基于驾驶员需求转矩预测的模型预测控制能量管理

doi:10.3785/j.issn.1008-973X.2020.07.010

浙江大学学报(工学版)

2020, Vol. 54

Issue (7): 1325-1334 DOI: 10.3785/j.issn.1008-973X.2020.07.010

机械与能源工程

基于驾驶员需求转矩预测的模型预测控制能量管理

江冬冬,李道飞*(

),俞小莉

浙江大学动力机械及车辆工程研究所，浙江杭州 310027

Model predictive control energy management based ondriver demand torque prediction

Dong-dong JIANG,Dao-fei LI*(

),Xiao-li YU

Institute of Power Machinery and Vehicular Engineering, Zhejiang University, Hangzhou 310027, China

全文: PDF(1924 KB) HTML

摘要：

以并联式混合动力车辆为研究对象，基于驾驶员需求转矩预测，采用线性时变模型预测控制算法对对象车辆进行能量管理控制. 根据驾驶员前一段时间内的需求转矩，可以预测下一时段内驾驶员的需求转矩. 与指数函数预测方法相比，自回归模型在预测步长200步之内的预测准确度比指数函数高. 根据纵向动力学公式，可以由预测获得的需求转矩序列计算获得预测的车速序列；采用线性化处理的方法，将具有非线性特性的车辆模型转化成线性时变模型，采用线性时变模型预测控制算法进行求解；将基于驾驶员需求转矩预测的模型预测控制算法和基于规则的控制算法、工况已知的模型预测控制算法进行对比. 对比结果表明：基于驾驶员需求转矩预测的模型预测控制算法与基于规则的控制算法相比，在NEDC、UDDS和WLTC这3个标准工况下的燃油经济性均有所提高，但是与工况已知时的计算结果相比有提升的空间.

关键词： 并联式混合动力车辆; 驾驶员需求转矩预测; 能量管理控制; 线性时变模型预测控制

Abstract:

The linear-time varying model predictive control was proposed for the vehicle energy management control strategy based on the driver demand torque prediction aiming at the parallel hybrid electric vehicle. The driver’s demand torque in the next period was predicted according to the driver's demand torque in the previous period. The prediction accuracy of the autoregressive model within the 200 prediction step length is higher compared with the exponential function prediction method. The predicted speed sequence can be calculated from the predicted demand torque sequence according to the longitudinal dynamic formula. Then the vehicle model with nonlinear characteristics was transformed into a linear time-varying mode. The linear time-varying model predictive control algorithm was used to solve the problem. The model predictive control algorithm based on driver demand torque prediction was compared with the rule-based control algorithm and the model predictive control algorithm with known operating conditions. The comparison results show that the fuel economy of the model predictive control algorithm based on driver demand torque prediction is better under the three standard operating conditions of NEDC, UDDS and WLTC compared with the rule-based control algorithm, but there is room for improvement compared with the results of known operating condition.

Key words: parallel hybrid electric vehicle demand torque prediction of driver energy management control linear time-varying model predictive control

收稿日期: 2019-07-02 出版日期: 2020-07-05

CLC:

U 469

基金资助: 中央高校基本科研业务费专项资助项目（2019QNA4018）；浙江科技厅重点研发计划资助项目（2018C01057，2018C01058）；宁波市“科技创新2025”重大专项资助项目（2018B10064，2018B10063）

通讯作者: 李道飞 E-mail: dfli@zju.edu.cn

作者简介: 江冬冬（1991—），男，博士生，从事车辆能量管理、智能驾驶的研究. orcid.org/0000-0001-9471-1775

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引用本文:

江冬冬,李道飞,俞小莉. 基于驾驶员需求转矩预测的模型预测控制能量管理[J]. 浙江大学学报(工学版), 2020, 54(7): 1325-1334.

Dong-dong JIANG,Dao-fei LI,Xiao-li YU. Model predictive control energy management based ondriver demand torque prediction. Journal of ZheJiang University (Engineering Science), 2020, 54(7): 1325-1334.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2020.07.010 或 http://www.zjujournals.com/eng/CN/Y2020/V54/I7/1325

图 1 并联式混合动力车辆结构示意图

表 1 车辆的基本参数

图 2 车辆模型示意图

图 3 发动机特性图

图 4 电机特性图

图 5 换挡逻辑图

图 6 发动机油耗多项式拟合结果

图 7 外特性转矩多项式拟合结果

图 8 电机效率多项式拟合结果

图 9 指数函数预测结果图

表 2 ARX模型预测误差

图 10 2种预测方法预测结果对比图

图 11 2种预测方法的预测误差对比图

图 12 RB和MPC控制算法的车速及SOC曲线

图 13 RB和MPC控制算法的发动机/电机转矩曲线

图 14 NEDC工况下RB和MPC算法的油耗对比

图 15 UDDS工况下RB和MPC算法的油耗对比

图 16 WLTC工况下RB和MPC算法的油耗对比

图 17 NEDC工况下基于预测值和准确值的MPC仿真结果对比图

图 18 WLTC工况下基于预测值和准确值的MPC仿真结果对比图

图 19 UDDS工况下基于预测值和准确值的MPC仿真结果对比图

1	环境保护部国家质量监督检验检疫总局. 轻型汽车污染物排放限值及测量方法(中国第六阶段): GB18352.6-2016 [S/OL]. 2016-12-23. http://www.doc88.com/p-7038457360771.html.
2	JOHNSON V H, WIPKE K B, RAUSEN D J. HEV control strategy for real-time optimization of fuel economy and emission [C]//SAE International. Arlington: SAE, 2000: 1543.
3	ANBARAN S A, IDRIS N R N, JANNATI M, et al. Rule-based supervisory control of split-parallel hybrid electric vehicle [C]//Proceedings of the 2014 IEEE Conference on Energy Conversion. Johor Bahru: IEEE, 2014: 7?12.
4	MUSARDO C, RIZZONI G, GUEZENNEC Y, et al A-ECMS: an adaptive algorithm for hybrid electric vehicle energy management[J]. European Journal of Control, 2005, 11 (4/5): 509- 524
5	SEZER V, GOKASAN M, BOGOSYAN S A novel ECMS and combined cost map approach for high-efficiency series hybrid electric vehicles[J]. IEEE Transactions on Vehicular Technology, 2011, 60 (8): 3557- 3570 doi: 10.1109/TVT.2011.2166981
6	KULIKOV I A, BAULINA E E, FILONOV A I. Optimal control of a hybrid vehicle's powertrain minimizing pollutant emissions and fuel consumption [C]//SAE 2014 Commercial Vehicle Engineering Congress. Illinois: SAE, 2014: 2372-2376.
7	KO J, KO S, SON H, et al Development of brake system and regenerative braking cooperative control algorithm for automatic-transmission-based hybrid electric vehicles[J]. IEEE Transactions on Vehicular Technology, 2015, 64 (2): 431- 440 doi: 10.1109/TVT.2014.2325056
8	KIM N, CHA S, PENG H Optimal control of hybrid electric vehicles based on pontryagin’s minimum principle[J]. IEEE Transactions on Control Systems Technology, 2011, 19 (5): 1279- 1287 doi: 10.1109/TCST.2010.2061232
9	HUANG Y, WANG H, KHAJEPOUR A, et al Model predictive control power management strategies for HEVs: a review[J]. Journal of Power Sources, 2017, 341 (15): 91- 106
10	DI C S, WEI L, KOLMANOVSKY I V, et al Power smoothing energy management and its application to a series hybrid powertrain[J]. IEEE Transactions on Control Systems Technology, 2013, 21 (6): 2091- 2103 doi: 10.1109/TCST.2012.2218656
11	张洁丽. 基于模型预测控制的插电式混合动力客车能量管理策略研究[D]. 北京: 北京理工大学, 2016. ZHANG Jie-li. Energy management strategy for a plug-in hybrid electric bus based on model predictive control [D]. Beijing: Beijing Institute of Technology, 2016.
12	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 doi: 10.1109/TCST.2011.2134852
13	SUN C, HU X, MOURA S J, et al Velocity predictors for predictive energy management in hybrid electric vehicles[J]. IEEE Transactions on Control Systems Technology, 2015, 23 (3): 1197- 1204 doi: 10.1109/TCST.2014.2359176
14	孙超. 混合动力汽车预测能量管理研究[D]. 北京: 北京理工大学, 2016. SUN Chao. Predictive energy management of hybrid electric vehicle powertrains [D]. Beijing: Beijing Institute of Technology, 2016.
15	曾祥瑞, 黄开胜, 孟凡博具有实时运算潜力的并联混合动力汽车模型预测控制[J]. 汽车安全与节能学报, 2012, 3 (2): 165- 172 ZENG Xiang-rui, HUANG Kai-sheng, MENG Fan-bo Model predictive control for parallel hybrid electric vehicles with potential real-time capability[J]. Automotive Safety and Energy, 2012, 3 (2): 165- 172 doi: 10.3969/j.issn.1676-8484.2012.02.010
16	庞中华, 崔红. 系统辨识与自适应控制MATLAB仿真[M]. 北京: 北京航空航天大学出版社, 2009: 36-46.
17	刘金琨, 沈晓蓉, 赵龙. 系统辨识理论及MATLAB仿真[M]. 北京: 电子工业出版社, 2013: 31-47.
18	IYAMA H, NAMERIKAWA T. Fuel consumption optimization for a power-split HEV via gain-scheduled model predictive control [C]//2014 53rd Annual Conference of the Society of Instrument and Control Engineers of Japan. Sapporo: IEEE, 2014.
19	XU Xiao-kang, PENG Jun, ZHANG Rui, et al Adaptive model predictive control for cruise control of high-speed trains with time-varying parameters[J]. Journal of Advanced Transportation, 2019, (5): 1- 11

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