Incremental Capacity-Based Multifeature Fusion Model for Predicting State-of-Health of Lithium-Ion Batteries

Lithium-ion batteries have become an indispensable part of human industrial production and daily life. For the safe use, management and maintenance of lithium-ion batteries, the state-of-health (SOH) of lithium-ion batteries is an important indicator, so that the SOH estimation is of significant practical value. To accurately predict SOH, this article proposes a fusion prediction model that combines the particle swarm optimization (PSO) algorithm, bi-directional long short-term memory network (BiLSTM), and adaptive boosting (AdaBoost) algorithm. In the proposed prediction model, indirect health indicators (HIs), which characterize battery degradation, are obtained with the help of incremental capacity analysis (ICA) and are fed into BiLSTM to extract time-series features, whose parameters are optimized by employing the PSO algorithm. On this basis, the AdaBoost algorithm is applied to reduce the risk of overfitting the PSO–BiLSTM model. The study based on lithium-ion battery data from the Center for Advanced Life Cycle Engineering (CALCE) shows that the PSO–BiLSTM–AdaBoost model has higher accuracy, better robustness, and generalization ability.