（1.Tianjin University Research Institute of Architectural Design and Urban Planning Co.，Ltd， Tianjin 300073， China； 2.School of Electrical and Information Engineering， Tianjin University， Tianjin 300072， China） 在知网中查找 在百度中查找 在本站中查找
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摘要:
为了能够准确可靠地估计锂离子电池的健康状态（State of Health， SOH），提出一种基于时序卷积网络（Temporal Convolutional Network， TCN）的数据驱动模型来建立电池充电曲线与SOH之间的映射关系.TCN是一种由多层因果卷积组成的神经网络，它能够对电池充电曲线上的采样点序列进行编码，通过编码得到的编码向量会更易于与SOH建立映射关系.实验结果表明所提基于TCN的SOH估计模型具有较高的估计精度，对不同种类的电池也有良好的适应能力.
The state of health （SOH） of a lithium-ion battery reflects the aging degree of Lithium-ion the battery. When the battery is charged in constant current-constant voltage mode， the charging curves with different aging degrees are also different. Based on this fact， this paper proposes a data-driven model based on a temporal convolutional network （TCN） to establish the mapping relationship between the charging curve and SOH. TCN is a novel neural network composed of multi-layer causal convolution， which can encode the sequence of sampling points on the charging curve. The experiment proves that the encoding vector is easier to establish the mapping relationship with SOH. The experimental results show that the proposed SOH estimation model has high estimation accuracy andgood adaptability to different types of batteries.
