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Improved Range Prediction for Electric Vehicles by a Smart Tire Pressure Monitoring System

机译:通过智能胎压监测系统改进电动汽车的里程预测

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In recent years, the topic vehicle mass estimation has become more and more popular. One of the reasons for this development is the growing spread of electric vehicles. The advantages of a precise vehicle mass estimation for owners of electric vehicles are e.g., a reliable range prediction or the selection of energy efficient routes depending on the current payload and state of charge. This paper presents a novel approach to estimate the vehicle mass by monitoring the tire pressure in combination with a two-stage step detection and a modified Kalman filter. The so-called Smart Tire Pressure Monitoring System offers many chances to enhance energy efficient driving strategies or advanced driver assistance systems. The presented paper shows the results of a large-scale test series of the Smart Tire Pressure Monitoring System. Based on these results, the second part of the paper clarifies the gained improvement of the range prediction by the detected vehicle masses. The analysis of the energy consumption of a Mitsubishi i-MiEV with four own driving cycles highlights the potential for improvement for the range prediction in detail.
机译:近年来,主题车辆质量估计变得越来越受欢迎。这种发展的原因之一是电动汽车的繁衍蔓延。电动车辆所有者的精确车辆质量估计的优点是例如可靠的范围预测或根据电流有效载荷和充电状态选择节能路线。本文介绍了一种新的方法来估计车辆质量通过与两级步长检测和改进的卡尔曼滤波器组合监测轮胎压力。所谓的智能轮胎压力监测系统提供了许多机会,可以增强节能驾驶策略或先进的驾驶员辅助系统。本文显示了智能轮胎压力监测系统的大型测试系列的结果。基于这些结果,纸张的第二部分阐明了检测到的车辆质量的范围预测的增强改善。具有四个自动驾驶循环的三菱I-MEIEIER的能量消耗的分析突出了详细的范围预测的改进潜力。

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