基于卡尔曼滤波与四态集总热模型的锂电池温度估计

Temperature Estimation of Lithium Battery Based on Kalman Filtering and Four-State Lumped Thermal Modeling

摘要: 在保证电池包安全运转的电池热管理系统中，温度估计至关重要，但目前在实车中所用的温度传感器采集温度的方法，不仅采集不到电池的内部温度，而且还带来了成本和安全问题的增加。为此本文提出一种基于卡尔曼滤波算法和四态集总热模型的锂电池温度估计方法估算方壳锂电池的内部和表面温度。通过四态集总热模型反应由电池内部和表面以及环境的热传递引起的温度梯度，再通过卡尔曼滤波算法对模型结果进行优化校正。估计出来的温度再反应给产热模型更新随温度变化的内阻，形成一个闭环估计。结果表明模型的误差可以控制在0.6˚C以内，该模型对热管理系统的设计起到了很好的指导意义。

Abstract: Temperature estimation is crucial in a battery thermal management system that ensures the safe operation of the battery pack. However, the current method of collecting temperature by temperature sensors in real vehicles not only fails to collect the internal temperature of the battery, but also increases the cost and safety problems. To this end, this paper proposes a temperature estimation method for lithium batteries based on Kalman filtering algorithm and four-state lumped thermal model to estimate the internal and surface temperatures of square-shell lithium batteries. The temperature gradient caused by the heat transfer inside and on the surface of the cell as well as the environment is reacted by a four-state lumped thermal model, and then the model results are optimally corrected by a Kalman filtering algorithm. The estimated temperature is then reacted to the heat production model to update the temperature-dependent internal resistance, forming a closed-loop estimate. The results show that the error of the model can be controlled within 0.6˚C, and the model plays a good guiding significance for the design of thermal management system.