An Adaptive Control Framework for Optimizing Hybrid Electric Vehicle Performance Using Road Gradient Detection

Lubis, Zulkarnain (Universitas Islam Sumatera Utara)
Annisa, Selly (Faculty Engineering, Universitas Negeri Medan Sumatera Utara, Medan, Indonesia)
Beg, A. H. (School of Computing and Mathematics, Charles Sturt University, Panorama Avenue, Bathurst 2795, Australia)

Zero : Jurnal Sains, Matematika, dan Terapan
Vol 9, No 1 (2025): Zero: Jurnal Sains Matematika dan Terapan

This study introduces an adaptive control framework for hybrid electric vehicles (HEVs) that optimizes performance based on real-time road gradient conditions, such as uphill and downhill terrain. Utilizing an inclinometer and an accelerometer, the system continuously monitors road angle and vehicle dynamics. The adaptive control algorithm processes this data to adjust the output of both the electric motor and internal combustion engine, optimizing energy efficiency and vehicle performance. Experimental results on hilly routes show an 8% improvement in energy efficiency compared to conventional control systems. Additionally, the system ensures stable vehicle speed with an average deviation of ±2.5 km/h. These findings highlight the potential of gradient-based adaptive control to enhance HEV performance, especially on challenging terrains, by improving energy efficiency and driving stability. This approach offers a promising solution for future HEV applications in regions with varied topography.