Research and application of battery performance decline law based on real vehicle data

The presented article develops the detailed analysis of battery performance degradation profiles for EVs, based on operational data collected in real-world use.Based on data points gathered for 150 vehicles over 24 months, we have developed and then validated an integrated degradation prediction model incorporating several degradation mechanisms.Our study applies a novel hybrid approach that will combine physics-based principles with data-driven methods for outlining the battery aging profile.The model proposed in this paper realizes a better prediction performance of 94.3% under different operational conditions and thus proves to be considerably superior to the existing techniques.Indeed, the change of temperature and charging behavior becomes the main influence factor with the correlation coefficient of 0.85 and 0.78, respectively.After applying the proposed model to a fleet management system, there are 32.4% maintenance cost reduction and 15.8% increasing of the cycle life for batteries.It represents in detail the continuous degradation assessment and predictive maintenance framework, validated on different vehicle platforms under varying operational conditions.These findings provide valuable inputs related to the improvement of battery management strategies and life extension of a battery in electric vehicle applications, hence benefiting theoretical understanding and practical application in electric vehicle battery management.