预测锂离子电池(LIB)的容量对于确保LIB的安全运行和延长其使用寿命至关重要。然而,LIB容易受到环境干扰的影响,这可能会影响预测的准确性。此外,预测LIB容量的过程中的可解释性对于用户理解模型也很重要,发现问题,并做出决定。在这项研究中,介绍了一种考虑环境干扰(IM-EI)的LIB容量预测方法。斯皮尔曼相关系数,可解释性原则,信念规则库(BRB),和可解释性约束用于提高IM-EI的预测精度和可解释性。引入动态属性可靠性以最小化环境干扰的影响。实验结果表明,IM-EI模型与其他模型相比具有较好的可解释性和较高的精度。在干扰条件下,该模型仍具有较好的精度和鲁棒性。
Predicting the capacity of lithium-ion battery (LIB) plays a crucial role in ensuring the safe operation of LIBs and prolonging their lifespan. However, LIBs are easily affected by environmental interference, which may impact the precision of predictions. Furthermore,
interpretability in the process of predicting LIB capacity is also important for users to understand the model, identify issues, and make decisions. In this study, an interpretable method considering environmental interference (IM-EI) for predicting LIB capacity is introduced. Spearman correlation coefficients,
interpretability principles, belief rule base (BRB), and
interpretability constraints are used to improve the prediction precision and
interpretability of IM-EI. Dynamic attribute reliability is introduced to minimize the effect of environmental interference. The experimental results show that IM-EI model has good
interpretability and high precision compared to the other models. Under interference conditions, the model still has good precision and robustness.