基于HSMM的铝空电池后期SOC估计

Low-SOC Estimation of Aluminum-Air Battery Based on HSMM

  • 摘要: 电池荷电状态 (SOC) 的估算精度是影响新能源汽车性能的重要因素之一。传统的安时法由于累积误差较大始终无法满足精确的SOC估计。该文采用基于隐半马尔可夫模型 (HSMM) 的SOC预测作为安时法的一个补充, 使铝空电池后期估计精度可以得到保障。该模型的每个不同状态产生多组观察值, 根据各个状态之间的转换概率以及状态驻留时间可以比较准确地预测后期各个状态下的剩余寿命。经过实验仿真验证, 与单一的安时法相比, 结合HSMM的SOC估计精度在后期有较大提升。

     

    Abstract: The estimation accuracy of the battery state of charge (SOC) is one of the key factors that affect the performance of new energy vehicles. Owing to the large accumulated error, traditional ampere-hour method cannot meet the precise estimate of the aluminum-air battery SOC. In this paper, the approach based on hidden semi-Markov models (HSMM) of SOC prediction is applied as a complement for the ampere-hour method, making the latter estimation precision of aluminum air battery be guaranteed. Each of the different states of the model produces multiple sets of observations. According to the transition probability between the various states and the residence time, the model can more accurately predict the remaining time of each state. Through the experimental simulation and comparison with the single ampere-hour method, SOC estimation error combined with HSMM promotes the prediction accuracy when the battery is running out.

     

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