An evaluation study of different modelling techniques for calendar ageing prediction of lithium-ion batteries. (October 2020)

• Record Type: Journal Article
• Authors: Liu, Kailong; Ashwin, T.R.; Hu, Xiaosong; Lucu, Mattin; Widanage, W. Dhammika
• Is Part Of: Renewable & sustainable energy reviews. Volume 131(2020)
• Publication Date: 2020-10
• Subjects: Lithium-ion battery -- Calendar ageing prediction -- Electrochemical model -- Semi-empirical model -- Data-driven model -- Electric vehicle; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2020.110017
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: Prediction of battery calendar ageing is a key but challenging issue in the development of durable electric vehicles. This paper simultaneously evaluates three mainstream types of modelling techniques for calendar ageing prediction of Lithium-ion (Li-ion) batteries. They are the pseudo two dimensional (P2D)-based electrochemical model, Arrhenius law-based semi-empirical model, and Gaussian process regression (GPR)-based data-driven model. Specifically, both the electrochemical and semi-empirical models are consciously developed or selected from the state-of-the-art modelling literature. For the data-driven model, due to the limited research in the existing publications, a machine learning-enabled GPR model is derived and applied for calendar ageing prediction. An experimental setup is developed to load the commercial Panasonic NCR18650BD batteries and to collect the experimental calendar ageing data under different storage temperature and SOC levels over 435 days. Based upon this well-rounded database, each model is well trained through using its corresponding training solution. Then the prediction performances of these models are studied and evaluated in terms of the model accuracy, generalization ability and uncertainty management. Both the challenges and future prospects of each model type are highlighted to assist the industrial and academic research communities, thus boosting the progress of designing advanced modelling techniques in battery calendar ageing prediction domain. Highlights: Accurate predictions of calendar ageing are crucial for battery real applications. Three representative modelling techniques are evaluated based on a well-rounded database. The accuracy, generalization ability and uncertainty management of these models are compared. Benefits and key challenges of each model type are systematically analysed and discussed. Inspiring future prospects are given toward the improvement of existing techniques.