基于电热耦合模型和模型预测控制的锂离子电池充电控制

A. K. de Souza, G. Plett, M. Trimboli
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引用次数: 4

摘要

本文提出了一种基于高精度低阶电热等效电路模型的模型预测控制(MPC)在锂离子电池性能管理中的新应用,并通过实验室实验进行了验证。所提出的方法使用MPC来确保符合细胞水平的操作限制,并且有可能通过减轻导致容量衰减的细胞降解的某些机制来延长寿命。建立了一个五态电热模型,并对其进行了表征和验证。实现采用扩展的非线性卡尔曼滤波进行状态估计,MPC控制充放电电流。用26650圆柱形磷酸锂铁(LFP)电池对完整的方法进行了实验验证。新方法:(1)建立了细胞动力学的完全耦合电热模型;(ii)采用动态迟滞模型以提高准确性;(iii)采用非线性卡尔曼滤波器进行精确的状态估计,以通知MPC算法;(iv)利用改进的MPC形式,正确地模拟直接馈通行为来表征欧姆电阻。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lithium-Ion Battery Charging Control Using a Coupled Electro-Thermal Model and Model Predictive Control
This paper presents a novel application of model predictive control (MPC) to the problem of managing lithiumion cell performance using a highly accurate low-order electrothermal equivalent circuit model and is experimentally validated via laboratory experiments. The proposed method uses MPC to ensure compliance with cell-level operational limits and has the potential to extend lifetime by mitigating certain mechanisms of cell degradation leading to capacity fade. A five-state electrothermal model is developed, characterized and validated. Implementation employs an extended nonlinear Kalman filter for state estimation and MPC for controlling charge/discharge current. The complete method is experimentally validated using a 26650 cylindrical format lithium-iron phosphate (LFP) cell. The new method: (i) develops a fully coupled electro-thermal model of cell dynamics; (ii) incorporates a dynamic hysteresis model to improve accuracy; (iii) employs a nonlinear Kalman filter for accurate state estimation to inform the MPC algorithm; and (iv) utilizes a modified form of MPC which correctly models direct feed-through behavior to characterize ohmic resistance.
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