Abstract
In electric vehicle technologies, the state of health prediction and safety assessment of battery packs are key issues to be solved. In this paper, the battery system data collected on the electric vehicle data management platform is used to model the corresponding state of health of the electric vehicle during charging and discharging processes. The increment in capacity in the same voltage range is used as the battery state of health indicator. In order to improve the modeling accuracy, the influence of ambient temperature on the capacity performance of the battery pack is considered. A temperature correction coefficient is added to the battery state of health model. Finally, a double exponential function is used to describe the process of battery health decline. Additionally, for the case where the amount of data is relatively small, model migration is also applied in the method. Particle swarm optimization algorithm is used to calibrate the model parameters. Based on the migration battery pack model and parameter identification method, the proposed method can obtain accurate battery pack SOH prediction result. The method is simple and easy to perform on the electric vehicle data management platform.
Highlights
IntroductionThe technological revolution and industrial reform are taking place across the world
At present, the technological revolution and industrial reform are taking place across the world
In the research of battery management technology of electric vehicles, accurate evaluation and prediction of the state of health (SOH) of battery can guide the reasonable use of a vehicle battery and extend the service life of battery, which is of great significance for the life cycle management and utilization of battery
Summary
The technological revolution and industrial reform are taking place across the world. The integration of traditional automobile technology, new energy technology and information technology is accelerating. It is a research topic to apply big data, artificial intelligence and other technologies to the new energy vehicle field. In the research of battery management technology of electric vehicles, accurate evaluation and prediction of the state of health (SOH) of battery can guide the reasonable use of a vehicle battery and extend the service life of battery, which is of great significance for the life cycle management and utilization of battery. The data management platform of electric vehicle collects real-time data generated during the driving process of electric vehicles. Using these data to explore the aging law of battery system is an important way of battery safety management and residual value evaluation
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