Extensible Optimization Control of Hybrid Transmission Switching Process for Off-road Vehicles

doi:10.12068/j.issn.1005-3026.2025.20239043

Abstract

Abstract:

Hybrid off-road vehicles include multiple operating modes， and the inappropriate control strategies will impact the smoothness and clutch life when vehicles switch operating modes. Taking the power split hybrid gearbox as the research object， the transformation relationship of contradictory features is established using the extensible control algorithm. The contradictory feature quantities are transformed by adjusting the weight matrix of model predictive control （MPC） to interfere with the extensible controller. The simulation model of the transmission system of off-road vehicles is established， and the results show that the jerk of the vehicle in the mode switching process using the extensible controller is less than 10 m/s³， which is about 20% lower compared with MPC. The friction work of clutches is about 18 kJ， and the jerk and sliding work effect are improved compared with the traditional MPC. Extensible control improves the smoothness of the mode switching process and enhances the quality of mode switching for off-road vehicles.

Key words: hybrid transmission, model predictive control, extensible control, mode switching, friction work

CLC Number:

TD 57

Xin-xin ZHAO, Bing LI, Jue YANG. Extensible Optimization Control of Hybrid Transmission Switching Process for Off-road Vehicles[J]. Journal of Northeastern University(Natural Science), 2025, 46(3): 106-114.

Figures/Tables 14

Fig.1 Structure of the hybrid transmission

Table 1 Vehicle related parameters

总质量/t	车轮动态半径/m	主减速器减速比	空气阻力系数	迎风面积/m²	滚动阻力系数	电池容量/Ah
113	0.837	22.07	0.65	7.5	0.025	158

Fig.2 Solving process of dynamic equations

Fig.3 Working mode of the clutch friction model

Table 3 States of the clutch in different modes

模式	离合器CL1	离合器CL2	制动器BR1	制动器BR2
1	0	0	1	1
2	1	0	0	1
3	0	1	1	0
4	1	1	0	0

Table 4 Model parameters for controller design

制动器BR1等效

作用半径R/m

制动器BR1

摩擦副数N

制动器BR1

面积A_c/m²

电磁阀的时间

常数 $τ_{c v}$ /s

电磁阀的增益

$K_{c v}$ /(MPa·A^-1)

制动器片的

摩擦因子 $μ$

Table 4 Model parameters for controller design

制动器BR1等效

作用半径R/m

制动器BR1

摩擦副数N

制动器BR1

面积A_c/m²

电磁阀的时间

常数 $τ_{c v}$ /s

电磁阀的增益

$K_{c v}$ /(MPa·A^-1)

制动器片的

摩擦因子 $μ$

0.098

0.39

0.04

1.0

0.13

Fig.4 Reference trajectory of clutch speed

Fig.5 Speed of clutch based on MPC

Fig.6 Simulation results of the mode switching process based on MPC

Fig.7 Overall framework of extendable adjustment in the mode switching control

Fig.8 Tracking error of MPC control in the mode switching process

Fig.9 One-dimensional extensible set

Fig.10 Comparison of clutch speed with extensible control and MPC

Fig.11 Comparison of extensible control and modelpredictive control results

References 19

1	Tang X L， Zhang D J， Liu T， et al. Research on the energy control of a dual-motor hybrid vehicle during engine start-stop process［J］．Energy， 2019， 166：1181-1193.
2	李显阳．并联混合动力汽车模式切换动态协调控制的仿真研究［D］．北京：北京交通大学，2014．
	Li Xian-yang. Simulation study on mode-shift dynamic coordinated control of parallel hybrid electric vehicle［D］.Beijing： Beijing Jiaotong University， 2014.
3	王卉．混合动力汽车模式切换动态协调控制研究［D］. 镇江：江苏大学，2020．
	Wang Hui. Research on dynamic coordinated control of mode switching for hybrid electric vehicles［D］. Zhenjiang： Jiangsu University， 2020.
4	Zeng X H， Yang N N， Wang J N， et al. Predictive-model-based dynamic coordination control strategy for power-split hybrid electric bus［J］.Mechanical Systems and Signal Processing， 2015， 60：785-798.
5	Beck R， Richert F， Bollig A， et al. Model predictive control of a parallel hybrid vehicle drivetrain［C］// Proceedings of the 44th IEEE Conference on Decision and Control. Seville， 2005： 2670-2675.
6	林歆悠，苏炼，郑清香. 应用模型预测控制的混合动力汽车模式切换动态协调控制［J］. 控制理论与应用， 2020， 37（4）： 897-906.
	Lin Xin-you， Su Lian， Zheng Qing-xiang. Dynamic coordination control of mode transition using model predictive control for hybrid electric vehicle［J］. Control Theory & Applications， 2020， 37（4）：897-906.
7	张扬. 平衡重式叉车防侧翻控制研究［D］.合肥：合肥工业大学，2020.
	Zhang Yang. Research on Anti-rollover Control of Counterbalanced Forklift［D］. Hefei： Hefei University of Technology， 2020.
8	孙友鼎. 弯道工况下自适应巡航系统多目标控制研究［D］.合肥：合肥工业大学，2021.
	Sun You-ding. Research on multi-objective control of adaptive cruise control system under curve condition［D］. Hefei： Hefei University of Technology， 2021.
9	秦顺琪. 基于可拓理论的智能车辆横纵向协调控制研究［D］.镇江：江苏大学，2021.
	Qin Shun-qi. Research on the lateral and longitudinal coordination control of intelligent vehicle based on extension theory［D］. Zhenjiang： Jiangsu University， 2021.
10	童昌圣. 面向AGV控制的故障诊断与知识可拓管理系统［D］.南京：南京航空航天大学，2020.
	Tong Chang-sheng. Fault diagnosis and knowledge extension management system for AGV control［D］. Nanjing： Nanjing University of Aeronautics and Astronaut， 2020.
11	Zhang J G， Zhao X X， Azad N L. LSTM-based adaptive energy management of connected hybrid mining trucks for improving fuel efficiency［C］//2022 IEEE 25th International Conference on Intelligent Transportation Systems （ITSC）. Macau： IEEE： 2848-2855.
12	Patinya S. Modeling and control of automatic transmission with planetary gears for shift quality［D］. Arlington： The University of Texas At Arlington， 2011.
13	Karmel A M. Dynamic modeling and analysis of the hydraulic system of automotive automatic transmissions ［C］// 1986 American Control Conference. Seattle： IEEE， 1986： 273–278.
14	Tugcu A， Hebbale K， Alexandridis A， et al. Modeling and simulation of the powertrain dynamics of vehicles equipped with automatic transmission［C］//Proceedings of Symposium on Simulation of Ground Vehicles and Transportation Systems. Los Angeles， 1986： 39-61.
15	Karnopp D. Computer simulation of stick-slip friction in mechanical dynamic systems［J］. Journal of Dynamic Systems， Measurement， and Control， 1985， 107（1）：100-103.
16	卢晓晖. 汽车传动系的滚动优化控制研究［D］.长春：吉林大学，2013.
	Lu Xiao-hui. Moving Horizon Optimal Control for Automotive Driveline Systems［D］. Changchun： Jilin University， 2013.
17	Sanada K， Kitagawa A. A study of two-degree-of-freedom control of rotating speed in an automatic transmission， considering modeling errors of a hydraulic system［J］. Control Engineering Practice， 1998， 6：1125–1132.
18	Dolcini P， Bechart H， Canudas de Wit C. Observer-based optimal control of dry clutch engagement［C］// Processdings of the 44th IEEE Conference on Decision and Control. Seville： IEEE， 2005： 440-445.
19	Hu C， Wang R R， Yan F J， et al. Output constraint control on path following of four-wheel independently actuated autonomous ground vehicles［J］. IEEE Transactions on Vehicular Technology， 2016， 65（6）： 4033-4043.

[1]	Chuan-yin TANG, Lyu PAN, Jing-hong LI, Ming-li ZHANG. Vehicle Path Tracking Control Considering Stability Boundaries and Roll Stability [J]. Journal of Northeastern University(Natural Science), 2024, 45(8): 1123-1134.
[2]	LI Shou-tao， WEI Yu-bo， LI Qiu-yuan， YU Ding-li. MPC Stability Control Method Considering the Variation of Vehicle′s Cornering Stiffness [J]. Journal of Northeastern University(Natural Science), 2023, 44(2): 162-167.
[3]	TANG Chuan-yin， ZHAO Yi-feng， ZHAO Ya-feng， ZHOU Shu-wen. Research on the Trajectory Tracking Control Method of Intelligent Vehicles [J]. Journal of Northeastern University Natural Science, 2020, 41(9): 1297-1303.
[4]	LIANG Zhong-chao， ZHANG Huan， ZHAO Jing， WANG Yong-fu. Trajectory Tracking Control of Unmanned Vehicles Based on Adaptive MPC [J]. Journal of Northeastern University Natural Science, 2020, 41(6): 835-840.