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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Vehicle Deceleration Prediction Model to Reflect Individual Driver Characteristics by Online Parameter Learning for Autonomous Regenerative Braking of Electric Vehicles
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Vehicle Deceleration Prediction Model to Reflect Individual Driver Characteristics by Online Parameter Learning for Autonomous Regenerative Braking of Electric Vehicles

机译：通过在线参数学习反映电动汽车自动再生制动的驾驶员减速度预测模型

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摘要

The connected powertrain control, which uses intelligent transportation system information, has been widely researched to improve driver convenience and energy efficiency. The vehicle state prediction on decelerating driving conditions can be applied to automatic regenerative braking in electric vehicles. However, drivers can feel a sense of heterogeneity when regenerative control is performed based on prediction results from a general prediction model. As a result, a deceleration prediction model which represents individual driving characteristics is required to ensure a more comfortable experience with an automatic regenerative braking control. Thus, in this paper, we proposed a deceleration prediction model based on the parametric mathematical equation and explicit model parameters. The model is designed specifically for deceleration prediction by using the parametric equation that describes deceleration characteristics. Furthermore, the explicit model parameters are updated according to individual driver characteristics using the driver’s braking data during real driving situations. The proposed algorithm was integrated and validated on a real-time embedded system, and then, it was applied to the model-based regenerative control algorithm as a case study.
机译：使用智能交通系统信息的互联动力总成控制已得到广泛研究，以提高驾驶员的便利性和能源效率。关于减速驾驶条件的车辆状态预测可以应用于电动车辆中的自动再生制动。但是，当基于来自一般预测模型的预测结果执行再生控制时，驾驶员会感到异质感。结果，需要代表各个驾驶特性的减速度预测模型，以确保通过自动再生制动控制获得更舒适的体验。因此，在本文中，我们提出了基于参数数学方程和显式模型参数的减速预测模型。该模型是通过使用描述减速特性的参数方程式专门为减速预测而设计的。此外，在实际驾驶情况下，使用驾驶员的制动数据根据各个驾驶员的特征更新显式模型参数。该算法在实时嵌入式系统上进行了集成和验证，然后以案例研究的形式应用于基于模型的再生控制算法。

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