Fault-Aware and Predictive Energy Management for Hybrid Energy Storage Systems in Electric Vehicles Using Mamdani Fuzzy Logic

Abstract: Hybrid Energy Storage Systems (HESS) integrating lithium-ion batteries with supercapacitors are increasingly adopted in electric vehicles (EVs) for dynamic power management. While Fuzzy Logic–based Energy Management Systems (EMS) effectively optimize power-split ratios under nominal operating conditions, they remain insensitive to hardware anomalies including battery overcurrent, thermal excursions, supercapacitor degradation, and converter faults. This paper presents a fault-aware intelligent EMS framework built around a Mamdani Fuzzy Inference System (FIS) that continuously monitors four sensor channels—battery voltage, current, temperature, and state-of-charge (SOC)—and classifies six distinct fault categories in real time via a dedicated Severity Index (SI ∈ [0, 1]). Upon fault detection, the controller adaptively modifies the battery duty cycle k_bat and redistributes transient power demands to the supercapacitor, preserving load continuity and system safety. MATLAB/Simulink simulations incorporating non-ideal component models, thermal dynamics, and converter losses demonstrate a 30% reduction in peak battery current, a 29% decrease in thermal rise (ΔT), and a 20% improvement in SOC retention relative to a conventional HESS without fault awareness. DC bus voltage stability (MAD = 8 V) is fully maintained across all injected fault scenarios. The proposed framework bridges the critical gap between energy optimization and hardware fault management in HESS for EV applications.

Keywords: Hybrid Energy Storage System (HESS), Mamdani Fuzzy Inference System, Fault Detection and Classification, Energy Management System, Battery State-of-Charge, Supercapacitor, Thermal Management, Electric Vehicles, DC-DC Converter, Severity Index