Maximizing electric vehicle efficiency: Integrating direct torque control for two-wheel drive system using performance enhancing approach

Neelam Maheswari, Jessi Sahaya, Palani Rama Mohan and J. Joselin Jeya Sheela

Energy, 2025, vol. 324, issue C

Abstract: The transition to electrified transportation has become essential to mitigate carbon dioxide (CO2) emissions and combat climate change. This paper presents an energy management scheme combining an Improved Osprey Optimization Algorithm (IOOA) and a Binarized Single-Spike Supervised Spiking Neural Network (BS4NN), referred to as the IOOA-BS4NN approach. The proposed system utilizes Fuel Cells (FC), Super capacitors (SC), and Batteries to optimize performance. IOOA minimizes energy consumption, while BS4NN predicts torque fluctuations. MATLAB/Simulink simulations demonstrate the effectiveness of the approach, achieving a 97 % efficiency compared to 87 % for Heap-Based Optimizer (HBO), 77 % for Nomadic People Optimizer (NPO), and 67 % for Wild Horse Optimizer (WHO). Fuel consumption is reduced to 54 Ipm from 64 Ipm (HBO), 74 Ipm (NPO), and 84 Ipm (WHO). Air consumption is minimized to 50 Ipm, outperforming HBO (60 Ipm), NPO (70 Ipm), and WHO (80 Ipm). These results highlight significant reductions in power usage, fuel, and air consumption, establishing IOOA-BS4NN as a superior solution for enhancing energy efficiency and environmental sustainability in electric vehicles (EVs).

Keywords: Energy management; Super capacitor; Battery; EV; Fuel cell; Model predictive direct torque control; State of charge (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:324:y:2025:i:c:s0360544225008795

DOI: 10.1016/j.energy.2025.135237

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