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-  2017 

增程式城市客车能量的分段跟踪优化方法
Piecewise tracking energy optimization approach for an extended-range electric city bus

DOI: 10.16511/j.cnki.qhdxxb.2017.22.024

Keywords: 能量优化,电量消耗管理,跟踪优化,模型预测控制,增程式电动汽车,
energy optimization
,state of charge (SOC) consumption management,tracking optimization,model predictive control,extended-range electric vehicle

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Abstract:

为在保证动力性的条件下提高增程式城市客车的燃油经济性,提出了一种基于电池荷电状态(SOC)消耗管理和功率分配的能量分段跟踪优化方法。该方法通过在每个控制周期内构建一个短期的需求功率预测序列,并设计参考曲线以实现SOC消耗管理的方式,建立了以费用最小为目标的动力系统功率分配的阶段性优化模型。引入模型预测控制方法,滚动优化并调整功率分配策略。基于该方法,一辆12 m增程式城市客车在中国城市公交工况下的100 km油耗为21.8 L,电耗为25.4 kWh,优于CDCS策略的结果(100 km油耗24.1 L,电耗25.4 kWh)。该方法能通过防止SOC在行程中被过早耗尽并使其在行程结束时降到最低,保证增程式城市客车的动力性并提高燃油经济性。
Abstract:A piecewise tracking energy optimization approach was developed to manage the battery state of charge (SOC) consumption and the splitting power to improve the fuel economy of extended-range electric city buses while ensuring their performance. The approach established a stage power splitting optimization model for each control period by constructing a power demand prediction sequence and designing a reference curve to manage the SOC consumption. Model predictive control was introduced for rolling optimization and strategy adjustment. For the Chinese city bus driving cycle, this approach enables a 12 meters extended-range electric city bus to use only 21.8 L fuel and 25.4 kWh electricity per 100 km, which are better than CDCS strategy based results (24.1 L fuel and 25.4 kWh electricity per 100 km). The results show that by preventing the SOC from running out during the route but only reaching its minimum, this approach ensures the dynamic performance and improves the fuel economy.

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