Abstract
Accurately predicting the state-of-health (SOH) and remaining useful life (RUL) of lithium-ion batteries is crucial for ensuring the safe and efficient operation of electric vehicles while minimizing associated risks. However, current deep learning methods are limited in their ability to selectively extract features and model time dependencies for these two parameters. Moreover, most existing methods rely on traditional recurrent neural networks, which have inherent shortcomings in long-term time-series modeling. To address these issues, this paper proposes a multi-task targeted learning framework for SOH and RUL prediction, which integrates multiple neural networks, including a multi-scale feature extraction module, an improved extended LSTM, and a dual-stream attention module. First, a feature extraction module with multi-scale CNNs is designed to capture detailed local battery decline patterns. Secondly, an improved extended LSTM network is employed to enhance the model's ability to retain long-term temporal information, thus improving temporal relationship modeling. Building on this, the dual-stream attention module-comprising polarized attention and sparse attention to selectively focus on key information relevant to SOH and RUL, respectively, by assigning higher weights to important features. Finally, a many-to-two mapping is achieved through the dual-task layer. To optimize the model's performance and reduce the need for manual hyperparameter tuning, the Hyperopt optimization algorithm is used. Extensive comparative experiments on battery aging datasets demonstrate that the proposed method reduces the average RMSE for SOH and RUL predictions by 111.3\% and 33.0\%, respectively, compared to traditional and state-of-the-art methods.
Abstract (translated)
准确预测锂离子电池的健康状态（SOH）和剩余使用寿命（RUL）对于确保电动汽车安全高效运行并降低相关风险至关重要。然而，当前深度学习方法在针对这两个参数选择性提取特征和建模时间依赖关系方面存在局限。此外，多数现有方法依赖传统循环神经网络，其在长期时间序列建模中存在固有缺陷。针对上述问题，本文提出一种用于SOH与RUL预测的多任务目标学习框架，该框架集成了多尺度特征提取模块、改进的扩展LSTM以及双流注意力机制等多种神经网络。首先，设计了基于多尺度CNN的特征提取模块，以捕捉电池衰减的详细局部模式。其次，采用改进的扩展LSTM网络增强模型对长期时序信息的保持能力，从而提升时序关系建模效果。在此基础上，双流注意力模块——包含极化注意力和稀疏注意力——通过为重要特征分配更高权重，分别选择性地聚焦于与SOH和RUL相关的关键信息。最后，通过双任务层实现多对二映射。为优化模型性能并减少手动超参数调优需求，采用Hyperopt优化算法。在电池老化数据集上的大量对比实验表明，相较于传统及先进方法，所提方法将SOH和RUL预测的平均RMSE分别降低了111.3%和33.0%。
URL
https://arxiv.org/abs/2603.22323
