In order to realize the integration of handling, lateral stability, rollover prevention, and ride comfort of distributed in-wheel motor drive electric vehicles (EVs), this study presents an integrated control strategy for distributed EVs based on states estimation through particle filter (PF) and nonlinear model predictive control (NMPC). To estimate the vehicle states including longitudinal velocity, lateral velocity, and sideslip angle, the random walk model is established to avoid complex tire forces calculation. Then PF completes estimation by measuring longitudinal and lateral acceleration and yaw rate. Considering handling, lateral stability, anti-rollover performance, and ride comfort comprehensively, proposed controller takes full advantages of distributed electric vehicle platform with four-wheel steering (4WS), four-wheel-drive (4WD), and active suspension technology to improve the overall performance in the NMPC structure. State/actuator constraints are also taken into account in the scheme. Finally, the co-simulation through commercial software Carsim and Simulink verifies the effectiveness of the vehicle states estimator and designed control algorithm. Results show that estimation error is controlled within 1% and overall performance of distributed EV is improved.
TaverniniDMetzlerMGruberP, et al. Explicit nonlinear model predictive control for electric vehicle traction control. IEEE Trans Control Syst Technol2019; 27(4): 1438–1451.
2.
ZhouHJiaFJingH, et al. Coordinated longitudinal and lateral motion control for four wheel independent motor-drive electric vehicle. IEEE Trans Veh Technol2018; 67(5): 3782–3790.
3.
JiaFLiuZZhouH, et al. A novel design of traction control based on a piecewise-linear parameter-varying technique for electric vehicles with in-wheel motors. IEEE Trans Veh Technol2018; 67(10): 9324–9336.
4.
JingHJiaFLiuZ.Multi-objective optimal control allocation for an over-actuated electric vehicle. IEEE Access2017; 6: 4824–4833.
5.
WuDLiYZhangJ, et al. Torque distribution of a four in-wheel motors electric vehicle based on a PMSM system model. Proc IMechE, Part D: J Automobile Engineering2018; 232(13): 1828–1845.
6.
YuanLZhaoHChenH, et al. Nonlinear MPC-based slip control for electric vehicles with vehicle safety constraints. Mechatronics2016; 38: 1–15.
7.
IvanovVSavitskiDShyrokauB.A survey of traction control and antilock braking systems of full electric vehicles with individually controlled electric motors. IEEE Trans Veh Technol2015; 64(9): 3878–3896.
8.
HuJSWangYFujimotoH, et al. Robust yaw stability control for in-wheel motor electric vehicles. IEEE ASME Trans Mechatron2017; 22(3): 1360–1370.
9.
Velazquez AlcantarJAssadianF. Vehicle dynamics control of an electric-all-wheel-drive hybrid electric vehicle using tyre force optimisation and allocation. Veh Syst Dyn2019; 57(12): 1897–1923.
10.
ImineHDjemaiM.Switched control for reducing impact of vertical forces on road and heavy-vehicle rollover avoidance. IEEE Trans Veh Technol2016; 65(6): 4044–4052.
11.
ZhangZMaRWangL, et al. Novel PMSM control for anti-lock braking considering transmission properties of the electric vehicle. IEEE Trans Veh Technol2018; 67(11): 10378–10386.
12.
JinXYuZYinG, et al. Improving vehicle handling stability based on combined AFS and DYC system via robust takagi-sugeno fuzzy control. IEEE Trans Intell Transp Syst2018; 19(8): 2696–2707.
13.
AligiaDAMagallanGADe AngeloCH.EV traction control based on nonlinear observers considering longitudinal and lateral tire forces. IEEE Trans Intell Transp Syst2018; 19(8): 2558–2571.
14.
HanKChoiMLeeB, et al. Development of a traction control system using a special type of sliding mode controller for hybrid 4WD vehicles. IEEE Trans Veh Technol2018; 67(1): 264–274.
15.
WangQZhaoYXuH, et al. Adaptive backstepping control with grey signal predictor for nonlinear active suspension system matching mechanical elastic wheel. Mech Syst Signal Process2019; 131: 97–111.
16.
TahamiFFarhangiSKazemiR.A fuzzy logic direct yaw-moment control system for all-wheel-drive electric vehicles. Veh Syst Dyn2004; 41(3): 203–221.
17.
ChenSYinGJinX. Study on the active steering system with direct yaw-moment control for electric vehicle with electric motored wheels. In: 2016 35th Chinese control conference (CCC), Chengdu, China, 27–29 July 2016, pp.8945–8950. New York, NY: IEEE.
18.
ZhaoJWongPKMaX, et al. Chassis integrated control for active suspension, active front steering and direct yaw moment systems using hierarchical strategy. Veh Syst Dyn2017; 55(1): 72–103.
19.
WangQZhaoYDengY, et al. Optimal coordinated control of ARS and DYC for four-wheel steer and in-wheel motor drive electric vehicle with unknown tire model. IEEE Trans Veh Technol2020; 69(10): 10809–10819.
20.
MousavinejadEHanQLYangF, et al. Integrated control of ground vehicles dynamics via advanced terminal sliding mode control. Veh Syst Dyn2017; 55(2): 268–294.
21.
ZhangHWangJ.Vehicle lateral dynamics control through AFS/DYC and robust gain-scheduling approach. IEEE Trans Veh Technol2016; 65(1): 489–494.
22.
ChenMZQHuYLiC, et al. Performance benefits of using inerter in semiactive suspensions. IEEE Trans Control Syst Technol2015; 23(4): 1571–1577.
23.
ZhangJSunWJingH.Nonlinear robust control of antilock braking systems assisted by active suspensions for automobile. IEEE Trans Control Syst Technol2019; 27(3): 1352–1359.
24.
ZhaoYXuHDengY, et al. Multi-objective optimization for ride comfort of hydro-pneumatic suspension vehicles with mechanical elastic wheel. Proc IMechE, Part D: J Automobile Engineering2019; 233(11): 2714–2728.
25.
SiampisEVelenisELongoS.Rear wheel torque vectoring model predictive control with velocity regulation for electric vehicles. Veh Syst Dyn2015; 53(11): 1555–1579.
26.
AtaeiMKhajepourAJeonS.Model predictive control for integrated lateral stability, traction/braking control, and rollover prevention of electric vehicles. Veh Syst Dyn2020; 58(1): 49–73.
27.
LiLLuYWangR, et al. A three-dimensional dynamics control framework of vehicle lateral stability and rollover prevention via active braking with MPC. IEEE Trans Ind Electron2017; 64(4): 3389–3401.
28.
GuoHWuZXinB, et al. Vehicle velocities estimation based on mixed EKF. In: Proceedings of 2011 Chinese control and decision conference (CCDC), Mianyang, China, 23–25 May 2011, pp.2030–2035. New York, NY: IEEE.
29.
HashemiEKhosravaniSKhajepourA, et al. Longitudinal vehicle state estimation using nonlinear and parameter-varying observers. Mechatronics2017; 43: 28–39.
30.
JalaliMHashemiEKhajepourA, et al. Integrated model predictive control and velocity estimation of electric vehicles. Mechatronics2017; 46: 84–100.
31.
SunFHuangXRudolphJ, et al. Vehicle state estimation for anti-lock control with nonlinear observer. Control Eng Pract2015; 43: 69–84.
32.
RezaeianAKhajepourAMelekW, et al. Simultaneous vehicle real-time longitudinal and lateral velocity estimation. IEEE Trans Veh Technol2017; 66(3): 1950–1962.
33.
ZhaoLHLiuZYChenH.Design of a nonlinear observer for vehicle velocity estimation and experiments. IEEE Trans Control Syst Technol2011; 19(3): 664–672.
34.
ZhangZHuangCDingD, et al. Hummingbirds optimization algorithm-based particle filter for maneuvering target tracking. Nonlinear Dyn2019; 97(2): 1227–1243.
35.
BiswasSKDempsterAG.Approximating sample state vectors using the ESPT for computationally efficient particle filtering. IEEE Trans Signal Process2019; 67(7): 1918–1928.
36.
NiJHuJXiangC.Envelope control for four-wheel independently actuated autonomous ground vehicle through AFS/DYC integrated control. IEEE Trans Veh Technol2017; 66(11): 9712–9726.
37.
YuHCheliFCastelli-DezzaF.Optimal design and control of 4-IWD electric vehicles based on a 14-DOF vehicle model. IEEE Trans Veh Technol2018; 67(11): 10457–10469.
38.
ChenWTanDZhaoL.Vehicle sideslip angle and road friction estimation using online gradient descent algorithm. IEEE Trans Veh Technol2018; 67(12): 11475–11485.
39.
BiJZhangTYuH, et al. State-of-health estimation of lithium-ion battery packs in electric vehicles based on genetic resampling particle filter. Appl Energy2016; 182: 558–568.
40.
GustafssonFGunnarssonFBergmanN, et al. Particle filters for positioning, navigation, and tracking. IEEE Trans Signal Process2002; 50(2): 425–437.
41.
CuiYKavasseriR.A particle filter for dynamic state estimation in multi-machine systems with detailed models. IEEE Trans Power Syst2015; 30(6): 3377–3385.
