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Optimized recuperation strategy for (Hybrid) Electric Vehicles based on intelligent sensors

机译:基于智能传感器的(混合动力)电动汽车的最佳换气策略

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The limited range of Battery Electric Vehicles (BEV) is a major restraint for their market acceptance. This paper shows that efficiency — hence range — increase can be achieved by optimal distribution of driving and braking torque to the electric machines of an EV. Furthermore, a novel strategy for efficient recuperation of kinetic energy during the braking process of a (H)EV based on intelligent sensors is introduced. Both mentioned approaches take a detailed model of the vehicle's dynamics and powertrain characteristics into account. The strategy is utilized in an Advanced Driver Assistance System, which enables guided anticipatory driving for the driver. It is shown that the proposed semi-automatic process allows increased recuperation capabilities compared to state-of-the-art regenerative braking with torque blending. An improvement of energy regeneration efficiency of 31.7 percentage points compared to a standard serial regenerative braking system is achieved in a typical braking scenario.
机译:电池电动汽车(BEV)的有限范围是对其市场接受度的主要限制。本文显示,通过将驱动扭矩和制动扭矩最佳分配给电动汽车的电机,可以提高效率,从而提高行驶里程。此外,介绍了一种基于智能传感器的(H)EV制动过程中有效回收动能的新策略。提到的两种方法都考虑了车辆动力学和动力总成特性的详细模型。该策略在高级驾驶员辅助系统中使用,该系统可为驾驶员提供指导性的预期驾驶。结果表明,与采用扭矩混合技术的最新型再生制动相比,所提出的半自动过程可提高再生能力。在典型的制动情况下,与标准的串行再生制动系统相比,能量再生效率提高了31.7个百分点。

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