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A model-based continuous differentiable current charging approach for electric vehicles in direct current microgrids

机译：一种基于模型的直流微电网电动车辆的连续可微分电流充电方法

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摘要

Microgrid-based electric vehicle (EV) fast charging station is believed to be a promising solution to lessen the enormous charging burden of large-scale EVs on the main grid, while the optimal charging protocol in microgrids is still missing. Thus, this paper presents a model-based continuous differentiable charging (CDC) approach for EV fasting charging in microgrids. The priority of the proposed method on bus voltage regulation over the conventional multi-stage constant current (MCC) strategy is first validated. The parameters of the CDC profile are obtained through the model-based particle swarm optimization framework. An electrothermal-coupled equivalent circuit model is adopted as the performance model while a physical-based semi-empirical battery degradation model is built to measure the capacity loss and lithium plating rate. The objective function of the optimization is to reduce the charging time, capacity fading, and bus voltage disturbance, with the constraints of preventing lithium deposition and limiting the maximum voltage and temperature. The optimization results of the CDC and MCC methods confirm that CDC protocol can reduce the charging time by about 33.5% without scarifying battery health. The sensitivity analyses of initial state of charge, ambient temperature, and heat dissipation coefficient further suggest the universality of the proposed CDC strategy.
机译：基于微电网的电动车辆（EV）快速充电站被认为是一个有希望的解决方案，以减轻主电网上大型EV的巨大充电负担，而Microgrids中的最佳充电协议仍然缺失。因此，本文介绍了一种基于模型的连续可微分充电（CDC）方法，用于微电网中的EV禁食充电。首先验证了在传统的多级恒流（MCC）策略上的总线电压调节方法的提出方法的优先级。通过基于模型的粒子群优化框架获得CDC分布的参数。采用电热耦合的等效电路模型作为性能模型，而建立了基于物理的半经验电池劣化模型以测量容量损耗和锂电镀速率。优化的目标函数是降低充电时间，容量衰落和总线电压干扰，防止锂沉积并限制最大电压和温度的约束。 CDC和MCC方法的优化结果证实，CDC协议可以将充电时间降低约33.5％而不会造成电池健康。初始充电状态，环境温度和散热系数的敏感性分析进一步提出了拟议的CDC策略的普遍性。

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  • 来源
    《Journal of power sources》 |2021年第15期|229019.1-229019.13|共13页
  • 作者

    Wang Shuoqi; Kuang Ke; Han Xuebing; Chu Zhengyu; Lu Languang; Ouyang Minggao;

  • 作者单位

    Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China;

    Univ Shanghai Sci & Technol Coll Mech Engn Shanghai 200093 Peoples R China;

    Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China;

    Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China;

    Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China;

    Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Electric vehicle; Fast charging; DC microgrid; Battery model; Single particle model; Particle swarm optimization;

    机译：电动车;快速充电;直流微电网;电池型号;单粒子模型;粒子群优化;

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