| 基于遗传算法系统效率优化的PHEV电量保持模式控制策略 |
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| 引用本文： | 林歆悠,吴超宇,林海波.基于遗传算法系统效率优化的PHEV电量保持模式控制策略[J].中国公路学报,2018,31(5):174-182. |
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| 作者姓名： | 林歆悠 吴超宇 林海波 |
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| 作者单位： | 福州大学 机械工程及自动化学院, 福建 福州 350002 |
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| 基金项目： | 国家自然科学基金项目（51505086）；重庆市教育委员会科学技术研究项目（KJ1601118）；CAD/CAM福建省高校工程研究中心资助项目（K201710） |
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| 摘 要： | 为了提高插电式混合动力汽车（PHEV）在电量保持下的燃油经济性，并解决插电式混合动力汽车在运行过程中动力元件效率对系统能量利用率影响的问题，制定了系统效率最优的控制策略。以PHEV关键动力部件的测试数据为基础，建立发动机、驱动电机、无级变速器（CVT）以及动力电池等关键部件的效率数值模型，并考虑了温度及荷电状态（SOC）对动力电池充放电功率的影响。设计以混合动力系统效率最优为适应度评价函数，将CVT速比、发动机转矩作为优化变量，以车速、加速度和SOC为状态变量，在动力性指标的约束下，运用遗传算法进行迭代寻优，PHEV的系统效率在第20代左右收敛于全局最优值。同时发动机转矩和CVT速比通过多代遗传进化，较快收敛于最佳值。将相关优化结果与车速、加速度拟合成相应的三维控制数表，综合数值建模和试验测试数据建模的方法，基于MATLAB/Simulink搭建插电式混合动力汽车整车控制策略仿真模型，采用新欧洲行驶循环工况进行仿真验证。结果表明：插电式混合动力汽车在电量保持模式下，利用遗传算法优化的系统效率最优控制策略相比优化前，动力电池SOC运行更为平稳，CVT效率有所提升，驱动电机及发动机转矩分配更为合理；百公里燃油消耗量从优化前的5.2 L降至4.5 L，燃油经济性提升了13.5%。
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| 关 键 词： | 汽车工程 PHEV系统效率优化控制策略 遗传算法 电量保持模式 无级变速器 |
| 收稿时间： | 2017-06-30 |
Control Strategy of PHEV Charge-sustaining Mode Based on GA System Efficiency Optimization |
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| LIN Xin-you,WU Chao-yu,LIN Hai-bo.Control Strategy of PHEV Charge-sustaining Mode Based on GA System Efficiency Optimization[J].China Journal of Highway and Transport,2018,31(5):174-182. |
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| Authors: | LIN Xin-you WU Chao-yu LIN Hai-bo |
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| Affiliation: | School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350002, Fujian, China |
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| Abstract: | To improve the fuel economy of a plug-in hybrid electric vehicle (PHEV) in charge-sustaining mode, and to solve the problem regarding the effect of power component efficiency on system energy utilization when the PHEV is running, an optimal system efficiency control strategy was implemented. Based on the test data of the PHEV key power components, a numerical model of the efficiency of key components, such as the engine, driving motor, continuously variable transmission, and power battery, was established. Then, the temperature and battery state of charge (SOC) with an effect on the charge and discharge power were considered. The optimal hybrid system efficiency served as a fitness function, and the CVT ratio and engine torque served as optimization variables. The speed, acceleration, and SOC were considered as the state variables. The performance indicators were taken as the constraints. Then, iterative optimization was carried out by a genetic algorithm (GA). The system efficiency converged to the global optimum in the 20th generation. The CVT ratio and engine torque also converged to the optimum value by genetic evolutions through a series of generations. The results of the optimal control strategy and the vehicle speed and acceleration were fitted into the corresponding three-dimensional control tables. By integrating a numerical modeling method with an experimental data modeling method, a strategy simulation model of the vehicle was developed based on the MATLAB/Simulink software, and a simulation analysis was carried out in the new European driving cycle. The results reveal that, in comparison with the optimal system efficiency control strategy by the GA in the in-charge-sustaining mode of the PHEV, the battery SOC operation is more reasonable, the efficiency of the CVT improves steadily, and the torque between the engine and the motor is more reasonably distributed. The fuel consumption per 100 km is reduced from 5.2 L to 4.5 L, while the fuel economy increases by 13.5% in comparison with the fuel economy before optimization. |
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| Keywords: | automotive engineering PHEV system efficiency optimization control strategy genetic algorithm charge-sustaining mode CVT |
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