A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery

• Published in: Applied energy 2021-10, Vol.300, p.117346, Article 117346
• Main Authors: Sui, Xin, He, Shan, Vilsen, Søren B., Meng, Jinhao, Teodorescu, Remus, Stroe, Daniel-Ioan
• Format: Article
• Language: English
• Subjects: Battery management system; Deep learning; durability; energy; Health monitoring; lithium batteries; Lithium-ion battery; Machine learning; neural networks; State of health; support vector machines
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2021.117346

•A comprehensive review of non-probabilistic machine learning for battery SOH estimation is presented.•For every algorithm, the principle derivation process is provided followed by flow charts with a unified form.•The challenges and unresolved issues of battery SOH estimation using machine learning technology are discussed.•The estimation performance, the publication trend, and the training mode of each method are compared.•The outlook of the research on future machine learning-based battery SOH estimation methods is given. Lithium-ion batteries are used in a wide range of applications including energy storage systems, electric transportations, and portable electronic devices. Accurately obtaining the batteries’ state of health (SOH) is critical to prolong the service life of the battery and ensure the safe and reliable operation of the system. Machine learning (ML) technology has attracted increasing attention due to its competitiveness in studying the behavior of complex nonlinear systems. With the development of big data and cloud computing, ML technology has a big potential in battery SOH estimation. In this paper, the five most studied types of ML algorithms for battery SOH estimation are systematically reviewed. The basic principle of each algorithm is rigorously derived followed by flow charts with a unified form, and the advantages and applicability of different methods are compared from a theoretical perspective. Then, the ML-based SOH estimation methods are comprehensively compared from following three aspects: the estimation performance of various algorithms under five performance metrics, the publication trend obtained by counting the publications in the past ten years, and the training modes considering the feature extraction and selection methods. According to the comparison results, it can be concluded that amongst these methods, support vector machine and artificial neural network algorithms are still research hotspots. Deep learning has great potential in estimating battery SOH under complex aging conditions especially when big data is available. Moreover, the ensemble learning method provides an emerging alternative trading-off between data size and accuracy. Finally, the outlooks of the research on future ML-based battery SOH estimation methods are closed, hoping to provide some inspiration when applying ML methods to battery SOH estimation.