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Constrained generalized predictive control of battery charging process based on a coupled thermoelectric model

机译:基于耦合热电模型的电池充电过程约束广义预测控制

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摘要

Battery temperature is a primary factor affecting the battery performance, and suitable battery temperature control in particular internal temperature control can not only guarantee battery safety but also improve its efficiency. This is however challenging as current controller designs for battery charging have no mechanisms to incorporate such information. This paper proposes a novel battery charging control strategy which applies the constrained generalized predictive control (GPC) to charge a LiFePO4 battery based on a newly developed coupled thermoelectric model. The control target primarily aims to maintain the battery cell internal temperature within a desirable range while delivering fast charging. To achieve this, the coupled thermoelectric model is firstly introduced to capture the battery behaviours in particular SOC and internal temperature which are not directly measurable in practice. Then a controlled auto-regressive integrated moving average (CARIMA) model whose parameters are identified by the recursive least squares (RLS) algorithm is developed as an online self-tuning predictive model for a GPC controller. Then the constrained generalized predictive controller is developed to control the charging current. Experiment results confirm the effectiveness of the proposed control strategy. Further, the best region of heat dissipation rate and proper internal temperature set-points are also investigated and analysed.\ud\ud
机译:电池温度是影响电池性能的主要因素,适当的电池温度控制(尤其是内部温度控制)不仅可以保证电池安全性,而且可以提高效率。然而,这是具有挑战性的,因为用于电池充电的电流控制器设计没有用于合并此类信息的机制。本文提出了一种新颖的电池充电控制策略,该策略基于新开发的耦合热电模型,将约束广义预测控制(GPC)应用于LiFePO4电池充电。控制目标的主要目的是在进行快速充电的同时将电池单元内部温度保持在期望的范围内。为此,首先引入耦合热电模型以捕获电池行为,尤其是SOC和内部温度,这些行为在实践中无法直接测量。然后,将其参数由递归最小二乘(RLS)算法识别的受控自回归综合移动平均(CARIMA)模型开发为GPC控制器的在线自调整预测模型。然后开发受约束的广义预测控制器以控制充电电流。实验结果证实了所提出的控制策略的有效性。此外,还研究和分析了最佳的散热率区域和适当的内部温度设定点。\ ud \ ud

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