科创中国

Vehicle mass is an important parameter in vehicle dynamics control systems.Although many algorithms have been developed for the estimation of mass,none of them have yet taken into account the different types of resistance that occur under different conditions.This paper proposes a vehicle mass estimator.The estimator incorporates road gradient information in the longitudinal accelerometer signal,and it removes the road grade from the longitudinal dynamics of the vehicle.Then,two different recursive least square method(RLSM)schemes are proposed to estimate the driving resistance and the mass independently based on the acceleration partition under different conditions.A 6 DOF dynamic model of four In-wheel Motor Vehicle is built to assist in the design of the algorithm and in the setting of the parameters.The acceleration limits are determined to not only reduce the estimated error but also ensure enough data for the resistance estimation and mass estimation in some critical situations.The modification of the algorithm is also discussed to improve the result of the mass estimation.Experiment data on a sphalt road,plastic runway,and gravel road and on sloping roads are used to validate the estimation algorithm.The adaptability of the algorithm is improved by using data collected under several critical operating conditions.The experimental results show the error of the estimation process to be within 2.6%,which indicates that the algorithm can estimate mass with great accuracy regardless of the road surface and gradient changes and that it may be valuable in engineering applications.This paper proposes a recursive least square vehicle mass estimation method based on acceleration partition.

The existing research of the acceleration control mainly focuses on an optimization of the velocity trajectory with respect to a criterion formulation that weights acceleration time and fuel consumption. The minimum-fuel acceleration problem in conventional vehicle has been solved by Pontryagin's maximum principle and dynamic programming algorithm, respectively. The acceleration control with minimum energy consumption for battery electric vehicle(EV) has not been reported. In this paper, the permanent magnet synchronous motor(PMSM) is controlled by the field oriented control(FOC) method and the electric drive system for the EV(including the PMSM, the inverter and the battery) is modeled to favor over a detailed consumption map. The analytical algorithm is proposed to analyze the optimal acceleration control and the optimal torque versus speed curve in the acceleration process is obtained. Considering the acceleration time, a penalty function is introduced to realize a fast vehicle speed tracking. The optimal acceleration control is also addressed with dynamic programming(DP). This method can solve the optimal acceleration problem with precise time constraint, but it consumes a large amount of computation time. The EV used in simulation and experiment is a four-wheel hub motor drive electric vehicle. The simulation and experimental results show that the required battery energy has little difference between the acceleration control solved by analytical algorithm and that solved by DP, and is greatly reduced comparing with the constant pedal opening acceleration. The proposed analytical and DP algorithms can minimize the energy consumption in EV's acceleration process and the analytical algorithm is easy to be implemented in real-time control.