Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review-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

Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review

发表时间：2020-12-28 阅读次数：字体：【大中小】

摘要：

Accurate health estimation and lifetime prediction of lithium-ion batteries are crucial for durable electric vehicles. Early detection of inadequate performance facilitates timely maintenance of battery systems. This reduces operational costs and prevents accidents and malfunctions. Recent advancements in “Big Data” analytics and related statistical/computational tools raised interest in data-driven battery health estimation. Here, we will review these in view of their feasibility and cost-effectiveness in dealing with battery health in real-world applications. We categorise these methods according to their underlying models/algorithms and discuss their advantages and limitations. In the final section we focus on challenges of real-time battery health management and discuss potential next-generation techniques. We are confident that this review will inform commercial technology choices and academic research agendas alike, thus boosting progress in data-driven battery health estimation and prediction on all technology readiness levels.

部分图片：

图1 Generic workflow for health prediction models using ML-estimated features. The learning machine senses the environment and stores data in memory and constructs the mapping from the feature space to the state space.

图2 Schematic illustration of RUL prediction with PF.

引文信息：

Liu,Aoife M. Foley,Alana Zülke,Maitane Berecibar,Elise Nanini-Maury,Joeri Van Mierlo,Harry E. Hoster. Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review[J]. Renewable and Sustainable Energy Reviews,2019,113. (下载链接)

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2. Matthieu Dubarry,David Beck. Big data training data for artificial intelligence-based Li-ion diagnosis and prognosis[J]. Journal of Power Sources,2020,479. (下载链接)

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