Recursive Least Square Method with Multiple Forgot Factor for Mass Estimation of Heavy Commercial Vehicle 2024-01-2762

Heavy commercial vehicles have large variations in load and high centroid positions, so it is particularly important to obtain timely and accurate load information during driving. If the load information can be accurately obtained and the braking force of each axle can be distributed on this basis, the braking performance and safety of the entire vehicle can be improved. Heavy commercial vehicle load information is different from passenger vehicles, so it is particularly important to study commercial vehicles engaged in freight and passenger transportation. Presently, numerous research endeavors focus on evaluating the quality of passenger vehicles. However, heavy commercial vehicles exhibit notable distinctions compared to their passenger counterparts. Due to substantial variations in vehicle mass pre and post-loading, coupled with notable suspension deformations, significant changes are observed. Hence, the task of estimating the mass of heavy commercial vehicles proves considerably more intricate than that of passenger vehicles. Nevertheless, the process of mass estimation is intricately linked to vehicular safety. Therefore, delving into the mass estimation of heavy commercial vehicles holds paramount significance in the realm of safety. The demand for precise access to commercial vehicle information is notably heightened in the context of intelligent technology. The Hill Start Assist system necessitates the real-time computation of engine torque, contingent upon the vehicle mass and road gradient, with the objective of minimizing fuel injection during hill starts. In the context of an electronic parking brake system, the determination of ground braking force entails acquiring the mass of the vehicle. The more accurate the mass, the better the braking control effect. In the electronic stability control system for vehicle bodies, the stability factor is affected by the quality of the entire vehicle, and its reliability will affect the judgment of oversteer and understeer. Vehicle quality and road slope are also key parameters for making gear decisions in gear shifting control, and accurate estimation of them can improve the quality of gear shifting control. Therefore, when conducting intelligent vehicle control, it is necessary to obtain real-time vehicle mass and road slope information during vehicle driving. In this paper, a multi forgetting factor recursive least square method is used to identify the vehicle mass and road slope for the problem of inconsistency between the vehicle mass and road slope variation frequency of heavy commercial vehicles. Firstly, a dynamic system model considering the rotational inertia of heavy commercial vehicles is established. Secondly, a multi forgetting factor recursive least square algorithm for vehicle mass and road slope identification is designed. Finally, the identification algorithm is verified at half load and full load respectively.