This study presents a real-world data–validated framework for driving scenario definition and robustness analysis of a parallel hybrid vehicle using meta-heuristic optimization. A holistic MVEM-based ICE is developed in MATLAB/Simulink and validated using CAN-bus data, achieving trend correlation values above 0.91 and a fuel consumption error of less than 1%. Hybrid operation is formulated as a SoC-aware parametric driving scenario definition problem, in which the hybrid mode speed threshold governs electric-to-combustion transitions. Across initial SoC levels of 25%, 50%, 75%, and 100%, GA, JS, and WOA converge to consistent SoC-dependent optimal mode speed thresholds, ranging from approximately 15.8 km/h at low SoC to 42.4 km/h at full SoC. Implementation of these optimized scenarios under real-world driving conditions yields fuel consumption reductions of 6.96%–18.62% compared to a pure ICE configuration. The results demonstrate that SoC-aware scenario formulation exerts a stronger influence on solution characteristics than algorithm selection.
YavaşÖSavranENalburBE, et al. Energy and carbon loss management in an electric bus factory for energy sustainability. Transdiscipl J Eng Sci2022; 13(1): 97. https://doi.org/10.22545/2022/00207
2.
KęskaADziubekMMichalikD. Market positioning of internal combustion engines and battery electric motors. Combust Engines2023; 195(4): 136–143. https://doi.org/10.19206/CE-169803
3.
SavranEKarpatF. Simulation-based power strategy optimization in a diesel hybrid vehicle. Int J Simul Model2025; 24(3): 413–424. https://doi.org/10.2507/IJSIMM24-3-727
4.
MittalVShahR. Energy management strategies for hybrid electric vehicles: a technology roadmap. World Electr Veh J2024; 15(9): 1–17. https://doi.org/10.3390/wevj15090424
5.
ShiledarAVillaniMRizzoniG. Hardware-in-the-loop implementation of an optimized energy management strategy for range-extended electric trucks. Energies2024; 17(21): 1–21. https://doi.org/10.3390/en17215294
Vamsi Krishna ReddyAKVenkata Lakshmi NarayanaK. Meta-heuristics optimization in electric vehicles -an extensive review. Renew Sustain Energ Rev2022; 160: 112285. https://doi.org/10.1016/j.rser.2022.112285
8.
MarinoniAMOnoratiAManca Di VillahermosaG, et al. Real driving cycle simulation of a hybrid bus by means of a co-simulation tool for the prediction of performance and emissions. Energies2023; 16(12): 1–29. https://doi.org/10.3390/en16124736
9.
ZhangBZhaoGHuangY, et al. Optimal energy management for series-parallel hybrid electric city bus based on improved genetic algorithm. Mechanika2020; 26(3): 252–259. https://doi.org/10.5755/j01.mech.26.3.24133
10.
ZhangSWangHYangC, et al. Optimization of energy management strategy for series hybrid electric vehicle equipped with dual-mode combustion engine under NVH constraints. Appl Sci2024; 14(24): 1–23. https://doi.org/10.3390/app142412021
11.
ChenZZhangHXiongR, et al. Energy Management strategy of connected hybrid electric vehicles considering electricity and oil price fluctuations: a case study of ten typical cities in China. J Energy Storage2021; 36: 102347. https://doi.org/10.1016/j.est.2021.102347
12.
SavranEKarpatEKarpatF. Ga and WOA-Based optimization for electric powertrain efficiency. Int J Simul Model2024; 23(4): 599–610. https://doi.org/10.2507/IJSIMM23-4-699
13.
YanCZhangJShangX, et al. A hybrid power source and its control strategy optimization for low-speed electric vehicle. Proc Inst Mech Eng D J Automobile Eng. Epub ahead of print 8November2025. https://doi.org/10.1177/09544070251389156
14.
LiangXZhangBPengJ, et al. Data-driven analysis on hybrid electric vehicle configuration with optimal fuel economy in China. Proc Inst Mech Eng D J Automobile Eng2026; 240(5): 2897–2914. https://doi.org/10.1177/09544070251334688
15.
TangXXieYWangB, et al. Energy management strategy for hydraulic hybrid system with compound accumulator based on dynamic programming. Proc Inst Mech Eng D J Automobile Eng2023; 237(8): 1819–1829. https://doi.org/10.1177/09544070221102010
16.
WangSCuiTWangM, et al. Demand deviation based dynamic coordinated control strategy for parallel mild hybrid heavy-duty vehicle. Proc Inst Mech Eng D J Automobile Eng. Epub ahead of print 15January2026. https://doi.org/10.1177/09544070251412475
17.
PanMWenQSunW, et al. A driving cycle recognition-based optimized fuzzy energy management strategy for enhanced performance of parallel hybrid electric vehicles. Proc Inst Mech Eng D J Automobile Eng. Epub ahead of print 7March2026. https://doi.org/10.1177/09544070261427683
18.
ElkhoulyHIZakyEAAlmuflihAS, et al. Machine learning analysis of lot streaming hybrid flow shop scheduling with variable sub-lot sizes: impacts on makespan and tardiness. In: International conference on artificial intelligence. FICAILY 2025. Studies in computational intelligence, 2026, pp.1–10. https://doi.org/10.1007/978-3-032-00232-7_5
19.
TazayAFSamyMMBarakatS. A techno-economic feasibility analysis of an autonomous hybrid renewable energy sources for university building at Saudi Arabia. J Electr Eng Technol2020; 15(6): 2519–2527. https://doi.org/10.1007/s42835-020-00539-x
20.
SamyMMEmamATag-EldinE, et al. Exploring energy storage methods for grid-connected clean power plants in case of repetitive outages. J Energy Storage2022; 54: 105307. https://doi.org/10.1016/j.est.2022.105307
21.
GüvenAFSamyMM. Performance analysis of autonomous green energy system based on multi and hybrid metaheuristic optimization approaches. Energy Convers Manag2022; 269: 116058. https://doi.org/10.1016/j.enconman.2022.116058
22.
AlshammariNFSamyMMBarakatS. Comprehensive analysis of multi-objective optimization algorithms for sustainable hybrid electric vehicle charging systems. Mathematics2023; 11(7): 1–10. https://doi.org/10.3390/math11071741
23.
BarakatSSamyMMEteibaMB, et al. Feasibility study of grid connected PV-biomass integrated energy system in Egypt. Int J Emerg Electric Power Syst2016; 17(5): 519–528. https://doi.org/10.1515/ijeeps-2016-0056
24.
BarakatSOsmanAITag-EldinE, et al. Achieving green mobility: multi-objective optimization for sustainable electric vehicle charging. Energy Strategy Rev2024; 53: 101351. https://doi.org/10.1016/j.esr.2024.101351
25.
GuvenAFAbdelazizAYSamyMM, et al. Optimizing ENERGY DYNAMICS: a comprehensive analysis of hybrid energy storage systems integrating battery banks and supercapacitors. Energy Convers Manag2024; 312: 118560. https://doi.org/10.1016/j.enconman.2024.118560
26.
DjouahiANegrouBTougguiY, et al. Optimal sizing and thermal control in a fuel cell hybrid electric vehicle via FC-HEV application. J Braz Soc Mech Sci Eng2023; 45(10): 1–10. https://doi.org/10.1007/s40430-023-04437-x
27.
AbdeldjalilDNegrouBYoussefT, et al. Incorporating the best sizing and a new energy management approach into the fuel cell hybrid electric vehicle design. Energy Environ2025; 36(2): 616–637. https://doi.org/10.1177/0958305X231177743
28.
DjouahiANegrouBRouabahB, et al. Optimal sizing of battery and super-capacitor based on the MOPSO technique via a new FC-HEV application. Energies2023; 16: 3902. https://doi.org/10.3390/en16093902
29.
EteibaMBBarakatSSamyMM, et al. Optimization of an off-grid PV/biomass hybrid system with different battery technologies. Sustain Cities Soc2018; 40(1): 713–727. https://doi.org/10.1016/j.scs.2018.01.012
30.
SamyMMMosaadMIEl-NaggarMF, et al. Reliability support of undependable grid using green energy systems; economic study. IEEE Access2021; 9(1): 14528–14539. https://doi.org/10.1109/ACCESS.2020.3048487
31.
MokhtaraCNegrouBSettouN, et al. Design optimization of off-grid hybrid renewable energy systems considering the effects of building energy performance and climate change: case study of Algeria. Energy2021; 219: 119605. https://doi.org/10.1016/j.energy.2020.119605
32.
SamyMMElkhoulyHIBarakatS. Multi-objectiveoptimization of hybrid renewable energy system based on biomass and fuel cells. Int J Enery Res2021; 45(6): 8214–8230. https://doi.org/10.1002/er.5815
33.
SamyMMBarakatSRamadanHS. A flower pollination optimization algorithm for an off-grid PV-fuel cell hybrid renewable system. Int J Hydrogen Energy2019; 44(4): 2141–2152. https://doi.org/10.1016/j.ijhydene.2018.05.127
34.
BarakatSEmamASamyMM. Investigating grid-connected green power systems’ energy storage solutions in the event of frequent blackouts. Energy Rep2022; 8(1): 5177–5191. https://doi.org/10.1016/j.egyr.2022.03.201
35.
GüvenAFYörükerenNSamyMM. Design optimization of a stand-alone green energy system of university campus based on Jaya-harmony search and ant colony optimization algorithms approaches. Energy2022; 253: 124089. https://doi.org/10.1016/j.energy.2022.124089
36.
SamyMMAlmamlookREElkhoulyHI, et al. Decision-making and optimal design of green energy system based on statistical methods and artificial neural network approaches. Sustain Cities Soc2022; 84: 104015. https://doi.org/10.1016/j.scs.2022.104015
37.
GuoNZouYSunC, et al. Integrated cost-optimal convex optimization for eco-driving of connected hybrid electric vehicles on sloped and curved roads. IEEE Trans Vehicular Technol2026; 1(1): 1–13. https://doi.org/10.1109/TVT.2026.3674802
38.
GuoNZhangWChenW, et al. Universal bounds estimation and efficient tuning for equivalent factor in real-time cost-optimal predictive ECMS of PHEVs. IEEE Trans Transp Electrification2025; 11(1): 2796–2813. https://doi.org/10.1109/TTE.2024.3429129
39.
ZhouYHongwenHShouY, et al. An enhanced PMP-MPC energy management strategy incorporating driving condition characteristics. In: 2025 4th international conference on green energy and power systems (ICGEPS), 2025, pp.1–10. https://doi.org/10.1109/ICGEPS65133.2025.11034445.
HoljevacNCheliFGobbiM. A simulation-based concept design approach for combustion engine and battery electric vehicles. Proc Inst Mech Eng D J Automobile Eng2019; 233(7): 1950–1967. https://doi.org/10.1177/0954407018777350
43.
KochABürchnerTHerrmannT, et al. Eco-driving for different electric powertrain topologies considering motor efficiency. World Electr Veh J2021; 12(1): 1–19. https://doi.org/10.3390/WEVJ12010006
GuzzellaLOnderCH. Introduction to modeling and control of internal combustion engine systems. Springer, 2009.
46.
OnoratiAMontenegroG. 1D and multi-D modeling techniques for IC engine simulation. SAE, 2020.
47.
ShamekhiAMShamekhiAH. Spark ignition engine modeling and control system design: a guide to model-in-the-loop hierarchical control methodology. CRC Press, 2023.
48.
SavranEKarpatEKarpatF. Fuel cell electric vehicle hydrogen consumption and battery cycle optimization using bald eagle search algorithm. Appl Sci2024; 14(17): 1–19.
49.
ChaiTDraxlerRR. Root mean square error (RMSE) or mean absolute error (MAE)? – arguments against avoiding RMSE in the literature. Geosci Model Dev2014; 7(3): 1247–1250. https://doi.org/10.5194/gmd-7-1247-2014
50.
RingardJSeylerFLinguetL. A quantile mapping bias correction method based on hydroclimatic classification of the Guiana shield. Sensors2017; 17(6): 1–17. https://doi.org/10.3390/s17061413
51.
BurgundDNikolovskiSGalićD, et al. Pearson correlation in determination of quality of current transformers. Sensors2023; 23(5): 1–10. https://doi.org/10.3390/s23052704
