Real-Time Estimation of Vehicle Mass and Road Grade Based on Multi-Sensor Data Fusion

Vehicle mass and road grade are two key parameters for New Energy Vehicles. It plays an important role in the power distribution of multi-energy power systems and braking energy recovery. Using a 4-wheel drive (4WD) electric mini-car as an experimental platform, a road grade and vehicle mass estimation algorithm based on multi-data fusion technology is studied. Firstly, a Simulink model for GPS (Global Positioning System)/INS (Inertial Navigation System)/wheel-speed data fusion is established, taking advantage of the characteristics of a 4WD electric vehicle. An off-line simulation is conducted with data from a real vehicle test to verify the model. Then the verified algorithm is downloaded and successfully implemented in the Vehicle Control Unit based on MPC561 digital core by Simulink Automatic Code Generation technology. Finally, a hardware-inloop simulation based on CANoe and CANalyzer is conducted for the testing and evaluation of the VCU. The result shows that the real-time multi-data fusion algorithm produces a good estimation of the road grade and vehicle mass with an error of 5%, and the convergence and steady-state error meet the need of real vehicle applications.