一种编解码器模型的锂离子电池健康状态估算

随着锂离子电池应用领域的愈加广泛,实时、准确的评估其健康状态(state of health,SOH)成为确保电池安全可靠运行的重要要求。该文提出一种基于注意力机制解码器模型的锂离子电池SOH估算方法,该算法结合与GRU的特点,将数据编码成一组包含内在特征的序列,并由注意力帮助解码器完成最终的解算。该算法无需建立电池模型,也不需要过多的先验知识,仅通过单个采样周期的电压、电流采样值即可获得较高精度的SOH估计值。为适应更多应用场景,该文设计定长片段放电数据、定长片段充电数据及变长片段充电数据等3种输入模式,验证实验中,3种估算模式的平均绝对误差均小于1%,表明该估算方法具有估算周期短、估算精度高及适应性强等特性。

A Novel Method Based on Encoder-decoder Framework for Li-ion Battery State of Health Estimation

With the wide use of lithium-ion batteries, accurately estimating the state of health(SOH) online has become a significant requirement to ensure the safety and reliable operation of batteries. In this paper, a method based on encoder-decoder framework with attention mechanism was proposed to predict SOH of lithium-ion batteries, which combined CNN and GRU and encoded data into a set of sequences containing intrinsic features;and then with the attention mechanism, the decoder completed the final estimation. This algorithm does not need to establish any battery model or too much prior knowledge. It can get accurate predictions for SOH through the voltage and current in a single cycle. In order to adapt to a variety of situations, this paper designed three inputs modes: fixed-length segment of discharging data, fixed-length segment of charging data, and variable-length segment of charging data. The average error of these modes is less than 1% on the test set, which also confirms that the method proposed in this paper has advantages such as short estimation period, high estimation accuracy, and good adaptability.