The transition within transportation towards battery electric vehicles can lead to a more sustainable future. To account for the development goal ‘climate action’ stated by the United Nations, it is mandatory, within the conceptual design phase, to derive energy-efficient system designs. One barrier is the uncertainty of the driving behaviour within the usage phase. This uncertainty is often addressed by using a stochastic synthesis process to derive representative driving cycles and by using cycle-based optimization. To deal with this uncertainty, a new approach based on a stochastic optimization program is presented. This leads to an optimization model that is solved with an exact solver. It is compared to a system design approach based on driving cycles and a genetic algorithm solver. Both approaches are applied to find efficient electric powertrains with fixed-speed and multi-speed transmissions. Hence, the similarities, differences and respective advantages of each optimization procedure are discussed.

交通运输向电池电动汽车的过渡可以带来更可持续的未来。考虑到联合国提出的发展目标“气候行动”，在概念设计阶段，必须进行节能系统设计。一个障碍是使用阶段内驾驶行为的不确定性。这种不确定性通常通过使用随机综合过程来获得代表性的驾驶周期和使用基于周期的优化来解决。为了处理这种不确定性，提出了一种基于随机优化程序的新方法。这导致使用精确求解器求解的优化模型。它与基于驾驶周期和遗传算法求解器的系统设计方法进行了比较。这两种方法都适用于寻找具有固定速度和多速变速器的高效电动动力系统。因此，讨论了每个优化过程的相似之处、不同之处和各自的优点。