and model accuracy, this paper conducts research on high-precision SOH estimation methods for lithium-ion batteries. A BiLSTM model optimized by the Sparrow Search Algorithm (SSA) is adopted for SOH estimation. The SSA-BiLSTM model is constructed, and the experiments are conducted on multiple types of battery datasets, such as NCM811 and LFP, and the cross-validation strategy is used to evaluate the model’s performance. The experimental results show that the SOH prediction system software developed based on this model has the functions of rapid estimation and three-dimensional trend visualization. The paper verifies the functions of the SOH prediction system software developed by the model, which has practical reference significance for the development and application of SOH estimation systems in energy storage scenarios.
Wu et al. (Tue,) studied this question.