Abstract
Lithium-ion batteries are the primary power source in electric vehicles, and the prognosis of their remaining useful life is vital for ensuring the safety, stability, and long lifetime of electric vehicles. Accurately establishing a mechanism model of a vehicle lithium-ion battery involves a complex electrochemical process. Remaining useful life (RUL) prognostics based on data-driven methods has become a focus of research. Current research on data-driven methodologies is summarized in this paper. By analyzing the problems of vehicle lithium-ion batteries in practical applications, the problems that need to be solved in the future are identified.
Highlights
Electric vehicles have become a focus of global research owing to their energy savings and environmental friendliness [1]
Data-driven Remaining useful life (RUL) prediction methods can be divided into three groups based on the artificial intelligence filtering techniques and stochastic process degradation, groups based on the artificial intelligence filtering techniques and stochastic process degradation, respectively
Vehicle lithium-ion batteries are influenced by many factors, including temperature, discharge current, and so on, which has led to a decline in accuracy
Summary
Electric vehicles have become a focus of global research owing to their energy savings and environmental friendliness [1]. Model S made in Norway suddenly caught fire in 2014 when it was charging in the fast charging station [6] To avoid such catastrophic incidents caused by the degradation of lithium-ion batteries and to. With the rapid proliferation of lithium-ion battery applications, research based on data-driven methods (such as degradation model establishment, RUL prediction, research based on data-driven methods (such as degradation model establishment, RUL prediction, and health states assessment) is summarized in this paper. Data-driven RUL prediction methods can be divided into three groups based on the artificial intelligence filtering techniques and stochastic process degradation, groups based on the artificial intelligence filtering techniques and stochastic process degradation, respectively.
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