An Adaptive Control Framework for Optimizing Hybrid Electric Vehicle Performance Using Road Gradient Detection
Abstract
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.
Keywords
Full Text:
PDFReferences
Aryza, S., Efendi, S., & Sihombing, P. (2024). A Robust Optimization to Dynamic Supplier Decisions and Supply Allocation Problems in the Multi-Retail Industry. Eastern-European Journal of Enterprise Technologies, (3).
Adaptive energy management strategy for HEVs in dynamic environments based on reinforcement learning(2025). IEEE Transactions / Energy Management Journal.
B. Li, L. Xiong and B. Leng, (2023) "Adaptive Anti-slip Regulation Method for Electric Vehicle with In-wheel Motors Considering the Road Slope," IEEE Intelligent Vehicles Symposium (IV), Changshu, China, pp. 1-6, doi: 10.1109/IVS.2018.8500399.
Choi, H., et al (2022). Hybrid Electric Vehicle Development with Solar Energy Integration. Journal of Cleaner Production, 360, 132451.
Ge, Y. (2024). Adaptive control of plug in hybrid electric vehicles based on energy management strategy and dynamic programming algorithm. International Journal of Renewable Energy Development, 13(6), 1104–1114.
Hulagu, S., & Celikoglu, H. B. (2021). Comparative evaluation of macro and micro approaches to emission modeling using GPS data: a case study. Transportation Research Procedia, 52, 629-636.
Lin, H., & Xu, Z. (2022). Energy Management and Optimization for Hybrid Electric Vehicles: A Review. Renewable and Sustainable Energy Reviews, 158, 112104.
Lee, H., Song, C., Kim, N., & Cha, S. W. (2023). Actor critic TD3 based deep reinforcement learning for energy management strategy of HEV. In 2023 5th International Congress on H C Optimization and Robotics (HORA), IEEE.
Liu, Z.et al (2023). Real time energy management for HEV combining naturalistic driving data and deep reinforcement learning with high generalization. Applied Energy, 377, 124350.
Marwan Liange He, et al (May 2025). Adaptive energy management strategy for Extended Range Electric Vehicles under complex road conditions based on RF IGWO and MGO. Energy, 328(2), 136500.
Ma, H., et al (2023). Vehicle Mass and Road Gradient Estimation by Series Kalman Filter and 3 Axis Accelerometer for Real World Application. SAE Technical Paper 2023 01 1677. Metodologi ini sangat relevan untuk sistem deteksi gradien menggunakan sensor akselerometer
Nguyen, T. et al (2022). Recent Advances in Electric Vehicle Technologies. Transportation Research Part D: Transport and Environment, 104, 103207.
Patel, R., & Desai, V. (2021). Comparative Study of Electric Motor Drives for Automotive Applications. International Journal of Automotive Technology, 22(5), 1167-1176.
Gonzalez, M., Hernandez, P., & Ruiz, A. (2021). Efficiency Analysis of Induction Motors for Electric Vehicles Applications. Energy Conversion and Management, 243, 114400.
Wang, X., Song, G., Wu, Y., & Zhai, Z. (2023). Road Grade Estimation Based on Power Demand Difference of Heavy Duty Diesel Trucks for Emission Estimation. Transportation Research Record, 2677(1).
Yue, H., et al 2024). Optimization of Shift Strategy Based on Vehicle Mass and Road Gradient Estimation. World Electric Vehicle Journal, 15(12), 545.
Zhang, X., & Liu, Y. (2021). Adaptive Control Strategies for Electric and Hybrid Electric Vehicles: A Survey. IEEE Transactions on Industrial Electronics, 68(9), 7604-7614.
Zhu, Z.-k., He, C., Li, J.-q., & Liu, X.-y. (2023). Estimation of Vehicle Mass and Road Grade of Electric Vehicle Based on Hybrid Algorithm. Journal of Highway and Transportation Research and Development, 17(2), 48 55.
Zulkarnain Lubis, Solly Aryza (2023). An Improvement Control Performance of AC Motor 3 Phase Water Tower Centrifugal Pump. Jurnal Scientia, Volume 12 No 4, 2023, ISSN 2302-0059.
Zulkarnain Lubis (2022). Hybrid Electric Vehicles (HEV) - DC Motor Coupled Three Phase Induction Motor for Automotive Application. ISBN: 978-623-7297-51-2. SEMNASTEK - UISU 2022.
Zulkarnain Lubis (2022). New Model of Smartphone Usage for Starter, Stop, and Safety Automotive with Voice Command Based on Arduino Uno. Vol.6 No.3 August 2022. P-ISSN: 2548-9739, E-ISSN: 2685-5240 (SENATIKA) -3 – 2022.
Zulkarnain Lubis, Solly Aryza, RG (2024). PLC Analysis in Controlling Robotic Instrument Hardware in the Development of Home Industry. ESCAF 2024, p-ISSN: 2962 – 7710, e-ISSN: 3021 – 8594.
Zhu, Y., et al (2022) Review article: A comprehensive review of energy management strategies for hybrid electric vehicles, Mech. Sci., 13, 147–188, https://doi.org/10.5194/ms-13-147.
DOI: http://dx.doi.org/10.30829/zero.v9i1.24302
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Publisher : Department of Mathematics Faculty of Science and Technology Universitas Islam Negeri Sumatera Utara Medan | |
✉️ Email: zero_journal@uinsu.ac.id 📱 WhatsApp:085270009767 (Admin Official) | |
 |  |  |  |  |