增程式城市客车能量的分段跟踪优化方法

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清华大学辅仁网/2017-07-07

增程式城市客车能量的分段跟踪优化方法

谢海明, 林成涛, 刘涛, 田光宇, 黄勇

清华大学汽车安全与节能国家重点实验室, 北京 100084

Piecewise tracking energy optimization approach for an extended-range electric city bus

XIE Haiming, LIN Chengtao, LIU Tao, TIAN Guangyu, HUANG Yong

State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China

摘要:

输出: BibTeX | EndNote (RIS)

摘要为在保证动力性的条件下提高增程式城市客车的燃油经济性，提出了一种基于电池荷电状态（SOC）消耗管理和功率分配的能量分段跟踪优化方法。该方法通过在每个控制周期内构建一个短期的需求功率预测序列，并设计参考曲线以实现SOC消耗管理的方式，建立了以费用最小为目标的动力系统功率分配的阶段性优化模型。引入模型预测控制方法，滚动优化并调整功率分配策略。基于该方法，一辆12 m增程式城市客车在中国城市公交工况下的100 km油耗为21.8 L，电耗为25.4 kWh，优于CDCS策略的结果（100 km油耗24.1 L，电耗25.4 kWh）。该方法能通过防止SOC在行程中被过早耗尽并使其在行程结束时降到最低，保证增程式城市客车的动力性并提高燃油经济性。

关键词 ：能量优化,电量消耗管理,跟踪优化,模型预测控制,增程式电动汽车

Abstract：A piecewise tracking energy optimization approach was developed to manage the battery state of charge (SOC) consumption and the splitting power to improve the fuel economy of extended-range electric city buses while ensuring their performance. The approach established a stage power splitting optimization model for each control period by constructing a power demand prediction sequence and designing a reference curve to manage the SOC consumption. Model predictive control was introduced for rolling optimization and strategy adjustment. For the Chinese city bus driving cycle, this approach enables a 12 meters extended-range electric city bus to use only 21.8 L fuel and 25.4 kWh electricity per 100 km, which are better than CDCS strategy based results (24.1 L fuel and 25.4 kWh electricity per 100 km). The results show that by preventing the SOC from running out during the route but only reaching its minimum, this approach ensures the dynamic performance and improves the fuel economy.

Key words：energy optimization state of charge (SOC) consumption management tracking optimization model predictive control extended-range electric vehicle

收稿日期: 2016-09-30 出版日期: 2017-05-20

ZTFLH:

U469.72

通讯作者:黄勇,高工,E-mail:huangyev@tsinghua.edu.cnE-mail: huangyev@tsinghua.edu.cn

引用本文:

谢海明, 林成涛, 刘涛, 田光宇, 黄勇. 增程式城市客车能量的分段跟踪优化方法[J]. 清华大学学报（自然科学版）, 2017, 57(5): 476-482.
XIE Haiming, LIN Chengtao, LIU Tao, TIAN Guangyu, HUANG Yong. Piecewise tracking energy optimization approach for an extended-range electric city bus. Journal of Tsinghua University(Science and Technology), 2017, 57(5): 476-482.

链接本文:

http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2017.22.024或 http://jst.tsinghuajournals.com/CN/Y2017/V57/I5/476

图表:

图1 增程式电动汽车动力系统结构与能量流动

图2 基于发动机效率图的APU 工作点设计

表1 APU 各工作点对应的输出功率和燃油消耗率

图3 中国城市公交工况下的车速

图4 中国城市公交工况下的需求功率

图5 需求功率序列的状态转移概率

图6 SOC消耗参考曲线设计原理示例

图7 SOC消耗参考曲线

图8 能量分段跟踪优化方法的APU 输出功率

图9 基于能量分段跟踪优化方法的SOC演变曲线

参考文献:

[1]	LI Yanhe, Kar N C. Advanced design approach of power split device of plug-in hybrid electric vehicles using dynamic programming [C]//2011 IEEE Vehicle Power and Propulsion Conference. Piscataway, NJ: IEEE Press, 2011: 1-6.
[2]	Moura S J, Fathy H K, Callaway D S, et al. A stochastic optimal control approach for power management in plug-in hybrid electric vehicles [J]. IEEE Transactions on Control Systems Technology, 2011, 19(3): 545-555.
[3]	ZHANG Bingzhan, Mi C C, ZHANG Mengyang. Charge-depleting control strategies and fuel optimization of blended-mode plug-in hybrid electric vehicles [J]. IEEE Transactions on Vehicular Technology, 2011, 60(4): 1516-1525.
[4]	Wirasingha S G, Emadi A. Classification and review of control strategies for plug-in hybrid electric vehicles [J]. IEEE Transactions on Vehicular Technology, 2011, 60(1): 111-122.
[5]	Salmasi F R. Control strategies for hybrid electric vehicles: Evolution, classification, comparison, and future trends [J]. IEEE Transactions on Vehicular Technology, 2007, 56(5): 2393-2404.
[6]	LIN Chanchiao, PENG Huei, 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.
[7]	Brahma A, Guezennec Y, Rizzoni G. Optimal energy management in series hybrid electric vehicles [C]//Proc of the American Control Conference. Piscataway, NJ: IEEE Press, 2000: 60-64.
[8]	Brahma A, Guezennec Y, Rizzoni G. Dynamic optimization of mechanical electrical power flow in parallel hybrid electric vehicles [C]//Proc of the 5th International Symposium Advanced Vehicle Control. Ann Arbor, MI, 2000.
[9]	Tribioli L, Onori S. Analysis of energy management strategies in plug-in hybrid electric vehicles: Application to the GM Chevrolet Volt [C]//Proc of the American Control Conference. Piscataway, NJ: IEEE Press, 2013: 5966-5971.
[10]	XIE Haiming, TIAN Guangyu, HUANG Yong, et al. A four stage energy control strategy and fuel economy simulation for extended-range electric city bus [C]//2013 World Electric Vehicle Symposium and Exhibition. Piscataway, NJ: IEEE Press, 2014.
[11]	Lee H D, Sul S K. Fuzzy-logic-based torque control strategy for parallel-type hybrid electric vehicle [J]. IEEE Transactions on Industrial Electronics, 1998, 45(4): 625-632.
[12]	Denis N, Dubois M R, Desrochers A. Fuzzy-based blended control for the energy management of a parallel plug-in hybrid electric vehicle [J]. Intelligent Transport Systems IET, 2015, 9(1): 30-37.
[13]	LIN Chanchiao, PENG Huei, Grizzle J W. A stochastic control strategy for hybrid electric vehicles [C]//Proc of the American Control Conference. Piscataway, NJ: IEEE Press, 2004: 4710-4715.
[14]	WANG Yang, Boyd S. Fast model predictive control using online optimization [J]. IEEE Transactions on Control Systems Technology, 2010, 18(2): 267-278.
[15]	Ripaccioli G, Bemporad A, Assadian F, et al. Hybrid modeling, identification, and predictive control: An application to hybrid electric vehicle energy management [M]//Majumdar R, Tabuada P. Hybrid Systems: Computation and Control. Berlin: Springer, 2009: 321-335.
[16]	Borhan H A, Vahidi A, Phillips A M, et al. Predictive energy management of a power-split hybrid electric vehicle [C]//Proc of the American Control Conference. Piscataway, NJ: IEEE Press, 2009: 3970-3976.
[17]	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.
[18]	Borhan H A, Vahidi A. Model predictive control of a power-split hybrid electric vehicle with combined battery and ultracapacitor energy storage [C]//Proc of the 2010 American Control Conference. Piscataway, NJ: IEEE Press, 2010: 5031-5036.
[19]	Uthaichana K, Bengea S, DeCarlo R, et al. Hybrid model predictive control tracking of a sawtooth driving profile for an HEV [C]//Proc of the American Control Conference. Piscataway, NJ: IEEE Press, 2008: 967-974.
[20]	Bernardini D, Bemporad A. Scenario-based model predictive control of stochastic constrained linear systems [C]//Proc of the IEEE Conference on Decision and Control. Piscataway, NJ: IEEE Press, 2009: 6333-6338.
[21]	Ripaccioli G, Bernardini D, Cairano S D, et al. A stochastic model predictive control approach for series hybrid electric vehicle power management [C]//Proc of the 2010 American Control Conference, ACC 2010. Piscataway, NJ: IEEE Press, 2010: 5844-5849.
[22]	Johnson V H. Battery performance models in ADVISOR [J]. Journal of Power Sources, 2002, 110(2): 321-329.
[23]	XIE Haiming, CHEN Hongxu, TIAN Guangyu, et al. A stochastic model predictive control strategy for energy management of series PHEV [C]//28th International Electric Vehicle Symposium and Exhibition 2015. Goyang, Korea, 2015.

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