Aiming for a greener transportation future, this study introduces an innovative control system for plug-in hybrid electric vehicles (PHEVs) that utilizes machine learning (ML) techniques to forecast energy usage in the pure electric mode of the vehicle and optimize power allocation across different operational modes, including pure electric, series hybrid, parallel hybrid, and internal combustion operation. The fuzzy logic decision-making process governs the vehicle control system. The performance was assessed under various driving conditions. Key findings include a significant enhancement in pure electric mode efficiency, achieving an extended full-electric range of approximately 84 kilometers on an 80% utilization of a 20-kWh battery pack. During the WLTC driving cycle, the control system reduced fuel consumption to 2.86 L/100km, representing a 20% reduction in gasoline-equivalent fuel consumption. Evaluations of vehicle performance at discrete driving speeds, highlighted effective energy management, with the vehicle battery charging at lower speeds and discharging at higher speeds, showing optimized energy recovery and consumption strategies. Initial battery charge levels notably influenced vehicle performance. A 90% initial charge enabled prolonged all-electric operation, minimizing fuel consumption to 2 L/100km less than that of the base control system. Real-world driving pattern analysis revealed significant variations, with shorter, slower cycles requiring lower fuel consumption due to prioritized electric propulsion, while longer, faster cycles increased internal combustion engine usage. The control system also adapted to different battery state of health (SOH) conditions, with higher SOH facilitating extended electric mode usage, reducing total fuel consumption by up to 2.87 L/100km.

翻译：面向绿色交通的未来，本研究提出了一种用于插电式混合动力汽车（PHEV）的创新控制系统。该系统利用机器学习（ML）技术预测车辆纯电动模式下的能量使用情况，并优化纯电动、串联混合动力、并联混合动力及内燃机运行等多种工作模式间的功率分配。车辆控制系统由模糊逻辑决策过程主导。研究在不同驾驶条件下评估了系统性能。主要发现包括：纯电动模式效率显著提升，在利用20千瓦时电池组80%电量的情况下，实现了约84公里的扩展全电续航里程。在WLTC驾驶循环中，控制系统将燃油消耗降低至2.86升/100公里，相当于汽油当量燃油消耗降低了20%。对车辆在离散驾驶速度下的性能评估突显了有效的能量管理：车辆电池在较低速度下充电，在较高速度下放电，展示了优化的能量回收与消耗策略。电池初始荷电状态对车辆性能有显著影响。90%的初始电量可实现更长的全电行驶，将燃油消耗降至比基准控制系统低2升/100公里。实际驾驶模式分析显示出显著差异：较短、较慢的循环因优先使用电力驱动而需要更低的燃油消耗，而较长、较快的循环则增加了内燃机的使用。该系统还能适应不同的电池健康状态（SOH）条件，较高的SOH有助于延长电动模式使用时间，使总燃油消耗最多降低2.87升/100公里。