Full Text:   <648>

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CLC number: U469.72

On-line Access: 2016-11-03

Received: 2016-01-26

Revision Accepted: 2016-09-26

Crosschecked: 2016-10-10

Cited: 1

Clicked: 1674

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Wen Song

http://orcid.org/0000-0003-0714-6275

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Journal of Zhejiang University SCIENCE A 2016 Vol.17 No.11 P.903-910

http://doi.org/10.1631/jzus.A1600036


A charging management-based intelligent control strategy for extended-range electric vehicles


Author(s):  Wen Song, Xin Zhang, Yi Tian, Li-he Xi

Affiliation(s):  Beijing Key Laboratory of Powertrain for New Energy Vehicles, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China; more

Corresponding email(s):   zhangxin@bjtu.edu.cn

Key Words:  Recognition of running state, Incremental algorithm, Parallel computing, Electric vehicle


Wen Song, Xin Zhang, Yi Tian, Li-he Xi. A charging management-based intelligent control strategy for extended-range electric vehicles[J]. Journal of Zhejiang University Science A, 2016, 17(11): 903-910.

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Abstract: 
To fully take advantage of external charging conditions and reduce fuel consumption for extended-range electric vehicles, a charging management-based intelligent control strategy is proposed. The intelligent control strategy is applied to different driving patterns based on the various characteristics of urban roads. When the vehicle is driving on arterial roads, a constant power control strategy is applied. When the driver decides to go to a charging station, the extender-off time can be determined based on the current state of the vehicle and the distance to the charging station. When the vehicle is driving on an expressway, a power follower control strategy is applied. The range-extender engine is controlled to work over a wide variety of regions to obtain optimum fuel economy. The simulation results indicate that as the vehicle arrives at the charging station, the proposed charging management-based intelligent control strategy has made the state of charge reach the lowest permissible level after the driver made the decision to charge at the charging station. Therefore, the driver can charge the vehicle with as much clean electric energy as possible from the charging station.

The manuscript presents an intelligent control strategy for battery management in an electric-powered vehicle. The intelligent control method was developed based on the identification of the current status of the battery running condition and the estimation of the electricity needed to the charging station, so that determine the optimal closing time of the extender. The novelty of the paper lies on determination of the optimal closing time so that the maximal clear electrical energy can be charged in the charging station. Computer simulation has been undertaken to validate the proposed strategy. The simulation results proved the effectiveness of the proposed method.

增程式电动汽车智能充电管理控制策略研究

目的:为了充分利用外界充电条件,减少现有增程式电动汽车燃油消耗,结合当前运行工况、整车运行状态以及相距充电站距离等信息,通过合理调整整车需求功率在增程器与动力电池之间的分配,使得增程式电动汽车在到达充电站时荷电状态(SOC)降低到理想充电范围,提高了整车从充电站获取清洁电能的效率。
创新点:1. 根据车辆当前状态,建立电池需求电量估算模块和増程器输出功率计算模块;2. 在司机决定进入充电站充电后,控制电池SOC在车辆进入充电站时降到最低。
方法:1. 通过对使用环境进行分析,建立智能控制策略,在不同的运行工况下对增程器采用不同的控制方式。通过理论推导,建立动力电池需求电量估算模块(公式2);2. 通过仿真计算,将所提出的智能控制策略与初始控制策略进行对比。
结论:1. 与整车原策略相比,本文提出的智能控制策略能够根据车辆当前运行工况,控制增程器采用不同的工作方式;2. 在司机决定到充电站充电时,根据当前SOC值,确定最佳增程器关闭时刻,可使增程式电动汽车在到达充电站时,SOC降低到理想充电范围,提高动力电池从充电站获取清洁电能的能力。

关键词:运行状态识别;增量算法;并行计算;电动汽车

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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