In this work, a state-space battery model is derived mathematically to estimate the state-of-charge (SoC) of a battery system. Subsequently, Kalman filter (KF) is applied to predict the dynamical behavior of the battery model. Results show an accurate prediction as the accumulated error, in terms of root-mean-square (RMS), is a very small value. From this work, it is found that different sets of >Q and >R values (KF's parameters) can be applied for better performance and hence lower RMS error. This is the motivation for the application of a metaheuristic algorithm. Hence, the result is further improved by applying a genetic algorithm (GA) to tune >Q and >R parameters of the KF. In an online application, a GA can be applied to obtain the optimal parameters of the KF before its application to a real plant (system). This simply means that the instantaneous response of the KF is not affected by the time consuming GA as this approach is applied only once to obtain the optimal parameters. The relevant workable MATLAB source codes are given in the appendix to ease future work and analysis in this area.
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机译：在这项工作中，通过数学推导得出状态空间电池模型，以估算电池系统的充电状态（SoC）。随后，将卡尔曼滤波器（KF）应用于预测电池模型的动力学行为。结果显示出准确的预测，因为以均方根（RMS）表示的累积误差很小。从这项工作中发现，可以应用不同组的> Q 和> R 值（KF的参数）来提高性能，从而降低RMS误差。这是应用元启发式算法的动机。因此，通过应用遗传算法（GA）调整KF的> Q 和> R 参数，可以进一步改善结果。在在线应用中，可以将GA应用于KF的最佳参数，然后再将其应用于实际工厂（系统）。这仅意味着KF的瞬时响应不受耗时的GA的影响，因为此方法仅应用一次即可获得最佳参数。附录中给出了相关的可行MATLAB源代码，以简化以后在该领域的工作和分析。
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