发表机构
The Pennsylvania State University（宾夕法尼亚州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于Koopman算子的模型预测控制框架，实现锂离子电池串联单体荷电状态与温度同步均衡，通过凸二次规划高效调节误差，性能媲美非线性模型预测控制，计算时间大幅降低。
AI 中文摘要
电池单体间的电气和热差异会导致锂离子电池组中荷电状态（SoC）和温度分布不均匀。本文提出了一种基于Koopman算子的框架，用于串联电池单体的SoC和温度同步均衡。利用扩展动态模态分解识别出的电池单体特定Koopman预测器被组装成相邻电池误差动态，该动态保留了异质性和热扰动效应。Tikhonov正则化的前馈控制器提供了数值上稳健的补偿，而不会导致过大的电流放大，同时基于约束的Koopman模型预测控制（KMPC）通过凸二次规划调节残余误差。三电池仿真在连续两个城市测功机驾驶循环下验证了该控制器。KMPC实现了与非线性模型预测控制（NMPC）相当的均衡性能。与未控制情况相比，KMPC将相邻电池SoC和核心温度差异的均方根误差（RMSE）分别降低了66-81%和57-76%，同时将每次优化的计算时间从NMPC的0.0259秒减少到KMPC的0.0028秒。
英文摘要
Cell-to-cell electrical and thermal variations produce nonuniform state of charge (SoC) and temperature distributions in lithium-ion battery packs. This paper proposes a Koopman-operator-based framework for simultaneous SoC and temperature balancing of series-connected cells. Cell-specific Koopman predictors identified using extended dynamic mode decomposition are assembled into neighboring-cell error dynamics that retain heterogeneity and thermal-disturbance effects. A Tikhonov-regularized feedforward controller provides numerically robust compensation without excessive current amplification, while constrained Koopman-based model predictive control (KMPC) regulates the residual errors through a convex quadratic program. Three-cell simulations validate the controller under two consecutive Urban Dynamometer Driving Schedule cycles. KMPC achieves balancing performance comparable to nonlinear model predictive control (NMPC). Relative to the uncontrolled case, KMPC reduces the RMSEs of the neighboring-cell SoC and core temperature differences by 66-81% and 57-76%, respectively, while reducing the computation time per optimization from 0.0259s for NMPC to 0.0028s for KMPC.
