再生制动系统预测模型的建立

Andrea Caratti, Gabriele Catacchio, Carlo Gambino, N. Kar
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引用次数: 15

摘要

该项目解决的基本问题是恢复传统车辆制动过程中失去的动能。有了再生制动系统(RBS),就有可能通过将动能转化为电能来减缓车辆的速度,这些电能可以立即使用,也可以储存起来,直到需要的时候再使用。这与传统的制动系统形成对比,在传统的制动系统中,多余的动能通过摩擦转化为热量并浪费在环境中。在混合动力汽车中,再生制动动作是利用电动机作为发电机来完成的。通过这种方式,来自车轮的能量从动能转化为电能,转子和定子绕组之间的磁摩擦提供了制动效果。这项研究的目的是设计一个电力系统模型,可以将动能转换并存储到高压电池中。该项目研究的动力系统配置为轻度和全混合动力,其中内燃机与电动机相结合,除了再生制动动作外,还能提供启动/停止和动力辅助功能。研究的最终结果由一个工具表示,该工具已在Matlab®中实现,以预测执行新欧洲驾驶循环的车辆中电量的时间变化。该模型还为根据目标电气参数确定系统主要部件的尺寸提供了预测工具。最后,分析了该系统在效率、燃料消耗和减排方面的改进。相对于现有的模型,这种方法需要很少的主要输入参数来描述RBS,从而具有更高的灵活性和更广泛的应用范围。
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Development of a predictive model for Regenerative Braking System
The basic problem that this project addresses is the recovery of the kinetic energy lost during braking in a conventional vehicle. With Regenerative Braking Systems (RBS) it is possible to slow a vehicle down by converting its kinetic energy into electric energy, which can be either used immediately or stored until needed. This contrasts with conventional braking systems, where the excess kinetic energy is converted into heat by friction and wasted into the environment. In hybrid electric vehicles, the regenerative braking action is performed using the electric motor as a generator. In this way the energy from the wheels is converted from kinetic into electric and the magnetic friction between the rotor and the stator windings provides the braking effect. The aim of this study is to design a model of an electric system that allows converting the kinetic energy and storing it into a high voltage battery. The powertrain configurations investigated in this project are the mild and full hybrids, in which the internal combustion engine is coupled with an electric motor, able to provide a start/stop and a power assist functions in addition to the regenerative braking actions. The final result of the study is represented by a tool that has been implemented into Matlab® in order to predict the time variation of the electric quantities in a vehicle performing the New European Driving Cycle. This model also offers a predictive tool for dimensioning the main components of the system, according to the target electric parameters. Finally, the improvements that such a system could give in terms of efficiency, fuel consumption and emissions reduction have been analyzed. With respect to the existing models, this approach requires few main input parameters to characterize the RBS, resulting in a higher flexibility and a wider range of application.
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