A rapid neural network–based state of health estimation scheme for screening of end of life electric vehicle batteries. (March 2021)

• Record Type: Journal Article
• Authors: Rastegarpanah, Alireza; Hathaway, Jamie; Ahmeid, Mohamed; Lambert, Simon; Walton, Allan; Stolkin, Rustam
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 235:Number 3(2021)
• Page Start: 330
• Page End: 346
• Publication Date: 2021-03
• Subjects: Neural networks -- electric vehicles -- state of health -- lithium-ion batteries -- screening -- gateway testing; Mechanical engineering -- Periodicals; Automatic control -- Periodicals; Systems engineering -- Periodicals; 621.3
• DOI: 10.1177/0959651820953254
• Languages: English
• ISSNs: 0959-6518

Abstract

There is growing interest in recycling and re-use of electric vehicle batteries owing to their growing market share and use of high-value materials such as cobalt and nickel. To inform the subsequent applications at battery end of life, it is necessary to quantify their state of health. This study proposes an estimation scheme for the state of health of high-power lithium-ion batteries based on extraction of parameters from impedance data of 13 Nissan Leaf 2011 battery modules modelled by a modified Randles equivalent circuit model. Using the extracted parameters as predictors for the state of health, a baseline single hidden layer neural network was evaluated by root mean square and peak state of health prediction errors and refined using a Gaussian process optimisation procedure. The optimised neural network predicted state of health with a root mean square error of (1.729 ± 0.147)%, which is shown to be competitive with some of the most performant existing neural network–based state of health estimation schemes, and is expected to outperform the baseline model with ∼50 training samples. The use of equivalent circuit model parameters enables more in-depth analysis of the battery degradation state than many similar neural network–based schemes while maintaining similar accuracy despite a reduced dataset, while there is demonstrated potential for measurement times to be reduced to as little as 30 s with frequency targeting of the impedance measurements.