基于6σ设计的插电混合动力客车的能量管理策略

doi:10.3969/j.issn.1674-8484.2022.02.017

汽车安全与节能学报 ›› 2022, Vol. 13 ›› Issue (2): 358-367.DOI: 10.3969/j.issn.1674-8484.2022.02.017

基于6σ设计的插电混合动力客车的能量管理策略

赖辉平¹(), 侯亮¹^,^*(), 王少杰¹, 刘强生²

1.厦门大学机电工程系,厦门 361104,中国
2.厦门金龙联合汽车工业有限公司,厦门 361023,中国

收稿日期:2021-11-11 修回日期:2021-12-13 出版日期:2022-06-30 发布日期:2022-07-01
通讯作者: 侯亮
作者简介:*侯亮,教授。E-mail： hliang@xmu.edu.cn。
赖辉平（1997—）,男（汉族）,福建,硕士研究生。E-mail： zr07191025@163.com。
基金资助:
国家自然科学基金面上项目(51975495);中央引导地方专项(2020L3002);厦门市重大科技项目(3502Z20191019)

Energy management strategy of plug-in hybrid electric bus based on 6σ Design

LAI Huiping¹(), HOU Liang¹^,^*(), WANG Shaojie¹, LIU Qiangsheng²

1. Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen, 361105, China
2. Xiamen King Long United Automobile Industry Co. Ltd., Wuhan University of Technology, Xiamen, 361023, China

Received:2021-11-11 Revised:2021-12-13 Online:2022-06-30 Published:2022-07-01
Contact: HOU Liang

摘要/Abstract

摘要：

为提高城市插电式混合动力客车(PHEB)对随机质量、不同道路坡度、不同工况的适应性,本文提出了一种基于6σ设计的PHEB能量管理策略设计方法。将随机载客量作为噪声因子;离线过程采用6σ设计和等效燃油消耗控制策略(ECMS),获得能够对抗随机载荷变化的鲁棒控制参数;在线过程采用K最邻近法(KNN)行驶工况在线识别模型,实现复杂道路下的自适应控制。结果表明：该方法与基于规则的控制策略相比,100 km油耗平均减少了2.64 L。因此,该方法提高了混合动力城市公交客车的在不同工况环境下的适应性。

关键词: 插电混动动力客车(PHEB), 载客量, 6σ设计(DFSS), 等效燃油消耗控制策略(ECMS)

Abstract:

A design method of energy management strategy for plug-in hybrid electric bus (PHEB) was proposed based on the 6σ Design (Design For Six Sigma, DFSS) method to improve urban PHEB to adapted to random mass, different road slopes or/and different driving cycles. The off-line process used the 6σ-Design and the equivalent fuel consumption control strategy (ECMS), taken the random passenger load as noise factor, to obtain robust control parameters that can resist random load changes. And adaptive control was achieved by an online identification model with k Nearest Neighbor (KNN) method for driving conditions on complex roads. The results show that the 100-km fuel consumption of the proposed method is reduced by 2.64 L on average compared with the rule-based control strategy. Therefore, this method improves the adaptability of hybrid urban buses under different driving conditions.

Key words: plug-in hybrid electric bus (PHEB), bus-load, design for six sigma (DFSS), equivalent fuel consumption control strategy (ECMS)

中图分类号:

U469.7

赖辉平, 侯亮, 王少杰, 刘强生. 基于6σ设计的插电混合动力客车的能量管理策略[J]. 汽车安全与节能学报, 2022, 13(2): 358-367.

LAI Huiping, HOU Liang, WANG Shaojie, LIU Qiangsheng. Energy management strategy of plug-in hybrid electric bus based on 6σ Design[J]. Journal of Automotive Safety and Energy, 2022, 13(2): 358-367.

图/表 14

图1 基于DFSS的PHEB能量管理策略鲁棒设计方法

图2 道路坡度获取示意图

图3 道路沿程坡度

图4 优化输入工况

图5 混合动力系统构型

图6 基于DFSS的PHEB智能控制优化模型

图7 DFSS优化原理

图8 Isight参数优化平台搭建

图9 历史工况DFSS优化迭代过程

工况序号	β_cha	β_dis
1	3.34	4.64
2	1.79	2.12
3	3.89	3.61
4	3.02	3.62
5	2.58	2.15
6	3.40	4.81

表1 历史工况DFSS鲁棒设计结果

图10 验证工况采集乘客人数数据

图11 基于DFSS的PHEB能量管理策略鲁棒设计验证过程

图12 几种验证工况仿真结果

验证工况序号	100 km油耗 / L
验证工况序号	DID	RB
1	15.27	18.13
2	15.08	17.65
3	14.87	17.54
4	15.42	17.88

表2 不同控制方法下油耗结果

参考文献 22

[1]	Wang L, Youn B D, Azarm S, et al. Customer-driven product design selection using web-ased user-generated content[C]// ASME 2011 Int’l Desi Eng Tech Conf Compu Info Engi Conf. 2011: 405-419.
[2]	王少杰, 侯亮, 方奕凯, 等. 考虑产品运行大数据的装载机变速箱优化设计[J]. 机械工程学报, 2018, 54(22): 218-232. doi: 10.3901/JME.2018.22.218
	WANG Shaojie, HOU Liang, FANG Yikai, et al. Optimization design of wheel loader gearbox considering product operational big data[J]. J Mech Engi, 2018, 54(22): 218-232. (in Chinese)
[3]	程徐. 数据驱动的风机桨叶覆冰检测研究[D]. 天津: 天津理工大学, 2021.
	CHENG Xu. Data-driven blade icing detection of wind turbines[D]. Tianjin: Tianjin University of Technology, 2021. (in Chinese)
[4]	刘文慧. 基于数据驱动的火电厂脱硫浆液循环泵运行优化方法研究[D]. 济南: 山东建筑大学, 2020.
	LIU Wenhui. New operation optimization method based on data-driven for desulfurization slurry recycle pump in thermal power plant[D]. Jinan: Shandong Jianzhu University, 2020. (in Chinese)
[5]	ZHANG Wei, WANG Shaojie, HOU Liang, et al. Operating data-driven inverse design optimization for product usage personalization with an application to wheel loaders[J]. J Indu Info Integr, 2021, 23(1): 100212-100224.
[6]	DU Yongchang, ZHAO Yue, WANG Qinpu, et al. Trip-oriented stochastic optimal energy management strategy for plug-in hybrid electric bus[J]. Energy, 2016, 115(pt.1): 1259-1271. doi: 10.1016/j.energy.2016.09.056 URL
[7]	WANG Yue, ZENG Xiaohua, SONG Dafeng, et al. Hierarchical optimal intelligent energy management strategy for a power-split hybrid electric bus based on driving information[J]. Energy, 2020, 199: 117499-117515. doi: 10.1016/j.energy.2020.117499 URL
[8]	CHEN Zheng, LI Liang, YAN Bingjie, et al. Multimode energy management for plug-in hybrid electric buses based on driving cycles prediction[J]. IEEE Trans Intell Transp Syst, 2016, 17(10): 2811-2821. doi: 10.1109/TITS.2016.2527244 URL
[9]	SHANGGUAN Jinyong, GUO Hongqiang, YUE Ming. Robust energy management of plug-in hybrid electric bus considering the uncertainties of driving cycles and vehicle mass: Science Direct[J]. Energy, 2020, 203: 117836-117849. doi: 10.1016/j.energy.2020.117836 URL
[10]	DUAN Bingmu, WANG Qiannan, WANG Jiena, et al. Calibration efficiency improvement of rule-based energy management system for a plug-in hybrid electric vehicle[J]. Int’t J Automot Technol, 2017, 18(2): 335-342.
[11]	Tribioli L, Cozzolino R, Chiappini D, et al. Energy management of a plug-in fuel cell/battery hybrid vehicle with on-board fuel processing[J]. Appl Energy, 2016, 184: 140-154. doi: 10.1016/j.apenergy.2016.10.015 URL
[12]	侯亮, 林浩菁, 王少杰, 等. 基于运行数据驱动反向设计的复杂装备个性化定制[J]. 机械工程学报, 2021, 57(8): 65-80. doi: 10.3901/JME.2021.08.065
	HOU Liang, LIN Haojing, WANG SHAOJie, et al. Mass personalization for complex equipment based on operating data-driven inverse design[J]. J Mech Engi, 2021, 57(8): 65-80. (in Chinese)
[13]	王越. 基于车联网信息的混合动力客车智能能量管理控制策略研究[D]. 吉林: 吉林大学, 2020.
	WANG Yue. Research on intelligent energy management strategy for hybrid electric bus based on vehicular networking information[D]. Jilin: Jilin University, 2020. (in Chinese)
[14]	曹骞, 李君, 曲大为. 大连市乘用车典型行驶工况的构建[J]. 上海交通大学学报, 2018, 52(11): 1537-1542.
	CAO Qian, LI Jun, QU Dawei, et al. Construction of typical driving cycle for passenger cars in the city of dalian[J]. J Shanghai Jiaotong Univ, 2018, 52(11): 1537-1542. (in Chinese)
[15]	江志农, 赵南洋, 夏敏, 等. 一种基于流形学习和KNN算法的柴油机工况识别方法[J]. 噪声与振动控制, 2019, 39(3): 1-6.
	JIANG Zhinong, ZHAO Nanyang, XIA Min, et al. Operating mode identification method for diesel engines based on manifold learning and KNN algorithm[J]. Nois Vibr Contr, 2019, 39(3): 1-6. (in Chinese)
[16]	高建平, 赵金宝, 杨松. 基于驾驶意图的插电式混合动力公交车控制策略[J]. 机械工程学报, 2016, 52(24): 107-114. doi: 10.3901/JME.2016.24.107
	GAO Jianping, ZHAO Jinbao, YANG Song, et al. Control strategy of plug-in hybrid electric bus based on driver intention[J]. J Mech Engi, 2016, 52(24): 107-114. (in Chinese)
[17]	GUO Hongqiang, HOU Daizheng, DU Shangyue, et al. A driving pattern recognition -based energy management for plug-in hybrid electric bus to counter the noise of stochastic vehicle mass[J]. Energy, 2020, 198: 117289-117304. doi: 10.1016/j.energy.2020.117289 URL
[18]	辛俊胜, 商跃进, 王红, 等. 基于最优拉丁超立方抽样的动车组轴箱弹簧稳健设计[J]. 铁道机车车辆, 2020, 40(5): 60-64.
	XIN Junsheng, SHANG Yuejin, WANG Hong, et al. Robust design of axle box spring for EMU based on optimal Latin hypercube sampling[J]. Railway Locomotive & Car, 2020, 40(5): 60-64. (in Chinese)
[19]	覃鹏飞, 谢贵山, 徐志丹, 等. 基于DFSS设计方法的车身结构开发[J]. 机械设计, 2021, 38(1): 108-114.
	QIN Pengfei, XIE Guishan, XU Zhidan, et al. Development of body structure based on the dfss design[J]. J Mach Desi, 2021, 38(1): 108-114. (in Chinese)
[20]	赵娜. 基于6 Sigma理论的稳健优化策略研究[D]. 沈阳: 东北大学, 2014.
	ZHAO Na. Study of robust optimization based on six sigma theory[D]. Shenyang: Northeastern University, 2014. (in Chinese)
[21]	ZHANG Yuanjian, CHU Liang, FU Zicheng, et al. Optimal energy management strategy for parallel plug-in hybrid electric vehicle based on driving behavior analysis and real time traffic information prediction[J]. Mechatronics, 2017, 46: 177-192. doi: 10.1016/j.mechatronics.2017.08.008 URL
[22]	GUO Hongqiang, LU Silong, HUI Hongzhang, et al. Receding horizon control-based energy management for plug-in hybrid electric buses using a predictive model of terminal soc constraint in consideration of stochastic vehicle mass[J]. Energy, 2019, 176: 292-308. doi: 10.1016/j.energy.2019.03.192

基于6σ设计的插电混合动力客车的能量管理策略

Energy management strategy of plug-in hybrid electric bus based on 6σ Design

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 14

参考文献 22

相关文章 15

编辑推荐

Metrics

本文评价

期刊信息

在线期刊

作者中心

审稿中心

联系我们

[1]	董佳硕, 丹聃, 魏名山, 赵一航, 张杨军. 基于平板热管的动力电池热管理系统[J]. 汽车安全与节能学报, 2026, 17(2): 209-217.
[2]	吴祥成, 郭俊轩, 陈华宇, 苏良彬, 卢金. 基于储能箱的电动汽车集成式热管理系统设计及性能分析[J]. 汽车安全与节能学报, 2026, 17(2): 244-252.
[3]	高锋阳, 刘嘉, 韩国鹏, 齐丰旭, 刘庆寅. 考虑耐久性的PEM燃料电池有轨电车自适应优化控制策略[J]. 汽车安全与节能学报, 2024, 15(4): 569-578.
[4]	陈绮桐, 赵东, 刘丛志, 李亮. 考虑经验驾驶行为的入弯实时类人速度规划方法[J]. 汽车安全与节能学报, 2024, 15(3): 309-320.
[5]	朱鑫宁, 王茜, 刘荣康, 朱思明, 张剑波, 周伟. 质子交换膜燃料电池随行波流场设计与传质特性[J]. 汽车安全与节能学报, 2024, 15(3): 351-359.
[6]	关权, 涂正凯. 锂电池与燃料电池混合观光车动力系统的优化设计[J]. 汽车安全与节能学报, 2024, 15(2): 199-207.
[7]	郝旭, 刘成荫, 姜禹彤, 王贺武, 邹岱江, 钟锐恒, 戴锋. 中国语境下纯电动微型车全生命周期碳排放[J]. 汽车安全与节能学报, 2024, 15(1): 47-53.
[8]	徐明诚, 徐利伟, 殷国栋, 董锋威. 燃油车和纯电车混杂智能网联队列系统的节能与稳定控制[J]. 汽车安全与节能学报, 2024, 15(1): 71-82.
[9]	许英博, 李景涛, 高飞翔, 武平乐, 李铮. 智能电动浪潮下的汽车产业格局重塑[J]. 汽车安全与节能学报, 2023, 14(6): 651-663.
[10]	张佳乐, 焦生杰, 王玥琳, 易小刚. 基于微分同胚变换的智慧交通锥反步编队控制[J]. 汽车安全与节能学报, 2023, 14(6): 723-733.
[11]	李雪, 张甫仁, 路兴隆, 黄郅凯, 赵浩东, 史亚洲. 锂离子动力电池蛇形通道液体冷却板的性能优化[J]. 汽车安全与节能学报, 2023, 14(3): 385-392.
[12]	贾凡, 王谙词, 殷翔, 曹锋, 刘和成. 不同控制策略下新能源汽车跨临界CO₂热泵最优运行特性[J]. 汽车安全与节能学报, 2022, 13(4): 770-777.
[13]	尹燕莉, 张鑫新, 潘小亮, 詹森, 黄学江, 王福振. 基于等效因子的Q学习燃料电池汽车能量管理策略[J]. 汽车安全与节能学报, 2022, 13(4): 785-795.
[14]	周泉, 张策腾飞, 李雁飞, 帅斌, 徐宏明. 基于数字孪生和PSO算法的混动车辆能量管理策略鲁棒优化[J]. 汽车安全与节能学报, 2022, 13(3): 517-525.
[15]	宋波, 孙凯, 车志钊, 陈锐, 刘怀宇, 任美林, 王天友. 能量管理策略对燃料电池客车热管理系统性能的影响[J]. 汽车安全与节能学报, 2022, 13(3): 526-534.