Driving performance impact analysis on EV components for efficient, safe, and intelligent mobility

Electric vehicles (EVs) are the cornerstone of sustainable transportation, but their performance and component longevity are heavily influenced by driving behaviors. This study proposes a comprehensive analytical framework to assess how different driving styles affect the operational health of key EV components such as the battery pack, motor, and DC-DC converter. Various driving styles such as aggressive, moderate, and economical are discriminated against using dynamic vehicle operation signatures including acceleration and braking intensity, turning profiles, and load variations. These behavioral patterns are reflected in the electrical responses, namely current and voltage waveforms across power electronic systems. By analyzing these electrical signatures, a range of KPIs can be estimated for each component, offering insights into their operational stress and degradation trends. Experimental analysis using real-time EV datasets validates the framework’s ability to predict and correlate driving patterns with component degradation trends. By evaluating these impacts, the study bridges the gap between driving behaviors and their consequences on EV performance in real-time operation. The research culminates in generating prescriptive and descriptive analytics, offering actionable insights and tailored recommendations for driving behavior improvement. Real-time suggestions empower drivers to adopt safer, more efficient styles, while actionable strategies provide a roadmap for EV manufacturers and policymakers to promote sustainable and efficient transportation systems. This study underscores the critical interplay between driving behavior and EV component health, paving the way for smarter, data-driven mobility solutions.