基于道路工况预测混合动力公交车SOC开环控制策略

混合动力车辆一般采用基于荷电状态（SOC）闭环的控制策略,对蓄电池组进行频繁充电,使SOC维持在较高水平,影响制动能量的回收,从而导致燃油经济性不理想.为此,利用BP神经网络并结合城市公交运行特点,提出SOC开环控制策略,对公交车未来站点间的运行工况进行预测,减少蓄电池组的充电次数,降低蓄电池组的荷电状态.试验表明,采用该控制策略可以显著降低电池组充电时间和次数,有利于制动能量的回收,百公里油耗降低了3%.

Control Strategy of SOC Open-Loop of Hybrid Electric Bus Based on Driving Cycle Prediction

The conventional strategy of hybrid electric vehicle was based on the state of charge （SOC）closed-loop which charged the battery frequently to sustain a high-level SOC and decreased the fuel efficiency because the energy from the regenerative braking couldn＇t be stored any more. SOC open-loop control strategy was proposed using the BP neural network and considering the trait of city bus to predict the next driving cycles between two stops in short future. Experiment result shows that the control strategy is useful to decrease charging times and time-period, which improve fuel efficiency by 3% higher than the vehicle on the route with the control strategy of SOC closed-loop because of en- hanced regenerative braking energy.