Investigation of battery-aging dynamics for electric vehicle management under real-world driving stress

The aging characteristics of lithium-ion (Li-ion) batteries under realistic operating conditions significantly affect the long-term reliability, safety, and operational efficiency of electric vehicles (EVs). Batteries in real-world services are subjected to complex, coupled stressors such as aggressive acceleration, regenerative braking, fast charging, temperature variation, high state-of-charge (SOC) operation and irregular depth-of-discharge (DOD) patterns, which differ from controlled laboratory cycling. These conditions accelerate nonlinear degradation pathways including solid electrolyte interphase (SEI) growth, lithium plating, cathode degradation, electrolyte decomposition, impedance growth and thermal instability. This paper presents a comprehensive review of battery aging dynamics in EVs, emphasising the link between driving stress, degradation evolution, and management strategies. Four classes of aging models; electrochemical, empirical/semi-empirical, equivalent-circuit and machine learning, are compared in terms of prediction ability for onboard battery management systems (BMSs). This review also introduces the Real-World Aging Stress Index (RWASI), a normalization-based composite metric that combines kinematic intensity, DC fast-charging fraction, ambient temperature deviation and SOC variation into a single bounded descriptor of cumulative degradation. Aging-aware BMS architectures incorporating adaptive charging control, digital twins, cloud-assisted diagnostics, and estimation for state-of-health (SOH) and remaining useful life (RUL) prediction are discussed, providing an integrated perspective to enhance EV durability, safety, and lifetime sustainability.