Predicting the state of charge and health of batteries using data-driven machine learning-北京理工大学先进储能科学与应用联合实验室-BIT AESA Advanced Energy Storage and Application

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

发表时间：2020-12-28 阅读次数：字体：【大中小】

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.

部分图片：

1A machine learning approach for SOC, SOH and RUL predictions of Li-ion batteries.png

图1 A machine learning approach for SOC, SOH and RUL predictions of Li-ion batteries.

2High-throughput battery fabrication and testing.png

图2 High-throughput battery fabrication and testing.

引文信息：

Man-Fai Ng,Jin Zhao,Qingyu Yan,Gareth J. Conduit,Zhi Wei Seh. Predicting the state of charge and health of batteries using data-driven machine learning[J]. Nature Machine Intelligence,2020,2(3). (下载链接)

其他相关论文：

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