Abstract
Battery state of charge (SOC) is the available capacity of a battery expressed as a percentage of its nominal capacity. It is essential to estimate the SOC accurately for the normal use of Li-ion batteries equipment. However, SOC cannot be measured directly, but can only be estimated indirectly by measurable variables. Both the traditional methods and the methods based on adaptive filtering algorithms have some limitations. According to the nonlinear characteristics of Li-ion batteries under actual working conditions, this paper applies a new deep learning method, temporal convolutional network (TCN), which is firstly used to estimate the SOC of Li-ion batteries. This method can directly map the voltage, current, and temperature that can be observed and measured during the operation of Li-ion batteries into the SOC without using the battery model or setting additional parameters. Moreover, it can learn and update parameters by itself during the training process. Only one model is needed to estimate the SOC under different temperatures and working conditions. The experimental results show that the proposed method achieves a low average absolute error of 0.82% at a fixed temperature and 0.67% at multiple temperatures with relatively simple model complexity.
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This work was supported by Inner Mongolia Natural Science Foundation (2018MS06019).
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Liu, Y., Li, J., Xiong, N.N. (2021). A Novel Estimation Method for the State of Charge of Lithium-Ion Battery Using Temporal Convolutional Network Under Multiple Working Conditions. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2020. Lecture Notes in Computer Science(), vol 12608. Springer, Cham. https://doi.org/10.1007/978-3-030-74717-6_4
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DOI: https://doi.org/10.1007/978-3-030-74717-6_4
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