State of charge estimation of lithium-ion batteries


Ph.D.The estimation of the state of charge (SOC) for batteries is critical for electric vehicles users to determine remaining driving distance and timely charging. Classical methods of SOC estimation include coulomb counting, machine learning methods and model-based estimation. These approached have several drawbacks, however. The coulomb counting method requires high precision of sensors as well as accurate estimation of initial SOC. Machine learning methods demand that the training data must be sufficient to cover the entire loading conditions. Sophisticated models would possibly introduce over-fitting problems and not robust. In this paper, starting from a simple internal resistance model, ambient temperature is introduced as a factor to enhance the SOC estimation. Further improvement of model comes from statistical inferences of the relationship between the open-circuit voltage (OCV) and state of charge (SOC). After modeling, the battery is treated as a state-space system, in which the unscented Kalman filter is applied to reduce the effct of environmental uncertainties, cell-to-cell variation, and the initial values of SOC. By applying nonparametric and semi-parametric estimators with the state equation, the accuracy of SOC estimation can be significantly improved.電池的荷電狀態(SOC)的估計對於電動汽車用戶來說是至關重要的,因其可以決定剩餘的行駛距離和充電時間。SOC估計的經典方法包括庫侖計數、機器學習方法和基於模型的估計。然而,此類方法在一定條件下下存在缺陷。庫侖計數法需要高精度的傳感器及對初始SOC的准確估計。機器學習方法要求訓練數據必須足以覆蓋整個加載條件。而太過複雜的模型可能會引入過度擬合的問題,並且缺乏泛化能力。本文首先從一個簡單的內部電阻模型出發,隨後引入環境溫度作提高SOC估計的一個因素。模型的進一步改進來自于對開路電壓(OCV)和電荷狀態(SOC)之間的關系的統計推斷。對電池建模後,我們將電池視作一種狀態空間系統(state-spacesystem),並使用無損卡爾曼濾波(unscented Kalman filter)來降低環境、電池間差別以及初始SOC猜測對估計的影響。通過將非參數和半參數估計量應用到狀態方程中,SOC估計的精度得到了顯著的提高。Tao, Ying.Thesis Ph.D. Chinese University of Hong Kong 2017.Includes bibliographical references (leaves 67-70).Abstracts also in Chinese.Title from PDF title page (viewed on 14, January, 2020)

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