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State of Charge Estimation Based on Microscopic Driving Parameters for Electric Vehicle's Battery

机译:基于微观驱动参数的电动汽车电池充电状态估计

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摘要

Recently, battery-powered electric vehicle (EV) has received wide attention due to less pollution during use, low noise, and high energy efficiency and is highly expected to improve urban air quality and then mitigate energy and environmental pressure. However, the widespread use of EV is still hindered by limited battery capacity and relatively short cruising range. This paper aims to propose a state of charge (SOC) estimation method for EV's battery necessary for route planning and dynamic route guidance, which can help EV drivers to search for the optimal energy-efficient routes and to reduce the risk of running out of electricity before arriving at the destination or charging station. Firstly, by analyzing the variation characteristics of power consumption rate with initial SOC and microscopic driving parameters (instantaneous speed and acceleration), a set of energy consumption rate models are established according to different operation modes. Then, the SOC estimation model is proposed based on the presented EV power consumption model. Finally, by comparing the estimated SOC with the measured SOC, the proposed SOC estimation method is proved to be highly accurate and effective, which can be well used in EV route planning and navigation systems.
机译:近年来,由于使用过程中的污染少,噪音低,能效高,电池驱动的电动汽车(EV)受到了广泛的关注,人们高度期望它能改善城市空气质量,从而减轻能源和环境压力。然而,电动汽车的广泛使用仍然受到电池容量有限和巡航距离相对较短的阻碍。本文旨在提出一种用于电动汽车电池的充电状态(SOC)估算方法,这是进行路线规划和动态路线引导所必需的,它可以帮助电动汽车驾驶员寻找最佳的节能途径,并减少用尽电力的风险在到达目的地或充电站之前。首先,通过分析功耗率随初始SOC和微观驱动参数(瞬时速度和加速度)的变化特性,根据不同的运行模式建立了一组能耗率模型。然后,基于提出的电动汽车功耗模型,提出了SOC估计模型。最后,通过将SOC估计值与实测SOC值进行比较,证明所提出的SOC估计方法是准确,有效的,可以很好地应用于EV路径规划和导航系统中。

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  • 来源
    《Mathematical Problems in Engineering 》 |2013年第16期| 946747.1-946747.6| 共6页
  • 作者单位

    Beijing Jiaotong Univ, Sch Traff & Transportat, Beijing 100044, Peoples R China.;

    Beijing Jiaotong Univ, Sch Traff & Transportat, Beijing 100044, Peoples R China.;

    Beijing Jiaotong Univ, Sch Traff & Transportat, Beijing 100044, Peoples R China.;

    Beijing Jiaotong Univ, Sch Traff & Transportat, Beijing 100044, Peoples R China.;

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