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A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter by Tian, Yong; Lai, Rucong; Li, Xiaoyu; Xiang, Lijuan; Tian, Jindong is a Engineering article available to read on EtoBox.

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Because of the extensive applications of lithium-ion batteries (LIBs) in electric vehicles (EVs), the battery management system (BMS) used to monitor the state and guarantee the operating safety of LIBs has been widely researched. The state of charge (SOC) is one of the most important states of LIBs that is monitored online. However, accurate SOC estimation is challenging because of erratic battery dynamics and SOC variation with current, temperature, operating conditions, etc. In this paper, a method combining a long short-term memory (LSTM) network with an adaptive cubature Kalman filter (ACKF) is proposed. The LSTM network is first utilized to learn the nonlinear relationship between the SOC and measurements, including current, voltage and temperature, and then, the ACKF is applied to smooth the outputs of the LSTM network, thus achieving accurate and stable SOC estimation. The proposed method can simplify the tedious procedure of tuning the parameters of the LSTM network, and it does not need to establish a battery model. Data collected from dynamic stress tests are used as training datasets, while data collected from US06 tests and federal urban driving schedules serve as test

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Author

Tian, Yong; Lai, Rucong; Li, Xiaoyu; Xiang, Lijuan; Tian, Jindong

Publisher

Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 0306-2619)

Published

2020

Language

EN

Field

Engineering (Physical Sciences)