Slope identification algorithm based on multiinformation data fusion filtering
(1. School of Automotive Engineering, Wuhan University of Technology, Wuhan, Hubei 430070, China; 2. Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan, Hubei 430070, China; 3. Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan, Hubei 430070, China; 4. Hubei Research Center for New Energy & Intelligent Connected Vehicle, Wuhan University of Technology, Wuhan, Hubei 430070, China)
Abstract：To accurately and quickly identify the slope value of the road with steep slope and large slope change rates, a slope recognition algorithm was proposed based on the multiinformation data fusion filtering. The advantages and disadvantages of different slope recognition algorithms were analyzed. The slope recognition models based on the vehicle dynamic, the acceleration sensing information with considering slope change rate and the GPS were respectively established. The interactive multimodel Kalman filter algorithm (IMMKF) was adopted, and the three slope recognition models were jointly filtered and estimated. The participation ratio of the slope recognition model was adaptively adjusted under different operating conditions. Taking the multisensor information of inwheel motor vehicle as carrier, the dSPACE test platform covering the dynamic model of fourwheel independent drive electric vehicles was constructed, and the simulation was completed. The results show that under the conditions of constant slope change rate, continuous slope change and standing slope, the slope identification results can quickly and accurately follow the actual value after small oscillation, which indicates that the proposed algorithm can improve the accuracy and robustness of slope recognition.
