State-of-charge estimation of lithium-ion battery based on clockwork recurrent neural network

Xiong Feng, Junxiong Chen, Zhongwei Zhang, Shuwen Miao and Qiao Zhu

Energy, 2021, vol. 236, issue C

Abstract: State of charge (SOC) is the most important parameter in battery management system (BMS). Firstly, in this paper, a new structure of standard recurrent neural network (RNN), named clockwork recurrent neural network (CWRNN) is introduced, which divides hidden layer into separate modules, assigns each module a different specify clock speed to solve long-term dependencies. Secondly, because of each module in CWRNN has different clock speeds, it makes computation only at its prescribed clock period, rather than compute and update all the inner parameters at every time step, so that CWRNN can reduce the training and computation cost obviously. Finally, employed network is verified at dynamic drive cycle at different temperature. The result shows that proposed network has satisfactory estimation results, such as the root mean square error (RMSE) is less than 1.29%.

Keywords: Tate of charge; Lithium-ion battery; Electric vehicles; Battery management system; Recurrent neural network (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:236:y:2021:i:c:s036054422101608x

DOI: 10.1016/j.energy.2021.121360

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