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<p>This work presents a comprehensive optimization study for enhancing the performance of a Parallel Hybrid Electric Vehicle (PHEV). The primary objectives were the simultaneous improvement of fuel economy and the reduction of pollutant emissions, namely CO, HC, and NOx, while ensuring all driving performance requirements were met. A multi-objective optimization (MLO) framework was developed and implemented using both the Particle Swarm Optimization (PSO) and DIRECT algorithms, with their performance evaluated against a mono-objective (MNO) approach and a pre-optimized baseline model. Simulations were conducted over standard driving cycles (UDDS and HWFET) using the ADVISOR/Simulink environment. The results demonstrate that the MLO-PSO algorithm is the most effective strategy, successfully converging to superior solutions that significantly outperform both the baseline vehicle and the MNO results. Key improvements included a marked increase in the operational efficiency of the Internal Combustion Engine (ICE) and Electric Motor (EM), smarter battery State of Charge (SOC) management, and a concurrent reduction in fuel consumption and emissions. Under the UDDS cycle, PSO-based optimization achieves fuel consumption reductions of up to 13.74%, compared to 10.33% obtained with the DIRECT method. CO emission reductions with PSO range from 13.26% to 13.41%, while DIRECT achieves higher CO reductions of up to 17.2%. HC emission reductions under UDDS reach 15.66% with MLO-PSO, compared to 14.56% with MNO-DIRECT. Under the HWFET cycle, PSO further improves fuel economy, reaching a maximum reduction of 16.92%, while DIRECT achieves up to 13.33%. MLO-PSO also enables limited emission benefits, including a slight NOx reduction of 0.61% under HWFET, highlighting the trade-off between fuel economy and emission control. The simulation results demonstrate that PSO-based strategies has remarkable superiority in MLO. This study conclusively shows that a properly weighted MLO approach is essential for overcoming the inherent trade-offs in PHEV design, resulting in a vehicle that is more efficient, less polluting, and performs reliably.</p>

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Enhanced fuel economy and emission reduction in parallel HEV powertrains through swarm and deterministic algorithms

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月19日

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  • Naila Ben Halima,
  • Nahla Ben Halima,
  • Naourez Ben Hadj,
  • Rafik Neji

摘要

This work presents a comprehensive optimization study for enhancing the performance of a Parallel Hybrid Electric Vehicle (PHEV). The primary objectives were the simultaneous improvement of fuel economy and the reduction of pollutant emissions, namely CO, HC, and NOx, while ensuring all driving performance requirements were met. A multi-objective optimization (MLO) framework was developed and implemented using both the Particle Swarm Optimization (PSO) and DIRECT algorithms, with their performance evaluated against a mono-objective (MNO) approach and a pre-optimized baseline model. Simulations were conducted over standard driving cycles (UDDS and HWFET) using the ADVISOR/Simulink environment. The results demonstrate that the MLO-PSO algorithm is the most effective strategy, successfully converging to superior solutions that significantly outperform both the baseline vehicle and the MNO results. Key improvements included a marked increase in the operational efficiency of the Internal Combustion Engine (ICE) and Electric Motor (EM), smarter battery State of Charge (SOC) management, and a concurrent reduction in fuel consumption and emissions. Under the UDDS cycle, PSO-based optimization achieves fuel consumption reductions of up to 13.74%, compared to 10.33% obtained with the DIRECT method. CO emission reductions with PSO range from 13.26% to 13.41%, while DIRECT achieves higher CO reductions of up to 17.2%. HC emission reductions under UDDS reach 15.66% with MLO-PSO, compared to 14.56% with MNO-DIRECT. Under the HWFET cycle, PSO further improves fuel economy, reaching a maximum reduction of 16.92%, while DIRECT achieves up to 13.33%. MLO-PSO also enables limited emission benefits, including a slight NOx reduction of 0.61% under HWFET, highlighting the trade-off between fuel economy and emission control. The simulation results demonstrate that PSO-based strategies has remarkable superiority in MLO. This study conclusively shows that a properly weighted MLO approach is essential for overcoming the inherent trade-offs in PHEV design, resulting in a vehicle that is more efficient, less polluting, and performs reliably.