荷电状态
卷积神经网络
计算机科学
稳健性(进化)
均方误差
电池(电)
短时记忆
人工神经网络
算法
循环神经网络
人工智能
功率(物理)
数学
统计
物理
基因
化学
量子力学
生物化学
作者
Chunsheng Hu,Fangjuan Cheng,Lvyi Ma,Bohao Li
出处
期刊:Journal of The Electrochemical Society
[The Electrochemical Society]
日期:2022-03-01
卷期号:169 (3): 030544-030544
被引量:23
标识
DOI:10.1149/1945-7111/ac5cf2
摘要
Accurately estimating the state of charge (SOC) of lithium-ion batteries is critical for developing more reliable and efficient operation of electric vehicles. However, the commonly used models cannot simultaneously extract effective spatial and temporal features from the original data, leading to an inefficient SOC estimation. This paper proposes a novel neural network method for accurate and robust battery SOC estimation, which incorporates the temporal convolutional network (TCN) and the long short-term memory (LSTM), namely TCN-LSTM model. Specifically, the TCN is employed to extract more advanced spatial features among multivariate variables, and the LSTM captures long-term dependencies from time-series data and maps battery temporal information into current SOC and historical inputs. The proposed model performs well in various estimation conditions. The average value of mean absolute error, root mean square error, and maximum error of SOC estimation achieve 0.48%, 0.60%, and 2.3% at multiple temperature conditions, respectively, and reach 0.70%, 0.81%, and 2.7% for a different battery, respectively. In addition, the proposed method has better accuracy than the LSTM or TCN used independently and the CNN-LSTM network. The computational burden with varying length of input is also investigated. In summary, experiment results show that the proposed method has excellent generalization and robustness.
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