For in-wheel driving vehicle electric vehicles (EVs), mechanical electromagnetic coupling effect caused by the air gap deformation in permanent magnet synchronous hub motor and intensified by the road excitation deteriorates the EVs performance. In this paper, after studying the numerical method for multi-field coupling problems of hub-driving vehicle under the coupled action of electromagnetic field and mechanical field. The experimental validation is investigated. The results indicate that the multi-field coupling effect in hub-driving motor worsens the dynamics performance of the vehicle. To enhance the vehicle performance, suppress mechanical electromagnetic coupling effect and, at same time, reduce the influence of controllable suspension time delay, a delay-dependent H∞ controller is designed based on Lyapunov theory. By applying the particle swarm optimization (PSO) algorithm and the linear matrix inequality theory, the desired output controller gain is derived. Numerical simulations reflect that the active suspension controller considering control time delay not only achieves the favorable riding comfort performance and restrains the coupling effect in hub driving motor but also ensures the suspension deflection and the safety performance requirement. Moreover, it maintains the closed-loop asymptotically stability regardless of t the variation on the sprung mass and control time delay.
LongGDingFZhangN, et al. Regenerative active suspension system with residual energy for in-wheel motor driven electric vehicle. Appl Energy2020; 260: 114180.
2.
HeLYeWHeZ, et al. A combining sliding mode control approach for electric motor anti-lock braking system of battery electric vehicle. Control Eng Pract2020; 102: 104520.
3.
ZhangYChuLFuZ, et al. Energy management strategy for plug-in hybrid electric vehicle integrated with vehicle-environment cooperation control. Energy2020; 197: 117192.
4.
QinYTangXJiaT, et al. Noise and vibration suppression in hybrid electric vehicles: state of the art and challenges. Renew Sustain Energy Rev2020; 124: 109782.
5.
WangZZhangTZhangZ, et al. A high-efficiency regenerative shock absorber considering twin ball screws transmissions for application in range-extended electric vehicles. Energy Built Environ2020; 1(1): 36–49.
6.
LiBDuHLiW.Fault-tolerant control of electric vehicles with in-wheel motors using actuator-grouping sliding mode controllers. Mech Syst Signal Process2016; 72–73: 462–485.
7.
HeHWangCJiaH, et al. An intelligent braking system composed single-pedal and multi-objective optimization neural network braking control strategies for electric vehicle. Appl Energy2020; 259: 114172.
8.
SongCXXiaoFSongSX, et al. Stability control of 4WD electric vehicle with in-wheel motors based on integrated control of electro-mechanical braking system. Appl Mech Mater2015; 740: 206–210.
9.
MengQQianCShuY.Active steering wheel shimmy control for electric vehicle by sampled-data output feedback. ISA Trans2019; 84: 262–270.
10.
SunWLiYHuangJ, et al. Vibration effect and control of in-wheel switched reluctance motor for electric vehicle. J Sound Vibr2015; 338: 105–120.
11.
QinYHeCShaoX, et al. Vibration mitigation for in-wheel switched reluctance motor driven electric vehicle with dynamic vibration absorbing structures. J Sound Vib2018; 419: 249–267.
12.
WuHZhengLLiY.Coupling effects in hub motor and optimization for active suspension system to improve the vehicle and the motor performance. J Sound Vib2020; 482: 115426.
13.
TanDWangHWangQ.Study on the rollover characteristic of in-wheel-motor-driven electric vehicles considering road and electromagnetic excitation. Shock Vib2016; 10: 1–13.
14.
LuoYTanD.Study on the dynamics of the in-wheel motor system. IEEE Trans Veh Technol2012; 61: 3510–3518.
15.
WangRJingHYanF, et al. Optimization and finite-frequency H∞ control of active suspensions in in-wheel motor driven electric ground vehicles. J Franklin Inst2015; 352(2): 468–484.
16.
ShaoXNaghdyFDuH.Reliable fuzzy H∞ control for active suspension of in-wheel motor driven electric vehicles with dynamic damping. Mech Syst Signal Process2017; 87: 365–383.
17.
ShaoXNaghdyFDuH, et al. Output feedback H∞ control for active suspension of in-wheel motor driven electric vehicle with control faults and input delay. ISA Trans2019; 92: 94–108.
18.
LiuMGuFHuangJ, et al. Integration design and optimization control of a dynamic vibration absorber for electric wheels with in-wheel motor. Energies2017; 10(12): 2069.
19.
ZhaoWWangYWangC.Multidisciplinary optimization of electric-wheel vehicle integrated chassis system based on steady endurance performance. J Clean Prod2018; 186: 640–651.
20.
JingHWangRLiC, et al. Fault-tolerant control of active suspensions in in-wheel motor driven electric vehicles. Int J Veh Des2015; 68: 22–36.
21.
YueWWangLLiuZ, et al. Sampled-data internet of connected vehicles control with channel fading and time-varying delay. Mech Syst Signal Process2020; 135: 106430.
22.
ChoGRLiJ-HParkD, et al. Robust trajectory tracking of autonomous underwater vehicles using back-stepping control and time delay estimation. Ocean Eng2020; 201: 107131.
23.
YanGFangMXuJ.Analysis and experiment of time-delayed optimal control for vehicle suspension system. J Sound Vib2019; 446: 144–158.
24.
PhuDXMienV.Robust control for vibration control systems with dead-zone band and time delay under severe disturbance using adaptive fuzzy neural network. J Franklin Inst2020; 357(17): 12281–12307.
25.
WangWSongY.Analytical computation method for steady-state stochastic response of a time-delay nonlinear automotive suspension system. Mech Syst Signal Process2019; 131: 434–445.
26.
SunXXuJFuJ.The effect and design of time delay in feedback control for a nonlinear isolation system. Mech Syst Signal Process2017; 87: 206–217.
27.
LiHJingXKarimiHR.Output-feedback-based H∞ control for vehicle suspension systems with control delay. IEEE Trans Ind Electron2014; 61(1): 436–446.
28.
ChoiHDAhnCKLimMT, et al. Dynamic output-feedback H∞ control for active half-vehicle suspension systems with time-varying input delay. Int J Control Autom Syst2016; 14: 59–68.
29.
WangGChadliMChenH, et al. Event-triggered control for active vehicle suspension systems with network-induced delays. J Franklin Inst2019; 356(1): 147–172.
30.
FangFDingHLiuY, et al. Fault tolerant sampled-data H∞ control for networked control systems with probabilistic time-varying delay. Inf Sci2021; 544: 395–414.
31.
ZapateiroMLuoNKarimiHR, et al. Vibration control of a class of semiactive suspension system using neural network and backstepping techniques. Mech Syst Signal Process2009; 23(6): 1946–1953.
32.
PangHZhangXChenJ, et al. Design of a coordinated adaptive backstepping tracking control for nonlinear uncertain active suspension system. Appl Math Model2019; 76: 479–494.
33.
MinXLiYTongS.Adaptive fuzzy output feedback inverse optimal control for vehicle active suspension systems. Neurocomputing2020; 403: 257–267.
34.
HongCKong-HuiG.Constrained H∞ control of active suspensions: an LMI approach. IEEE Trans Control Syst Technol2005; 13: 412–421.
35.
WangGChenCYuS.Robust non-fragile finite-frequency H∞ static output-feedback control for active suspension systems. Mech Syst Signal Process2017; 91: 41–56.
36.
WangHPMustafaGIYTianY.Model-free fractional-order sliding mode control for an active vehicle suspension system. Adv Eng Softw2018; 115: 452–461.
37.
RathJJKarimiHRDefoortM, et al. Output feedback active suspension control with higher order terminal sliding mode. IEEE Trans Ind Electron2017; 64(2): 1392–1402.
38.
DuHZhangN.H∞ control of active vehicle suspensions with actuator time delay. J Sound Vib2007; 301(1–2): 236–252.
39.
GaoHZhaoYSunW.Input-delayed control of uncertain seat suspension systems with human-body model. IEEE Trans Control Syst Technol2010; 18(3): 591–601.
40.
LiZZhengLRenY, et al. Multi-objective optimization of active suspension system in electric vehicle with in-wheel-motor against the negative electromechanical coupling effects. Mech Syst Signal Process2019; 116: 545–565.
41.
SunWPanHZhangY, et al. Multi-objective control for uncertain nonlinear active suspension systems. Mechatronics2014; 24(4): 318–327.
42.
UngerASchimmackFLohmannB, et al. Application of LQ-based semi-active suspension control in a vehicle. Control Eng Pract2013; 21(12): 1841–1850.
43.
LuoJZhaoJ.Robust H∞ control for networked switched fuzzy systems with network-induced delays and packet dropout. Circuits Syst Signal Process2015; 34(2): 663–679.
44.
WangRJingHYanF, et al. Robust fault-tolerant H∞ control of active suspension systems with finite-frequency constraint. Mech Syst Signal Process2015; 62: 341–355.
45.
QaziAJFarooquiUAKhanA, et al. Optimization of semi-active suspension system using particle swarm optimization algorithm. AASRI Procedia2013; 4: 160–166.
46.
GuoLXZhangLP. Robust H∞ control of active vehicle suspension under non-stationary running. J Sound Vib2012; 331(26): 5824–5837.
