论文标题
机会限制了电动汽车能源管理的优化
Chance Constrained Optimization for Energy Management in Electric Vehicles
论文作者
论文摘要
未来电动汽车的E-PowerTrain可以由能源发电单元（例如燃料电池和光伏模块），能源存储系统（例如电池和超级电容器），能量转换单元（例如，双向DC/DC/DC/DC Converters和dc/ac Ac倒置器）以及电动机和电动机的生成和Motores [1-摩托车，都可以使用。能源管理系统负责以满足技术约束的方式操作上述组件。应通过解决优化问题来完成此任务，该问题旨在最大程度地降低总运营成本[5]。确定性方法[7]已广泛解决了优化问题，该方法考虑了主动反应载荷轮廓的预测值。但是，如图1（a）所示，不可能准确地预测值，这意味着来自确定性方法的解决方案可能会导致不可行的操作（即违反约束）。因此，在考虑不确定参数的同时，应将随机优化方法[8]用于FI最佳解决方案策略。
E-powertrain of future electric vehicles could consist of energy generation units (e.g., fuel cells and photovoltaic modules), energy storage systems (e.g., batteries and supercapacitors), energy conversion units (e.g., bidirectional DC/DC converters and DC/AC inverters) and an electric machine, which can work in both generating and motoring modes [1- 6]. An energy management system is responsible to operate the above-mentioned components in a way that the technical constraints are satisfied. This task should be accomplished by solving an optimization problem, which could aim at minimizing the total operation costs [5]. The optimization problem has been widely addressed by deterministic approaches [7], which take into account the forecasted values of active-reactive load profile. However, as shown in Figure 1 (a), it is impossible to accurately forecast the values, meaning that the solutions coming from deterministic approaches could lead to infeasible operations (i.e., constraint violations). Therefore, stochastic optimization approaches [8] should be utilized to fi nd optimal solution strategies while considering uncertain parameters.
