基于扩展卡尔曼滤波的电船剩余行程估计

Adrian Martin Z. De Vera, F. Cruz
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引用次数: 0

摘要

随着电动交通需求的不断增长,海洋运输方式开始实施电力推进。里程焦虑是消费者在交通运输中采用电力推进时更常见的担忧之一。剩余里程估计功能可以减少里程焦虑,并为车辆乘客提供对车辆的深入了解。鉴于水基运输与陆地运输的性质不同,搁浅在海上的挑战与搁浅在陆地上的挑战不同。随着电动船只越来越受欢迎,监测此类船只使用的电池剩余能量容量和所述船只剩余可行驶范围的系统变得越来越重要。本研究利用扩展卡尔曼滤波(EKF)来监测电池充电状态(SOC),同时结合基于行驶距离的模型来估计给定充电状态值下的剩余里程。研究结果表明,EKF能够在运行过程中准确地确定SOC。距离估计模型也可以预测出行距离,与实际总出行距离的误差值为3.29%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimation of Remaining Travel Range of Electric Boat using Extended Kalman Filter
With the increasing demand for electric mobility, marine transportation modes are beginning to implement electric propulsion. Range anxiety is one of the more common consumer concerns regarding the adoption of electric propulsion in transportation. Remaining range estimation capabilities can reduce range anxiety and provide vehicle passengers with insights into their vehicles. Given the nature of water-based transportation being different from that of land transportation, being stranded at sea poses different challenges than being stranded on land. As electrically propelled boats grow in popularity, systems that monitor the remaining energy capacity of the batteries used in such boats and the remaining capable travel range of the said vessels become increasingly important. This study utilizes an extended Kalman filter (EKF) to monitor the battery state of charge (SOC) while incorporating travel distance-based models to estimate the remaining range at given values of state of charge. The results from the study show that the EKF is capable of accurately determining SOC during operation. The range estimation model is also shown to predict the travel distance, with the error value from the total actual distance traveled being 3.29%.
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