CNG-Electric Hybrid Powertrain for Light Commercial Vehicle. A Technical Evaluation of CNG Internal Combustion Engine, CNG-Electric Hybrid Series and Parallel Powertrains

Abstract

This master’s thesis presents a comprehensive technical evaluation of Compressed Natural Gas (CNG)-electric hybrid powertrains for Light Commercial Vehicles (LCVs), addressing the critical challenge of sustainable urban logistics through advanced simulation and control strategy analysis. Using Siemens Simcenter Amesim, three powertrain architectures conventional CNG Internal Combustion Engine (ICE), series hybrid, and parallel hybrid are rigorously compared across standardized Worldwide Harmonized Light Vehicles Test Cycle (WLTC) and Real Driving Emissions (RDE) cycles, as well as dynamic performance tests.

The study integrates sophisticated component models including a Mean Value Engine Model (MVEM) for the CNG engine, Permanent Magnet Synchronous Motors (PMSMs) with Field-Oriented Control, and an Equivalent Consumption Minimization Strategy (ECMS) for real-time energy management. Battery State of Charge (SoC) windows are optimized for each configuration: 55–65% for series hybrids (minimizing degradation) and 20–65% for parallel hybrids (maximizing electric range). Three-Way Catalytic Converters (3WCC) and regenerative braking systems are incorporated to enhance emissions reduction and energy recovery.

Results demonstrate that the parallel hybrid configuration achieves superior overall performance, delivering 18–28% fuel savings (4.15–5.89 kg/100 km) and 23–28% CO2 reduction (89.8–126.7 g/km) compared to the conventional ICE baseline, while maintaining robust dynamic performance (0–100 km/h in 12.0 s). The series hybrid excels in acceleration response (7.78 s) and achieves 70% CO reduction under steady-state RDE operation through constant-speed generator operation at peak efficiency (38% brake thermal efficiency). However, elevated CO emissions across all configurations (15–26 g/km, exceeding Euro 6 limits) highlight the need for enhanced aftertreatment strategies, specifically secondary air injection systems.

The parallel hybrid emerges as the optimal near-term solution for mixed-duty LCV operations, combining direct mechanical coupling, adaptive ECMS control (average power split ratio 0.67), and effective regenerative braking (approximately 15% energy recovery) to achieve the lowest load sensitivity and most balanced efficiencyperformance trade-off.

This work provides actionable insights for fleet operators, policymakers, and automotive engineers pursuing sustainable LCV electrification strategies. With targeted improvements including enhanced aftertreatment, machine learning-augmented ECMS, and optimized battery sizing through probabilistic modeling CNG-electric hybrids demonstrate potential for improved total emission reductions, positioning them as viable bridge technologies supporting EU Green Deal objectives and enabling decarbonized urban logistics by 2030.