An algorithm for state of charge estimation based on a single-particle model
An algorithm for state of charge estimation based on a single-particle model
复制标题
基于单粒子模型的荷电状态估计算法
DOI：
10.1016/j.est.2021.102644
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发表时间：
2021-05-13
影响因子：
9.4
通讯作者：
Xiang, Kui
中科院分区：
文献类型：
--
作者：
Ren, Lichao;Zhu, Guorong;Xiang, Kui
与等效电路模型或其他经验模型相比，基于物理的模型具有准确、详尽的优点，因此成为电池管理系统（BMS）中估计锂离子电池状态的潜在候选模型。传统的伪二维（P2D）模型耦合了大量的非线性偏微分方程，导致模型过于复杂，难以应用于实际。简化的单粒子（SP）模型更接近于真实的应用，但其精度有待提高，因为它仅在低充放电倍率下才能满足要求。针对SP模型的不足，提出了精度更高的扩展单颗粒模型（ESP）。提出了一种基于ESP模型和安时积分相结合的SOC闭环估计算法。结果表明，ESP模型能够有效地模拟电池的性能，闭环SOC估计算法能够在不增加计算复杂度的情况下修正初始SOC误差。在1C放电和FUDS放电条件下，基于ESP的SOC闭环估计的平均误差分别比安时积分减小了95%和92.5%。
Compared with the equivalent circuit models or other empirical models, a physics-based model has advantages of accurate and elaborate, and thus becomes a potential candidate used to estimate states of Li-ion batteries in a battery management systems (BMS). The traditional pseudo-two-dimensional (P2D) model couples a large number of nonlinear partial differential equations, leading to the model too complicated to be employed in actual application. The simplified single-particle (SP) model has a trend for the real usage, however, its accuracy needs to be improved, since it meets the demand only under the condition of a low charge/discharge rate. To overcome the drawbacks of the SP model, the extended single-particle model (ESP) with higher accuracy is proposed in this study. We also propose a new state of charge (SOC) closed-loop estimation algorithm based on the combination of ESP model and the ampere-hour integration. Results show that the ESP model can effectively simulate the performance of the battery, and the closed-loop SOC estimation algorithm can correct the initial SOC error without increasing the computational complexity. The mean error of the closed-loop SOC estimation based on ESP is reduced by about 95% and 92.5% than ampere-hour integration under 1 C discharge and FUDS discharge, respectively.