Electric Vehicle Batteries: Status and Perspectives of Data-Driven Diagnosis and Prognosis

Abstract

Mass marketing of battery-electric vehicles (EVs) will require that car buyers have high confidence in the performance, reliability and safety of the battery in their vehicles. Over the past decade, steady progress has been made towards the development of advanced battery diagnostic and prognostic technologies using data-driven methods that can be used to inform EV owners of the condition of their battery over its lifetime. The research has shown promise for accurately predicting battery state of health (SOH), state of safety (SOS), cycle life, the remaining useful life (RUL), and indicators of cells with high risk of failure (i.e., weak cells). These methods yield information about the battery that would be of great interest to EV owners, but at present it is not shared with them. This paper is concerned with the present status of the information available on the battery with a focus on data-driven diagnostic and prognostic approaches, and how the information would be generated in the future for the millions of EVs that will be on the road in the next decade. Finally, future trends and key challenges for the prognostics and health management of the batteries in real-world EV applications are presented from four perspectives (cloud-edge interaction, full-scale diagnosis, artificial intelligence and electronic health reports) are discussed.