Research On Joint Identification Algorithm Of Vehicle Driving Resistance Parameters Based On Extended Kalman Filter

Vehicle driving resistance is one of the important inputs to the energy management strategy of a hybrid power system.Accurate acquisition of various parameters that impact vehicle driving resistance is the basis for reconstructing vehicle driving resistance.Among all the parameters that impact vehicle driving resistance,whole vehicle mass and road grade have significant impacts on the driving resistance but cannot be directly measured.Therefore,it is of great theoretical and practical value to study online identification algorithms that can estimate vehicle driving resistance parameters such as whole vehicle mass and road gradient.Around the above-mentioned objectives,this paper focuses on studying the following aspects:It develops a new-generation vehicle-mounted intelligent information terminal with an online identification and research platform for vehicle resistance parameters.And completed the software and hardware development of vehicle-mounted intelligent information terminal.Aiming at the problem of high coupling between mass and road grade during vehicle driving resistance calculation,this paper proposes a joint identification algorithm of vehicle driving resistance parameters based on Extended Kalman Filter,achieving an optimal estimation of whole vehicle mass and gradient.It also verifies the accuracy of the algorithm using actually measured data.Using the joint identification algorithm as the core and the vehicle-mounted intelligent information terminal as the target hardware platform,this paper completes the embedded implementation and integration of the algorithm.On this basis,it builds an online verification system for vehicle state parameters and designs 5 groups of verification tests.The identification accuracy results of the algorithm show that: the absolute error of vehicle mass estimation is less than 5% and the root mean square error of road grade estimation is less than 1.24°.The verification results of real-time operational performance of the algorithm show that the algorithm only increases CPU overhead by 2% and meets the real-time operational requirements dictated by the intelligent terminal platform.In summary,after building the online identification platform of vehicle state parameters,this paper develops a joint identification algorithm of vehicle driving resistance parameters based on Extended Kalman Filter.In the end,it verifies the accuracy and real-time performance of the algorithm on real vehicles.