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Online State-of-Health Estimation for the Lithium-Ion Battery Based on An LSTM Neural Network with Attention Mechanism

Online state-of-health(SOH) estimation of lithium-ion battery(LIB) is a critical problem in battery management system. Because the measurement of capacity is limited by the environment, in practical applications, it is difficult to estimate SOH quickly. This paper proposed a online SOH estimation of the LIB method that combines long short-term memory(LSTM) neural network with attention mechanism(AM). The LSTM neural network is used to learn the mapping relationship between voltage, current, temperature and capacity to capture the long-term dependence of capacity reduction process. Then, we use the AM to select relevant hidden state of LSTM across all time steps. The effectiveness of the proposed method is evaluated by using the charge and discharge cycle data set of LIB from the NASA Ames Excellence Prediction Center.