基于机器学习与模糊逻辑的PHEV控制策略，显著提升纯电续航并降低油耗。
A novel ML-fuzzy control system for optimizing PHEV fuel efficiency and extending electric range under diverse driving conditions
- 融合机器学习与模糊逻辑预测能耗，动态分配动力模式。
- 纯电续航达84公里（20kWh电池80%利用率），WLTC油耗降20%至2.86L/100km。
- 适配不同驾驶习惯与电池健康状态，适合追求能效优化的车企与开发者。
为实现绿色交通愿景，本文提出一种新型机器学习-模糊逻辑控制系统，用于插电式混合动力汽车（PHEVs）的能量管理。该系统通过机器学习预测车辆纯电模式下的能耗，并优化纯电、串联混动、并联混动及内燃机运行模式之间的功率分配。模糊逻辑决策机制主导控制流程。在多种驾驶条件下评估性能：纯电模式效率显著提升，在20kWh电池包80%利用率下实现约84公里的全电动续航；在WLTC驾驶循环中，燃油消耗降至2.86 L/100km，较基准系统降低20%汽油当量。不同车速测试显示，低速时电池充电，高速时放电，能量回收与消耗策略更优。初始电量影响显著，90%初始电量可延长纯电行驶时间，使油耗低于基准系统2 L/100km。真实驾驶数据分析表明，短途慢速循环优先使用电力，长途快速循环则增加内燃机使用。系统还适应不同电池健康状态（SOH），高SOH可将总油耗减少最高达2.87 L/100km。
原文摘要 · Abstract (English)
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.
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