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首页> 外文期刊>Proceedings of the Institution of Mechanical Engineers, Part D. Journal of Automobile Engineering >A simulation-based case study for powertrain efficiency improvement by automated driving functions
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A simulation-based case study for powertrain efficiency improvement by automated driving functions

机译：基于仿真型驾驶功能改进的仿真案例研究

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摘要

An increasing level of driving automation and a successive electrification of modern powertrains enable a higher degree of freedom to improve vehicle fuel efficiency and reduce pollutant emissions. Currently, both domains themselves, driving automation as well as powertrain electrification, face the challenge of a rising development complexity with extensive use of virtual testing environments. However, state-of-the-art virtual testing environments typically strictly focus on just one domain and neglect the other. This paper shows the results of a simulation-based case study considering both domains simultaneously. The influence of energy saving automated functionalities on a conventional, a hybrid, and a pure electric powertrain is investigated for a carefully selected inner-city driving scenario. The vehicle simulation models for the different powertrain configurations are calibrated using test bench results and vehicle measurements. A model predictive acceleration controller is developed for realizing the speed optimization function. By considering traffic conditions such as traffic light schedules and a preceding vehicle as the boundary conditions, unnecessary accelerations and decelerations are avoided to reduce the energy demand. The case study is realized by applying this function to the three powertrains variants. As a final result, a clear difference in energy demand is observed: the hybrid powertrain benefits the most in terms of energy demand reduction in the given use case. The results clearly underscore that in future vehicle development programs, the powertrain and the real-world driving functionalities have to be optimized simultaneously to minimize the energy demand during everyday vehicle operation.
机译：驾驶自动化水平的增加和现代动力的连续电气化使得能够改善车辆燃料效率和减少污染物排放的程度。目前，域名本身，驾驶自动化以及动力总成电气化，面临着增长的发展复杂性的挑战，广泛使用虚拟测试环境。然而，最先进的虚拟测试环境通常严格专注于一个域并忽略另一个域。本文显示了一种基于模拟的案例研究的结果，其同时考虑两个域。为仔细选择的内部城市驾驶场景研究了节能自动化功能对常规，混合动力和纯电动动力的影响。使用测试台面结果和车辆测量校准不同动力系配置的车辆仿真模型。开发了模型预测加速度控制器，用于实现速度优化功能。通过考虑交通条件，例如交通灯表和前面的车辆作为边界条件，避免了不必要的加速和减速以降低能量需求。通过将该功能应用于三种动力驱动变量来实现案例研究。作为最终结果，观察到能量需求的明显差异：混合动力系在给定用例中的能量需求方面最多。结果显然强调，在未来的车辆开发计划中，动力总成和现实世界驾驶功能必须同时优化，以最大限度地减少日常车辆操作期间的能源需求。

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