Optimized Energy-Efficient Speed Control in Lightweight Electric Vehicle using Dynamic Spiking Graph Neural Network

Efficient Electric Vehicle (EV) speed control ensures smooth acceleration, stability, and energy optimization under different driving conditions. The speed control system in lightweight EVs enables effective velocity regulation which improves both performance characteristics and energy optimization during driving operations. In lightweight EVs speed control operates with high sensitivity to external disturbances such as road inclines together with load variations but this result in inefficient energy utilization. In order to address these issues, this paper proposes an approach of Dynamic Spiking Graph Neural Network (DSGNN) for speed control in light weight EV. The main goal is to improve energy efficiency in lightweight EVs by optimizing speed control using DSGNN. DSGNN is utilized to predict speed variations in lightweight EVs, capturing temporal dependencies for accurate speed tracking and control. The proposed method undergoes implementation and evaluation using MATLAB against various existing approaches, such as Ultra-Local Model-Based Fuzzy QLearning Multi-Agent System (ULM-FQMAS), Ant Colony Optimization- Fractional-Order Proportional Integral Derivative (ACO-FOPID), Fuzzy Proportional Integral Derivative (FPID), Self-Constructing Type-2 Fuzzy Neural Network (SCT2FNN) and Adaptive Neuro-Fuzzy Model Predictive Control (ANFMPC). The proposed DSGNN method delivers improved energy efficiency by 29.21% which proves its effectiveness for optimizing speed control and power utilization in lightweight EVs.