Bingyang Chen , Xingjie Zeng , Chao Liu , Yafei Xu , Heling Cao
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引用次数: 0
摘要
准确的充电状态(SOC)估计对于延长电池寿命和提高电池管理系统的安全性至关重要。然而,许多现有方法都面临着挑战,包括在特定驾驶模式下缺乏足够的样本、忽略低温等隐藏因素以及在转移学习中出现负转移等。本文介绍了自适应转移执行器(ATE)框架,该框架集成了增强变压器(Enformer)模型和自适应转移学习(ATL)。Enformer 融合了多级残留注意力(MRA)和模式动态分解(PDD),构成了预训练模型的骨干。MRA 解决了由于样本有限而导致的梯度消失问题,并捕捉了每个时间点的潜在关系。PDD 动态学习时间趋势、隐藏因素及其相互作用。ATL 提供了一种有效的特征学习策略,以促进 SOC 估计中的正迁移。在两个添加了噪声的公共数据集上的实验结果表明,与最先进的方法相比,所提出的方法提高了平均准确率。此外,九个转移场景的结果表明 ATE 框架具有很强的泛化和抗噪能力。
Health management of power batteries in low temperatures based on Adaptive Transfer Enformer framework
Accurate State of Charge (SOC) estimation is essential for extending battery life and improving the safety of battery management systems. However, many existing methods face challenges, including a lack of sufficient samples in specific driving modes, overlooking hidden factors such as low temperatures, and experiencing negative transfer in transfer learning. This paper introduces the Adaptive Transfer Enformer (ATE) Framework, which integrates an Enhanced Transformer (Enformer) model with Adaptive Transfer Learning (ATL). The Enformer incorporates Multilevel Residual Attention (MRA) and Pattern Dynamic Decomposition (PDD), forming the backbone of the pre-trained model. MRA addresses gradient vanishing issues due to limited samples and captures the underlying relationships at each time point. PDD dynamically learns temporal trends, hidden factors, and their interactions. ATL provides an effective feature learning strategy to promote positive transfer in SOC estimation. Experimental results on two public datasets with added noise show that the proposed method improves average accuracy compared to state-of-the-art methods. Additionally, results from nine transfer scenarios demonstrate the strong generalization and noise resistance capabilities of the ATE Framework.
期刊介绍:
Elsevier publishes Reliability Engineering & System Safety in association with the European Safety and Reliability Association and the Safety Engineering and Risk Analysis Division. The international journal is devoted to developing and applying methods to enhance the safety and reliability of complex technological systems, like nuclear power plants, chemical plants, hazardous waste facilities, space systems, offshore and maritime systems, transportation systems, constructed infrastructure, and manufacturing plants. The journal normally publishes only articles that involve the analysis of substantive problems related to the reliability of complex systems or present techniques and/or theoretical results that have a discernable relationship to the solution of such problems. An important aim is to balance academic material and practical applications.