Xing Chuang, Liu Hangyu, Zhang Zekun, Wang Jun, Wang Jiyao
College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China.
School of Cyber Science and Engineering, Sichuan University, Chengdu 610207, China.
Sensors (Basel). 2024 Jun 27;24(13):4185. doi: 10.3390/s24134185.
Predicting the health status of lithium-ion batteries is crucial for ensuring safety. The prediction process typically requires inputting multiple time series, which exhibit temporal dependencies. Existing methods for health status prediction fail to uncover both coarse-grained and fine-grained temporal dependencies between these series. Coarse-grained analysis often overlooks minor fluctuations in the data, while fine-grained analysis can be overly complex and prone to overfitting, negatively impacting the accuracy of battery health predictions. To address these issues, this study developed a Hybrid-grained Evolving Aware Graph (HEAG) model for enhanced prediction of lithium-ion battery health. In this approach, the Fine-grained Dependency Graph (FDG) helps us model the dependencies between different sequences at individual time points, and the Coarse-grained Dependency Graph (CDG) is used for capturing the patterns and magnitudes of changes across time series. The effectiveness of the proposed method was evaluated using two datasets. Experimental results demonstrate that our approach outperforms all baseline methods, and the efficacy of each component within the HEAG model is validated through the ablation study.
预测锂离子电池的健康状态对于确保安全至关重要。预测过程通常需要输入多个时间序列，这些序列呈现出时间依赖性。现有的健康状态预测方法未能揭示这些序列之间的粗粒度和细粒度时间依赖性。粗粒度分析往往会忽略数据中的微小波动，而细粒度分析可能过于复杂且容易过拟合，对电池健康预测的准确性产生负面影响。为了解决这些问题，本研究开发了一种混合粒度进化感知图（HEAG）模型，用于增强锂离子电池健康的预测。在这种方法中，细粒度依赖图（FDG）帮助我们对各个时间点不同序列之间的依赖性进行建模，而粗粒度依赖图（CDG）用于捕获时间序列中变化的模式和幅度。使用两个数据集对所提出方法的有效性进行了评估。实验结果表明，我们的方法优于所有基线方法，并且通过消融研究验证了HEAG模型中每个组件的有效性。
