SOH uncertainty estimation for lithium-ion battery packs based on cell inconsistency

In recent years, lithium-ion battery packs are widely used in several fields. State of health (SOH) of lithium-ion battery packs is a key parameter for evaluating the degradation of their performance. For battery packs, in addition to internal cell degradation, cell inconsistency also has a significant impact on the aging process, leading to more complex degradation and greater uncertainty in the SOH estimation. This paper extracts degradation features based on cell inconsistency and performs uncertainty modelling of battery pack SOH. Six single degradation parameters based on cell inconsistency are extracted using parameters such as monitorable voltage and time interval. In order to reduce the information redundancy of single degradation features and improve the features accuracy, the degradation features are fused using the kernel principal components analysis (KPCA) algorithm. The SOH estimation model of the battery pack is established based on long short-term memory (LSTM) network. The model utilizes a parallel training strategy to quantify the uncertainty in the estimation results. The experimental results based on real battery test data show that the correlation between degradation features and battery capacity is between 0.98 and 0.99, and the error of SOH estimation is less than 0.06, and 95% confidence interval is given.