Data-driven methods, which can explore the relationship among battery external parameters and battery states automatically without establishing complicated battery model, have been intensively applied to estimate state of charge (SOC), state of health (SOH) and remaining useful life (RUL) etc. Nevertheless, relatively few researches have been done on the selection of data-driven model parameters and the determination of model with the optimal comprehensive performance. To address these questions, this paper presents a multi-objective decision method for data-driven based estimation of battery states. This method adopts the combination of the analytic hierarchy process and the entropy weight method together with integrating subjective and objective weights. The mean absolute error and root squared mean error of training-set, validation-set and test-set are used as accuracy indexes, and modeling time is seen as computation burden index. These seven indexes are applied as objective criteria for the multi-objective evaluation method, successfully evaluating the comprehensive performance of estimation model. Moreover, with three cases for RUL estimation, the specific application process of selecting the model with the optimal comprehensive performance by the proposed method is presented in detail.

数据驱动方法可以自动探索电池外部参数与电池状态之间的关系，而无需建立复杂的电池模型，已被广泛应用于估算充电状态（SOC），健康状态（SOH）和剩余使用寿命（RUL）然而，关于数据驱动模型参数的选择和具有最佳综合性能的模型确定的研究相对较少。为了解决这些问题，本文提出了一种基于数据驱动的电池状态估计的多目标决策方法。该方法采用了层次分析法和熵权法相结合的方法，并结合了主客观权重。训练集的平均绝对误差和均方根误差 将验证集和测试集用作准确性指标，并将建模时间视为计算负担指标。将这七个指标作为多目标评估方法的客观标准，成功评估了评估模型的综合性能。此外，针对三种情况进行RUL估计，详细介绍了该方法选择综合性能最优的模型的具体应用过程。