A novel deep learning framework for state of health estimation of lithium-ion battery

•A deep learning model is proposed for SOH estimation.•The charging curves are directly taken as model inputs.•A hybrid network is introduced to take advantage of both CNN and GRU-RNN.•The method is proven to be accurate by the NASA dataset and the Oxford dataset. The state-of-health (SOH) estimation is a challenging task for lithium-ion battery, which contribute significantly to maximize the performance of battery-powered systems and guide the battery replacement. The complexity of degeneration mechanism enables data-driven methods to replace mechanism modeling methods to estimate SOH. The insight that motivates this study is that the charging curve of constant current-constant voltage charging mode could reflect the magnitude of SOH from the perspective of capacity. The proposed approach is based on a hybrid neural network called gate recurrent unit-convolutional neural network (GRU-CNN), which can learn the shared information and time dependencies of the charging curve with deep learning technology. Then the SOH could be estimated with the new observed charging curves such as voltage, current and temperature. The approach is demonstrated on the public NASA Randomized Battery Usage dataset and Oxford Battery Degradation dataset, and the maximum estimation error is limited to within 4.3%, thus proving its effectiveness.