吉林大学学报(工学版) ›› 2024, Vol. 54 ›› Issue (3): 600-609.doi: 10.13229/j.cnki.jdxbgxb.20221098

• 车辆工程·机械工程 • 上一篇    

压缩天然气发动机增程式电动汽车能量管理优化

赵靖华1,2,3(),张雨彤1,曹派1(),王忠恕2,李小平2,孙亚南3,解方喜2,3   

  1. 1.吉林师范大学 计算机学院,吉林 四平 136000
    2.吉林大学 汽车仿真与控制国家重点实验室,长春 130022
    3.江苏汉旭和电源科技有限公司,江苏 扬州 225000
  • 收稿日期:2022-08-26 出版日期:2024-03-01 发布日期:2024-04-18
  • 通讯作者: 曹派 E-mail:zhaojh08@mails.jlu.edu.cn;rajacakkavattin@foxmail.com
  • 作者简介:赵靖华(1980-),男,副教授,博士.研究方向:先进控制理论应用.E-mail:zhaojh08@mails.jlu.edu.cn
  • 基金资助:
    吉林省科技厅项目(20200301021RQ);吉林大学汽车仿真与控制国家重点实验室开放课题项目(20191201)

Optimal energy management on extended⁃range electric vehicle equipped with compressed natural gas engine

Jing-hua ZHAO1,2,3(),Yu-tong ZHANG1,Pai CAO1(),Zhong-shu WANG2,Xiao-ping LI2,Ya-nan SUN3,Fang-xi XIE2,3   

  1. 1.College of Computer,Jilin Normal University,Siping 136000,China
    2.State Key Laboratory of Automotive Simulation and Control,Jilin University,Changchun 130022,China
    3.Jiangsu Hanxuhe Power Technology Co. ,Ltd. ,Yangzhou 225000,China
  • Received:2022-08-26 Online:2024-03-01 Published:2024-04-18
  • Contact: Pai CAO E-mail:zhaojh08@mails.jlu.edu.cn;rajacakkavattin@foxmail.com

摘要:

根据一台压缩天然气(CNG)发动机试验数据,建立了CNG发动机增程式电动汽车模型。基于该模型提出了一种基于规则的能量管理策略,能够保证CNG发动机工作在高效区,但是存在电流波动幅度较大有损电池寿命的问题。为了抑制电流激变同时进一步降低增程式电动汽车(E-REV)的燃油消耗,基于模型预测控制(MPC)技术,提出了一种协同燃油消耗与电池保护的能量管理优化策略。世界轻型汽车测试循环(WLTC)工况下的仿真分析表明:相比于基于规则的能量分配控制器,本文提出的MPC控制器能够进一步降低3.7%的油耗,同时抑制了电流激变,有效保护了电池。

关键词: 自动控制技术, 模型预测控制, 能量管理, 增程式混合动力车辆, 压缩天然气发动机

Abstract:

An extended-range electric vehicle model with a compressed natural gas (CNG) engine was established referring to the experimental test data of the CNG engine. A rule‐based method for power split was proposed to ensure that the CNG engine works in the high-efficiency area. However, its current fluctuates greatly, which will result in a damaged battery. In order to suppress the current surge and further improve fuel consumption, a model predictive control (MPC) method for power split coordinating fuel consumption and battery protection was proposed. Simulation results show that the proposed MPC method can further reduce fuel consumption by 3.7% and suppress the current surge for effectively protecting the battery, as compared to the rule‐based method under a world light test cycle (WLTC) driving cycle.

Key words: automatic control technology, model predictive control, energy management, extended-range electric vehicle, compressed natural gas engine

中图分类号: 

  • TP273

图1

配有CNG发动机的电动汽车模型"

表1

车辆模型参数"

变量数值变量数值
车辆质量M/kg1254主减速比i04.05
空气密度ρ/(kg·m-31.2一档速比i13.608
车辆迎风面积Af/m22.52二档速比i22.05
空气阻力系数Cd0.3三档速比i31.237
滚动阻力系数β0.015四挡速比i40.91
轮胎半径r/m0.287五挡速比i50.747
传动效率ηt0.96

图2

某CNG发动机外特性曲线"

图3

某CNG发动机模型稳态工况验证结果"

图4

驱动电机的功率等势线(实线为基于多项式拟合后的功率)"

图5

Rint与SoC之间的拟合关系"

图6

基于规则的能量分配方法控制下的E-REV工作过程"

图7

CNG发动机工作过程"

图8

基于规则的控制器和基于MPC方法的能量分配控制结果对比"

1 Zhao J H, Hu Y F, Gao B Z. Sequential optimization of eco-driving taking into account fuel economy and emissions[J]. IEEE Access, 2019, 7: 130841-130853.
2 Vojtisek-Lom M, Fenkl M, Dufek M, et al. Off-cycle, real-world emissions of modern light duty diesel vehicles[C]∥SAE Paper, 2009-24-0148.
3 Reitz R D, Ogawa H, Payri R, et al. IJER editorial: the future of the internal combustion engine[J]. International Journal of Engine Research, 2020, 21(1): 3-10.
4 冯坚, 韩志玉, 高晓杰, 等. 基于动态规划算法和路况的增程式电动车能耗分析[J]. 同济大学学报: 自然科学版, 2019, 47(): 115-119.
Feng Jian, Han Zhi-yu, Gao Xiao-jie, et al. Energy consumption analysis of a range-extender electric vehicle based on a dynamic programming algorithm and road conditions[J]. Journal of Tongji University (Natural Science), 2019, 47(Sup.1): 115-119.
5 申永鹏. 增程式电动汽车能量管理与运行优化方法研究[D]. 长沙: 湖南大学电气与信息工程学院, 2015.
Shen Yong-peng. Study on the energy management and operating optimization for range extended electric vehicle[D]. Changsha: College of Electrical and Information Engineering, Hunan University, 2015.
6 Schouten N J, Salman M A, Kheir N A. Fuzzy logic control for parallel hybrid vehicles[J]. IEEE Transactions on Control Systems Technology, 2002, 10(3): 460-468.
7 Prokhorov D V. Toyota prius HEV neuro control and diagnostics[J]. Neural Networks, 2008, 21(2/3): 458-465.
8 王笑乐, 干频, 陈凌珊, 等. 增程式电动汽车发动机多工作点控制策略[J]. 江南大学学报: 自然科学版, 2015, 14(1): 56-63.
Wang Xiao-le, Gan Pin, Chen Ling-shan, et al. Study on extended-range electric vehicle engine's multiple operation points control strategy[J]. Journal of Jiangnan University (Natural Science Edition), 2015, 14(1): 56-63.
9 徐磊, 陈周欢, 白琴, 等. 一种应用于增程式电动汽车发动机控制策略试验研究[C]∥2019中国汽车工程学会年会论文集, 中国, 上海, 2019: 354-358.
Xu Lie, Chen Zhou-huan, Bai Qin, et al. A control strategy on engine for range extended electric vehicle (REEV)[C]∥Proceedings of the 2019 Annual Conference of the Chinese Society of Automotive Engineers, China, Shanghai, 2019: 354-358.
10 骆光炬, 邓晓亭, 鲁植雄, 等. 发动机管理策略对增程式电动车燃油经济性的影响研究[J]. 内燃机工程, 2020, 41(4): 79-86.
Luo Guang-ju, Deng Xiao-ting, Lu Zhi-xiong, et al. Research on influence of engine management strategy on fuel economy of extended range electric vehicle[J]. Journal of Chinese Internal Combustion Engine Engineering, 2020, 41(4): 79-86.
11 Lin C C, Peng H, Grizzle J W, et al. Power management strategy for a parallel hybrid electric truck[J]. IEEE Transactions on Control Systems Technology, 2003, 11(6): 839-849.
12 Paganelli G, Ercole G, Brahma A, et al. General supervisory control policy for the energy optimization of charge-sustaining hybrid electric vehicles[J]. JSAE Review, 2001, 22(4): 511-518.
13 Paganelli G, Tateno M, Brahma A, et al. Control development for a hybrid-electric sport-utility vehicle: strategy, implementation and field test results[J]. Proceedings of the 2001 American Control Conference, Arlington, VA, USA, 2001: 5064-5069.
14 孙耀, 胡云峰, 周杰敏, 等. 基于分层控制器的SCR系统滚动时域优化控制方法[J]. 吉林大学学报: 工学版, 2023, 53(1): 61-71.
Sun Yao, Hu Yun-feng, Zhou Jie-min, et al. Moving horizon optimization control of SCR system based on hierarchical controller[J]. Journal of Jilin University (Engineering and Technology Edition), 2023, 53(1): 61-71.
15 Borhan H, Vahidi A, Phillips A M, et al. MPC-based energy management of a power-split hybrid electric vehicle[J]. IEEE Transactions on Control Systems Technology, 2012, 20(3): 593-603.
16 Gill P E, Murray W, Saunders M A. SNOPT: an SQP algorithm for large-scale constrained optimization[J]. SIAM Review, 2005, 47(1): 99-131.
17 Sciarretta A, De Nunzio G, Ojeda L L. Optimal ecodriving control: energy-efficient driving of road vehicles as an optimal control problem[J]. IEEE Control Systems Magazine, 2015, 35(5): 71-90.
18 Gong X, Kolmanovsky I, Garone E, et al. Constrained control of free piston engine generator based on implicit reference governor[J]. Science China Information Sciences, 2018, 61(7): 1-17.
19 van Keulen T, Gillot J, de Jager B, et al. Solution for state constrained optimal control problems applied to power split control for hybrid vehicles[J]. Automatica, 2014, 50(1): 187-192.
20 Eriksson L, Lindell T, Leufven O, et al. Scalable component-based modeling for optimizing engines with supercharging, E-boost and turbo compound concepts[J]. SAE International Journal of Engines, 2012, 5(2): 579-595.
21 Eriksson L, Thomasson A, Ekberg K, et al. Look-ahead controls of heavy duty trucks on open roads-six benchmark solutions[J]. Control Engineering Practice, 2019, 83: 45-66.
22 赵靖华, 胡云峰, 高炳钊, 等. 基于尿素选择催化还原系统的氨覆盖率非线性降维观测器设计[J]. 吉林大学学报: 工学版, 2017, 49(2): 583-590.
Zhao Jing-hua, Hu Yun-feng, Gao Bing-zhao, et al. Design of nonlinear reduced-order observer for ammonia coverage based on urea-SCR systems[J]. Journal of Jilin University (Engineering and Technology Edition), 2017, 49(2): 583-590.
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