Predicting the state of charge and health of batteries using data-driven machine learning

电池(电) 计算机科学 吞吐量 机器学习 荷电状态 人工智能 领域(数学) 国家(计算机科学) 健康状况 无线 算法 功率(物理) 纯数学 物理 电信 量子力学 数学
作者
Man‐Fai Ng,Jin Zhao,Qingyu Yan,G. J. Conduit,Zhi Wei Seh
出处
期刊:Nature Machine Intelligence [Nature Portfolio]
卷期号:2 (3): 161-170 被引量:565
标识
DOI:10.1038/s42256-020-0156-7
摘要

Machine learning is a specific application of artificial intelligence that allows computers to learn and improve from data and experience via sets of algorithms, without the need for reprogramming. In the field of energy storage, machine learning has recently emerged as a promising modelling approach to determine the state of charge, state of health and remaining useful life of batteries. First, we review the two most studied types of battery models in the literature for battery state prediction: the equivalent circuit and physics-based models. Based on the current limitations of these models, we showcase the promise of various machine learning techniques for fast and accurate battery state prediction. Finally, we highlight the major challenges involved, especially in accurate modelling over length and time, performing in situ calculations and high-throughput data generation. Overall, this work provides insights into real-time, explainable machine learning for battery production, management and optimization in the future. Predicting the properties of batteries, such as their state of charge and remaining lifetime, is crucial for improving battery manufacturing, usage and optimisation for energy storage. The authors discuss how machine learning methods and high-throughput experimentation provide a data-driven approach to this problem, and highlight challenges in building models which provide fast and accurate battery state predictions.
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