State of charge estimation of Li-ion batteries based on deep learning methods and particle-swarm-optimized Kalman filter

The estimation of SOC is a key issue for the high-efficient and reliable operation of Li-ion batteries, thus has been increasingly concerned in current years with the development of electric vehicles. During dynamic test cycles, the accurate estimation of SOC is more difficult than steady operation conditions due to the fierce oscillations of the input signals. In this paper, a hybrid method of deep learning method and Kalman filter was proposed for the estimation of SOC. First, convolutional neural network or temporal convolutional network was combined with different variants of recurrent neural network, including long short term memory, gated recurrent unit, peeple hole long short term memory and bidirectional long short term memory, to achieve the estimation of SOC by capturing the spatial and temporal characteristics of input signals. Afterwards, the deep learning method was integrated with Kalman filter to eliminate the effects of transient signal oscillations and further improve the accuracy for the estimation of SOC. The results indicated that estimation accuracy and estimation time could be improved by less than 20 % by varying deep learning methods while after integrating deep learning method with Kalman filter, more than 45 % improvements in test accuracy could be achieved without obvious sacrifices in estimation time. •A hybrid SOC estimation model based on deep learning method and Kalman filter was proposed.•The proposed method was evaluated at transient test cycles with fierce oscillations of input signals.•The performance of different deep learning frameworks was compared in views of accuracy and computational efficiency.•The effects of Kalman filter were elaborated at different test cycles and temperatures.