Aiming at the lateral instability of distributed drive electric vehicles (DDEV) under extreme conditions, a nonlinear adaptive lateral stability control strategy is designed based on Cubature Kalman filter (CKF) observer and adaptive fuzzy sliding mode control (FSMC) theory. To solve the problem that it is difficult to measure some parameters of road and vehicle state directly, a vehicle state observer based on a double CKF algorithm is designed. The joint sliding mode surface is designed by the deviation between the ideal and observed values of yaw rate and sideslip angle. The SMC algorithm is combined with the adaptive fuzzy algorithm to design an adaptive FSMC to accurately obtain the additional yaw moment of the vehicle. Simulation verification is carried out based on CarSim and MATLAB/Simulink simulation platforms. The results show that the proposed lateral stability adaptive method can effectively improve the robustness of the DYC system.
KoisaariTUtriainenRKariT, et al. The most difficult at-fault fatal crashes to avoid with current active safety technology. Accid Anal Prev2020; 135: 105396.
2.
RaksincharoensakPOlaverri-MonrealC. Special Issue on future active safety technology towards zero-traffic accidents (FAST-zero) [Guest editorial]. IEEE Intell Transp Syst Mag2017; 9: 4–6.
3.
HrgeticMDeurJIvanovicV, et al. Vehicle sideslip angle EKF estimator based on nonlinear vehicle dynamics model and stochastic tire forces modeling. SAE Int J Passeng Cars Mech Syst2014; 7: 86–95.
4.
ZhangHWangJ. Vehicle lateral dynamics control through AFS/DYC and robust gain-scheduling approach. IEEE Trans Vehicular Technol2016; 65: 489–494.
5.
LiYDengHXuX, et al. Review on torque distribution strategies for four in-wheel motor drive electric vehicles. IOP Conf Ser Mater Sci Eng2018; 394: 42–50.
6.
YanWGuodongY. Tire lateral forces and sideslip angle estimation for distributed drive electric vehicle using noise adaptive cubature Kalman filter. J Mech Eng2019; 55: 103–112.
7.
XinXChenJZouJ.Vehicle state estimation using cubature Kalman filter. In: 2014 IEEE 17th international conference on computational science and engineering, Sichuan, China, 19–21 December 2014, pp.44–48. New York: IEEE.
8.
ZhangZZhengLLiL. Research on intelligent vehicle target state tracking based on robust adaptive SCKF. J Mech Eng2021; 57: 181–213.
9.
De NovellisLSorniottiAGruberP, et al. Comparison of feedback control techniques for torque-vectoring control of fully electric vehicles. IEEE Trans Vehicular Technol2014; 63: 3612–3623.
10.
CanaleMFagianoLFerraraA, et al. Comparing internal model control and sliding-mode approaches for vehicle yaw control. IEEE Trans Intell Transp Syst2009; 10(1): 31–41.
11.
UtkinVIChangH-C. Sliding mode control on electro-mechanical systems. Math Probl Eng2002; 8: 451–473.
12.
ZhuopingYJunLLuX. Control strategies of handling improvement of distributed drive electric vehicle. J Tongji Univ (Nat Sci)2014; 7: 1088–1095.
13.
ZhaoSHuHJingD. Stability control of distributed drive electric vehicle based on AFS/DYC coordinated control. J Huaqiao Univ (Nat Sci)2021; 5: 571–579.
14.
ChenYWangJ. Adaptive energy-efficient control allocation for planar motion control of over-actuated electric ground vehicles. IEEE Trans Control Syst Technol2014; 22: 1362–1373.
15.
GuoJWangJ. Lateral stability control of distributed drive electric vehicle based on fuzzy sliding mode control. In: 3rd IEEE information technology and mechatronics engineering conference (ITOEC), Chongqing, China, 3–5 October 2017. New York: IEEE.
16.
YangYYanYXuX, et al. Super-twisting algorithm with fast super-twisting disturbance observer for steer-by-wire vehicles. Proc IMechE, Part D: J Automobile Engineering2021; 235: 2324–2340.
17.
GengGWeiBDuanC, et al. A strong robust observer of distributed drive electric vehicle states based on strong tracking-iterative central difference Kalman filter algorithm. Adv Mech Eng2018; 10: 770–776.
18.
BardawilCTaljR.Integrated vehicle lateral stability control with different coordination strategies between active steering and differential braking. In: 2014 IEEE 17th international conference on intelligent transportation systems, Qingdao, China, 8–11 October 2014, pp.314–319. New York: IEEE.
19.
LiBDuHLiW, et al. Side-slip angle estimation based lateral dynamics control for omni-directional vehicles with optimal steering angle and traction/brake torque distribution. Mechatronics2015; 30: 348–362.
20.
LeATChenC-K. Vehicle stability control by using an adaptive sliding-mode algorithm. Int J Veh Des2016; 72: 107–131.
21.
GengGBWJiangH, et al. A research on driving state estimation for distributed drive electric vehicle based on NA-EKF. Automot Eng2018; 40: 770–776.
22.
ZhangLLiLQiB, et al. Torque adaptive traction control for distributed drive electric vehicle. J Mech Eng2013; 49: 106–113.
23.
TerrasJNevesA. Modelling and simulation of a commercial electric vehicle. In: Annual conference on intelligent transportation systems, Madeira Island, Portugal, 19–22 September 2010, pp.1588–1593. New York: IEEE.
24.
TürOKirayB. Modeling and control of a four wheel drive parallel hybrid electric vehicle. In: International conference on control applications, Munich, Germany, 4–6 October 2006, pp.155–162. New York: IEEE.
25.
LeAT. Vehicle stability controller based on adaptive sliding mode algorithm with estimated vehicle side-slip angle. In: DuyVDaoTKimS, et al. (eds) AETA 2016: recent advances in electrical engineering and related sciences. Cham: Springer, 2017, pp.880–890.
26.
DuRMiS. Simulation on control strategy for torque distribution of compound brake system in a distributed drive electric vehicle. Automot Eng2019; 3: 327–345.
27.
WangX. Research on adaptive fuzzy sliding mode control algorithm of two-wheeled self-balancing robot. Master Thesis, Harbin University of Technology, China, 2016.
