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摘要:
能量管理策略是混合动力汽车的核心技术之一，决定了车辆的燃油经济性和排放性能。针对现有混合动力汽车的能量管理都是基于固定工况开发而没有考虑实际道路工况的问题，基于智能交通系统（ITS）和专用短程通信技术（DSRC）获取的道路交通信息和周边车辆信息，提出了一种网联混合动力汽车分层能量控制方法。其中，上层控制器利用道路交通信息和模型预测控制算法预测车辆的最优目标速度并计算出需求转矩；下层控制器利用上层控制器获得的目标车速信息，实现最优车速跟随，并使用模糊神经网络控制算法优化发动机和电动机之间的转矩分配以降低燃油消耗。仿真结果表明：与传统的能量管理策略相比，所提方法可以有效避免车辆在红灯时停车，车辆的燃油消耗率降低了34.88%，HC、CO和NO
x 的排放分别降低10.59%、66.19%和1.05%，提升了混合动力汽车的燃油经济性和排放性能。Abstract:Energy management strategy is one of the core technologies of hybrid electric vehicles, which determines the fuel economy and emission performance of the vehicle. Aiming at the problem that the existing energy management strategies of hybrid electric vehicles are all developed based on the fixed operating conditions without considering the actual road driving conditions, proposes a hierarchical energy control method for hybrid electric vehicles in the connected environment based on the road trcoffic in formation and surrounding vehicle in formation obtained by intelligent transportation system (ITS) and dedicated short range communication (DSRC) technology. Road traffic information and model predictive control algorithm are utilized to predict the optimal velocity of vehicle in the upper controller. The lower controller is designed to follow the optimal velocity by using target vehicle velocity information obtained in the upper controller, and uses the fuzzy neural network control algorithm to optimize the torque distribution between the engine and the motor to reduce fuel consumption. The simulation results show that, compared with the traditional energy management strategy, the proposed method can avoid the vehicle stopping at the red light effectively, so that the fuel consumption rate of the vehicle is reduced by 34.88%, and the emission of HC, CO, and NO
x are reduced by 10.59%, 66.19%, and 1.05%, respectively, which improves the fuel economy and emission performance of hybrid electric vehicles. -
图 1 并联式混合动力汽车结构示意图
Figure 1. Schematic diagram of parallel hybrid electric vehicle structure
图 6 基于交通信息和模型预测控制的车速预测曲线
Figure 6. Prediction curves of vehicle velocity based on traffic information and MPC
图 9 基于规则和模糊神经网络的车辆尾气排放
Figure 9. Vehicle exhaust gas emission based on rules and fuzzy neural network
表 1 车辆主要部件参数
Table 1. Parameters of vehicle's main components
主要部件 参数名称 数值 发动机 最大功率/kW 41 电动机 最大功率/kW 75 蓄电池 容量/Ah 16 整备质量/kg 1 350 迎风面积/m2 2 轮胎半径/m 0.343 整车 滚动阻力系数 0.018 主减速器比 4.8 空气阻力系数 0.335 空气密度/(kg·m-3) 1.2 
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表 2 两种控制策略仿真结果对比
Table 2. Comparison of simulation result between two control strategies
控制策略 CO排放量/
(g·km-1)HC排放量/
(g·km-1)NOx排放量/
(g·km-1)FC/
(L·(100 km)-1)终止
SOC电机驱动
效率整车控制
系统效率基于规则的控制策略 7.879 0.557 0.286 4.3 0.649 3 0.59 0.147 基于模糊神经网络的控制策略 2.664 0.498 0.283 2.8 0.618 7 0.74 0.184
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