Performance Optimization and Battery Health Analysis of Electric Vehicles under Real-World Driving Conditions: A Data-Driven Experimental Approach

الملخص

Electric vehicles (EVs) have gained significant prominence as a sustainable and efficient mode of transportation. However, their real-world performance and the longevity of their battery systems present ongoing challenges that require comprehensive investigation.

While extensive research has focused on EV performance under controlled laboratory conditions, there remains a critical gap in understanding how diverse real-world driving behaviors, environmental conditions, and charging practices influence both energy efficiency and the rate of battery degradation. This lack of comprehensive real-world analysis hinders the optimization of EV operation and the accurate prediction of battery lifespan.

This study addresses the aforementioned problem by employing a data-driven experimental approach to analyze the performance optimization and battery health of electric vehicles under authentic driving conditions. The research synthesizes a substantial volume of data, including telematics from over 10,000 EVs, a full year of detailed battery management system (BMS) data from an Audi e-tron, and aggregated fleet data from 140,000 EVs in China.

The methodology leverages advanced data science techniques, including machine learning and reinforcement learning, to uncover complex relationships between various real-world factors and EV attributes. Key variables analyzed include driving style, ambient temperature, charging habits (e.g., frequency of DC fast charging), and the efficacy of thermal management systems.

The findings underscore the significant impact of real-world variables on EV range and battery State of Health (SOH). For instance, results indicate that cold weather can diminish EV range by as much as 50%, while frequent fast charging and high ambient temperatures accelerate battery degradation. Conversely, the implementation of effective thermal management systems, particularly liquid cooling, is shown to substantially mitigate battery wear. The study demonstrates that by applying data-driven insights, it is possible to achieve a 10-15% improvement in EV range and reduce battery degradation to an average rate of 1.5-2% per year. These conclusions provide actionable strategies for optimizing EV usage, charging protocols, and design considerations to enhance overall performance and extend battery lifespan in practical applications.