GPT4Battery: An LLM-driven Framework for Adaptive State of Health Estimation of Raw Li-ion Batteries

• URL: http://arxiv.org/abs/2402.00068v1
• Date: Tue, 30 Jan 2024 14:47:15 GMT
• Title: GPT4Battery: An LLM-driven Framework for Adaptive State of Health Estimation of Raw Li-ion Batteries
• Authors: Yuyuan Feng, Guosheng Hu, Zhihong Zhang
• Abstract summary: State of health (SOH) is a crucial indicator for assessing the degradation level of batteries that cannot be measured directly but requires estimation. This paper proposes a novel framework for adaptable SOH estimation across diverse batteries. The proposed framework achieves state-of-the-art accuracy on four widely recognized datasets collected from 62 batteries.
• Score: 20.144140373356194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: State of health (SOH) is a crucial indicator for assessing the degradation level of batteries that cannot be measured directly but requires estimation. Accurate SOH estimation enhances detection, control, and feedback for Li-ion batteries, allowing for safe and efficient energy management and guiding the development of new-generation batteries. Despite the significant progress in data-driven SOH estimation, the time and resource-consuming degradation experiments for generating lifelong training data pose a challenge in establishing one large model capable of handling diverse types of Li-ion batteries, e.g., cross-chemistry, cross-manufacturer, and cross-capacity. Hence, this paper utilizes the strong generalization capability of large language model (LLM) to proposes a novel framework for adaptable SOH estimation across diverse batteries. To match the real scenario where unlabeled data sequentially arrives in use with distribution shifts, the proposed model is modified by a test-time training technique to ensure estimation accuracy even at the battery's end of life. The validation results demonstrate that the proposed framework achieves state-of-the-art accuracy on four widely recognized datasets collected from 62 batteries. Furthermore, we analyze the theoretical challenges of cross-battery estimation and provide a quantitative explanation of the effectiveness of our method.

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