Development and Experimental Validation of a Blended Forward–Backward Dynamic Model for HEV Powertrain Energy Management
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- Publication code
- 20264526
- Paper/Info type
- International Journal of Automotive Engineering
Vol.17 No.3
- Pages
- 106-118(Total 13 p)
- Date of publication
- Jul 2026
- Publisher
- JSAE
- Language
- English
Detailed Information
| Category(E) | Research paper |
|---|---|
| Author(E) | 1) Rakesh V. Mulik, 2) Ekambaram Porpatham, 3) Senthil Kumar Arumugam |
| Affiliation(E) | 1) School of Mechanical Engineering, 2) School of Mechanical Engineering / Automotive Research Centre, 3) School of Mechanical Engineering / CO2 Research and Green Technology Center |
| Abstract(E) | The progressive depletion of petroleum-based energy reserves, coupled with the intensifying threat of global climate change, has catalyzed an urgent global imperative to explore alternative energy sources to conventional fossil fuels. Although electric vehicles offer a promising solution by eliminating tailpipe emissions, their widespread adoption is hindered by range anxiety and insufficient charging infrastructure. In contrast, Hybrid Electric Vehicles can serve as a practical transitional technology, offering improved fuel economy with reduced emissions by operating both the IC engine and electric motor in their best efficiency regions. The process of developing HEVs involves various modeling techniques, including Model-in-Loop (MiL), Software-in-Loop (SiL), and Hardware-in-Loop (HiL). In MiL, kinematic, quasi-static, and dynamic modeling techniques are employed to develop accurate digital twins and predict vehicle behavior across various driving conditions. This study introduces a novel hybrid simulation framework that integrates forward and backward simulation techniques to develop a full-parallel P3-type hybrid electric vehicle architecture. The input data for the developed model are generated from actual power plant testing to improve the system's accuracy. The proposed framework facilitates a comprehensive evaluation of energy management strategies and fuel consumption metrics. A deterministic, rule-based control algorithm incorporating PID tuning and engine start-stop functionality is developed to optimize power distribution between the internal combustion engine and the electric motor. Simulation results demonstrate a fuel-efficiency improvement of approximately 30% relative to a conventional baseline vehicle, with strong correlation with experimental data, with deviations of 4–6%. |
