This paper studies the state estimation issue of full-car active suspension systems of in-wheel motors-driven electric vehicles (EVs) under cloud monitoring. We propose a distributed moving horizon estimation (DMHE) method based on the event triggering mechanism (ETM) to deal with this issue. The full-car active suspension system is split into five interacting subsystems to lessen the calculation complexity of the DMHE. To improve the accuracy of the state estimation, the moving horizon estimation principle is used to handle the state and noise of the active suspension system. Then the packet loss event in the communication network is modeled as a random process that satisfies the Bernoulli property. Moreover, the boundedness of estimation errors of the proposed estimation method is proved under some sufficient conditions. The effectiveness of the algorithm is verified through the CarSim/Simulink joint simulation experiment and the half-car experimental platform.
LiuCChauKTKirtleyJL, et al. Guest editorial emerging electric machines and drives for smart energy conversion. IEEE Trans Energy Convers2018; 33(4): 1931–1933.
2.
KumarMPandaKPMoharanaJ, et al. Hybrid energy source-based BLDC motor drive for electric vehicle application. In: 2023 2nd international conference for innovation in technology (INOCON), Bangalore, India, 3–5 March 2023, pp.1–6. New York: IEEE.
3.
GuoNZhangXZouY, et al. A computationally efficient path-following control strategy of autonomous electric vehicles with yaw motion stabilization. IEEE Trans Transp Electrif2020; 6(2): 728–739.
4.
JinXWangJHeX, et al. Improving vibration performance of electric vehicles based on in-wheel motor-active suspension system via robust finite frequency control. IEEE Trans Intell Transp Syst2023; 24(2): 1631–1643.
5.
ZhangZDongJ.A new optimization control policy for fuzzy vehicle suspension systems under membership functions online learning. IEEE Trans Syst Man Cybern Syst2023; 53(5): 3255–3266.
6.
AhmadIGeXHanQ.Communication-constrained active suspension control for networked in-wheel motor-driven electric vehicles with dynamic dampers. IEEE Trans Intell Veh2022; 7(3): 590–602.
7.
ZhengXZhangHYanH, et al. Active full-vehicle suspension control via cloud-aided adaptive backstepping approach. IEEE Trans Cybern2020; 50(7): 3113–3124.
8.
SunWLiYHuangJ, et al. Vibration effect and control of in-wheel switched reluctance motor for electric vehicle. J Sound Vib2015; 338: 105–120.
9.
SunWLiYXuG, et al. Vibration control of in-wheel SRM for electric vehicle applications. In: INTER-NOISE and NOISE-CON congress and conference proceedings, Melbourne, VIC, Australia, 16–19 November 2014, pp.796–804. USA: Institute of Noise Control Engineering.
10.
ShaoXNaghdyFDuH.Reliable fuzzy control for active suspension of in-wheel motor driven electric vehicles with dynamic damping. Mech Syst Signal Process2017; 87: 365–383.
11.
WangRJingHKarimiHR, et al. Robust fault-tolerant control of active suspension systems with finite-frequency constraint. Mech Syst Signal Process2015; 62: 341–355.
12.
GaoDLvYSunY, et al. Remote mobile monitoring and fault diagnosis system for electric vehicle circular charging device based on cloud platform. In: 2020 Chinese control and decision conference (CCDC), Hefei, China, 22–24 August 2020, pp.4146–4151. New York: IEEE.
13.
WangRWangDWangL, et al. Design of remote monitoring system for new energy vehicles from the perspective of key technologies. In: 2021 IEEE international conference on advances in electrical engineering and computer applications (AEECA), Dalian, China, 27–28 August 2021, pp.463–467. New York: IEEE.
14.
GaoTWangXWuL, et al. Vehicle real-time data acquisition and analysis through intelligent remote monitoring platform. In: 2022 international conference on data analytics, computing and artificial intelligence (ICDACAI), Zakopane, Poland, 15–16 August 2022, pp.447–452. New York: IEEE.
15.
HuangZQiuM.CAN shortwave communication technology in power vehicle communication system and condition monitoring research. In: 2023 IEEE 3rd international conference on electronic technology, communication and information (ICETCI), Changchun, China, 26–28 May 2023, pp.1490–1494. New York: IEEE.
16.
PeiQNaJHuangY, et al. Adaptive estimation and control of MR damper for semi-active suspension systems. In: 2016 35th Chinese control conference (CCC), Chengdu, China, 27–29 July 2016, pp.3111–3116. New York: IEEE.
17.
HuangXWangJ.Real-time estimation of center of gravity position for lightweight vehicles using combined AKF–EKF method. IEEE Trans Veh Technol2014; 63(9): 4221–4231.
18.
LeeJHyunDHanK, et al. Real-time longitudinal location estimation of vehicle center of gravity. Int J Automot Technol2018; 19(4): 651–658.
19.
ZhangLYinXShenJ, et al. Cloud-aided moving horizon state estimation of a full-car semi-active suspension system. In: 2016 IEEE international conference on systems, man, and cybernetics (SMC), Budapest, Hungary, 9–12 October 2016, pp.527–532. New York: IEEE.
20.
LinCGongXXiongR, et al. A novel H∞ and EKF joint estimation method for determining the center of gravity position of electric vehicles. Appl Energy2017; 194(15): 609–616.
21.
JinXYinGHanifA. Cubature kalman filter-based state estimation for distributed drive electric vehicles. In: 2016 35th Chinese control conference (CCC), Chengdu, China, 27–29 July 2016, pp.9038–9042. New York: IEEE.
22.
LeeSJeonMShinV.Distributed estimation fusion with application to a multisensory vehicle suspension system with time delays. IEEE Trans Ind Electron2012; 59(11): 4475–4482.
23.
YinXLiZKolmanovskyIV.Distributed state estimation for linear systems with application to full-car active suspension systems. IEEE Trans Ind Electron2021; 68(2): 1615–1625.
24.
SchneiderRMarquardtW.Convergence and stability of a constrained partition-based moving horizon estimator. IEEE Trans Automat Contr2016; 61(5): 1316–1321.
25.
FarinaMFerrari-TrecateGScattoliniR.Moving-horizon partition-based state estimation of large-scale systems. Automatica2010; 46(5): 910–918.
26.
AhmadIGeXHanQ.Decentralized dynamic event-triggered communication and active suspension control of in-wheel motor driven electric vehicles with dynamic damping. IEEE CAA J Autom Sin2021; 8(5): 971–986.
27.
YinXHuangB.Event-triggered distributed moving horizon state estimation of linear systems. IEEE Trans Syst Man Cybern Syst2022; 52(10): 6439–6451.
28.
ZhangRLinH. Event-triggered distributed fusion estimation for multisensor multirate systems with correlated noises and fading measurements. In: 2022 China automation congress (CAC), Xiamen, China, 25–27 November 2022, pp.2285–2290. New York: IEEE.
29.
ZhaoGHuaC.A hybrid systems approach to event-triggered consensus of multiagent systems with packet losses. IEEE Trans Syst Man Cybern Syst2023; 53(3): 1773–1787.
30.
KooshkbaghiMMarquezHJ.Event-triggered discrete-time cubature Kalman filter for nonlinear dynamical systems with packet dropout. IEEE Trans Automat Contr2020; 65(5): 2278–2285.
31.
LiuYLiYGuoJ. Binary data-based parameter estimation of FIR systems with either-or communication and packet loss. In: 2022 41st Chinese control conference (CCC), Hefei, China, 25–27 July 2022, pp.1509–1514. New York: IEEE.
32.
SunHHanHQiaoJ.Quantized sampled-data stabilization for nonlinear NCSs subject to successive packet losses and probabilistic sampling. IEEE Trans Fuzzy Syst2024; 32(3): 969–978.
33.
ZhangWTangYZhengW, et al. Stability of sampled-data systems with packet losses: a nonuniform sampling interval approach. IEEE Trans Cybern2023; 53(12): 7648–7658.
34.
LiuZSongXZhangM.Maximum correntropy Kalman filter algorithm with two-step random delay and packet loss. In: 2023 2nd conference on fully actuated system theory and applications (CFASTA), Qingdao, China, 14–16 July 2023, pp.183–187. New York: IEEE.
35.
MansouriaARouamelMBourahalaF.Stability analysis and controller design of networked active vehicle suspension system subject to delays and packet losses. Algerian J Signals Syst2022; 7(1): 31–39.
36.
MaJSunS.Optimal linear estimators for multi-sensor stochastic uncertain systems with packet losses of both sides. Digit Signal Process2015; 37: 24–34.
37.
HuangYNaJWuX, et al. Approximation-free control for vehicle active suspensions with hydraulic actuator. IEEE Trans Ind Electron2018; 65(9): 7258–7267.
38.
LiYDengHXuX, et al. Modeling and testing of in-wheel motor drive intelligent electric vehicles based on co-simulation with Carsim/Simulink. IET Intell Transp Syst2019; 13(1): 115–123.
39.
BattistelliG.Distributed moving-horizon estimation with arrival-cost consensus. IEEE Trans Automat Contr2019; 64(8): 3316–3323.
40.
LiaoYNingDLiuP, et al. A versatile semiactive electrically interconnected suspension with heave-roll vibration decoupling control. IEEE Trans Ind Electron2023; 70(6): 6159–6169.
