Temperature monitoring is indispensable to the optimal and safe operation of a lithium-ion battery. In this paper, a spatio-temporal learning model designed by evolutionary algorithm is proposed to predict the thermal distribution. To formulate the multicharacteristic spatial dynamics, the chicken swarm optimization, based fusion of different dimensionality-reduction methods, is proposed for learning spatial basis functions. Through integration with the time/space separation based approach and equivalent circuit model based thermal model, the reduced-order model is derived. The related parameters of the reduced-order model are identified by integrating chicken swarm optimization with time/space separation based approach. A Bayesian-regularized neural-network based compensation model is developed to compensate for the model errors caused by the spatio-temporal coupled dynamics. Based on the Rademacher complexity, the generalization bound of the proposed model is analyzed. Simulations and comparisons demonstrate the superiority of the proposed model.

中文翻译：

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锂离子电池热分布时空学习模型的演化设计


温度监控对于锂离子电池的最佳和安全运行是必不可少的。本文提出了一种通过进化算法设计的时空学习模型来预测热分布。为了制定多特征空间动力学，提出了基于不同降维方法融合的鸡群优化来学习空间基函数。通过与基于时间/空间分离的方法和基于等效电路模型的热模型的集成，推导了降阶模型。通过将鸡群优化与基于时间/空间分离的方法相结合来识别降阶模型的相关参数。开发了基于贝叶斯正则化神经网络的补偿模型，以补偿时空耦合动态引起的模型误差。基于Rademacher复杂度，分析了所提出模型的泛化界限。仿真和比较证明了该模型的优越性。