| 考虑电动公交在途特性的电池状态梯次划分 |
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| 引用本文： | 奇格奇,李丹,段梦媛,关伟,马继辉. 考虑电动公交在途特性的电池状态梯次划分[J]. 中国公路学报, 2022, 35(8): 44-54. DOI: 10.19721/j.cnki.1001-7372.2022.08.005 |
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| 作者姓名： | 奇格奇 李丹 段梦媛 关伟 马继辉 |
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| 作者单位： | 1. 北京交通大学 交通运输学院, 北京 100044;2. 北京交通大学 综合交通运输大数据应用技术交通运输行业重点实验室, 北京 100044;3. 北京交通大学 北京市城市交通信息智能感知与服务工程技术研究中心, 北京 100044 |
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| 基金项目： | 国家自然科学基金项目(71961137008,71621001,91746201);中央高校基本科研业务费专项资金项目(2019JBZ003) |
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| 摘 要： | 电动公交电池容量衰减造成里程焦虑增加、服务可靠性降低、电池资源浪费等问题。因此,评估和发现电动公交实际运营过程中影响电池健康状态的关键因素并划分电池状态尤为重要。基于电动公交长时间实际行驶过程中的充放电数据,结合安时积分法与最小二乘拟合建立电池容量估计模型,并据此计算各充放电片段的电池健康状态。进一步考虑电动公交在途特性,从电池组充放电属性、车辆行驶工况、公交营运状态3个角度提取可能影响电池健康状态的相关因素,并采用因子分析法将影响因素组合为12个影响因子,使用随机森林回归构建电池健康状态预测模型,从而根据预测结果的准确性反推获得各影响因子的重要度。最后考虑不同影响因素的重要度,利用加权聚类算法梯次划分电动公交电池健康状态为4个类别,下降梯度分别为-0.013 6、-0.011 9、-0.003 4、-0.002 8,并通过对比研究发现了同一条线路不同梯次的车辆电池组在放电深度、速度标准差、最大加速度和刹车次数等影响因素上的差异。研究结果表明:车辆荷载、电池电流释放情况、车辆行驶中速度的变化、电池的使用时间、线路拥挤状况以及电池充电深度大小对于电池健康状态的影响程度较大,而在公交营运状态相同条件下,驾驶人的行为对电池健康状态衰减程度有着较大影响。
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| 关 键 词： | 交通工程 电池状态梯次划分 加权聚类 电池健康状态 电动公交 在途特性 |
| 收稿时间： | 2021-07-15 |
Echelon Division of Battery Status Considering On-road Characteristics of Electric Buses |
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| QI Ge-qi,LI Dan,DUAN Meng-yuan,GUAN Wei,MA Ji-hui. Echelon Division of Battery Status Considering On-road Characteristics of Electric Buses[J]. China Journal of Highway and Transport, 2022, 35(8): 44-54. DOI: 10.19721/j.cnki.1001-7372.2022.08.005 |
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| Authors: | QI Ge-qi LI Dan DUAN Meng-yuan GUAN Wei MA Ji-hui |
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| Affiliation: | 1. School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China;2. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China;3. Beijing Research Center of Urban Traffic Information Sensing and Service Technologies, Beijing Jiaotong University, Beijing 100044, China |
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| Abstract: | The decrease in the battery capacity of electric buses has caused problems such as increased mileage anxiety,reduced service reliability,and waste of battery resources.Therefore,it is particularly important to evaluate and discover the key factors that affect the state of the battery health during the actual operation of electric buses,and subsequently dividing the battery status.Based on the charging and discharging segment data for the long-term operation of electric buses,this study combines the ampere-hour integration method and the least square fitting to establish a battery capacity estimation model,which was used to calculate the battery health state of each segment.Unlike previous studies,this study further considers the on-road characteristics of electric buses and extracts the relevant factors that may affect the battery health from three perspectives,including battery pack charging and discharging attributes,vehicle driving conditions,and bus operation status.The factor analysis was used to combine the influencing factors into 12 integrated factors.Moreover,the random forest regression was utilized to construct a battery health state prediction model,so as to inversely obtain the importance of each integrated factor based on the accuracy of the prediction results.Finally,considering the importance of different influencing factors,the weighted clustering algorithm was used to classify the echelons of the electric bus battery health status into 4 categories with descending gradients of-0.013 6,-0.011 9,-0.003 4,and-0.002 8.Meanwhile,for the vehicle battery packs with different echelons but on the same bus line,differences were discovered due to influencing factors such as discharge depth,speed standard deviation,maximum acceleration and braking times.The results show that the vehicle load,battery current release,vehicle speed change,battery service time,line congestion and depth of battery charge have great influence on the battery health.It was also found that under the same bus operation status conditions,driver behavior presents a considerable impact on the attenuation degree of the battery health state. |
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| Keywords: | traffic engineering echelon division of battery status weighted clustering battery health state electric bus on-road characteristics |
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