Abstract:

The direct measurement of the state of charge (SOC) for lithium-ion batteries, whose physical characteristics or the electrochemical characteristics are highly complex, presents difficulty. Methods, such as deep neural networks, have recently received wide attention and have been exploited to estimate battery SOC. To improve the estimation performance of SOC, capture accurately the interior dynamic characteristics of the batteries, and alleviate the vanishing and exploding gradient problem in neural networks, in this paper, we introduce a bidirectional learning strategy and develop two specific methods, i.e., the estimation of the SOC with the bidirectional long short-term memory (Bi-LSTM) networks and the bidirectional gated recurrent unit (Bi-GRU) networks. Both the proposed bidirectional networks consist of three parts: the input, hidden, and output layers. The input layer accepts the sequence of measurements, such as the voltages, currents, and temperatures. The hidden layer consists of two LSTM/GRU sub-layers; one processes the forward information, and the other processes the backward information to learn the input-output sequence mapping form the context of the sequence. The output layer outputs the learned SOC. Python, TensorFlow, and Keras are used to develop the concrete models, and the resultant ones are tested and analyzed on the benchmark data sets, where three temperature and nine working conditions are considered. The results show that the proposed bidirectional learning methods can outperform their competitors, with higher accuracy and better robustness. Being different from the idea of constructing battery equivalent models, the proposed method is data-driven and intends to estimate SOC with easily obtainable measurements, such as the voltages, currents, and temperatures, and provides a potential method for SOC estimation.

Key words: lithium-ion battery, SOC estimation, Bi-LSTM network, Bi-GRU network