基于智能胎压监测系统的电动汽车里程预测改进

H. Fechtner, B. Schmuelling
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引用次数: 0

摘要

近年来,车辆质量估计这一话题越来越受到人们的关注。这种发展的原因之一是电动汽车的日益普及。对于电动汽车车主来说,精确的车辆质量估算的优势在于,例如,可靠的里程预测或根据当前有效载荷和充电状态选择节能路线。本文提出了一种结合两级步进检测和改进卡尔曼滤波的通过监测轮胎压力来估计车辆质量的新方法。所谓的智能胎压监测系统为提高节能驾驶策略或先进的驾驶辅助系统提供了许多机会。本文介绍了智能胎压监测系统的大型系列试验结果。在此基础上,本文第二部分阐述了利用检测到的车辆质量对距离预测的改进。通过对三菱i-MiEV的四个独立行驶循环的能耗分析,详细说明了里程预测的改进潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved Range Prediction for Electric Vehicles by a Smart Tire Pressure Monitoring System
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.
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