In the intelligent speed planning system, real-time estimation of road slope is the key to calculate slope resistance and realize the vehicles’ active safety control. However, if the road slope is measured by the sensor while the commercial vehicle is driving, the vibration of the vehicle body will affect its measurement accuracy. Therefore, the relevant algorithm is used to estimate the real-time slope of the road when the commercial vehicle is driving. At present, many domestic and foreign scholars have analyzed and tested the estimation of road slope by the least square method or Kalman filter algorithm. Although the two methods both can achieve the estimation, the real-time performance and accuracy still need to be improved.
In this paper, for traditional fuel commercial vehicle, the Kalman filter algorithm based on the kinematics and the extended Kalman filter algorithm based on the longitudinal dynamics are respectively used to estimate the road slope. In the process of estimation based on kinematics, considering the influence of road slope rate to estimate, the recursive least squares method with forgetting factor is used to estimate the road slope rate.Finally, the estimation results obtained by kinematics and dynamics are combined. It is expected that the error based on the algorithm-estimated slope value and the true slope value will be within 6% after the commercial vehicle is driving. The proposed algorithm has high accuracy, good real-time performance and strong stability.
Using the commercial vehicle as a motion node and estimating the slope of a certain road, the intelligent planning of other vehicles’ speed in that region can be realized through the cloud platform. Then the fuel economy of the commercial vehicle can be improved.