Effect of vehicle mass changes on the accuracy of Kalman filter estimation of electric vehicle speed

The mechanical drivetrain dynamics of electric vehicles can have a detrimental effect on the performance of the vehicle speed controller. It is common for the speed measurement from the motor encoder to be used for the vehicle speed feedback, after taking into account the gear ratio, but it is not valid to assume that motor and vehicle speeds are equal during transient conditions. In this study it is shown how the vehicle driveability can be greatly improved if estimates of vehicle speed and mass are obtained. Estimates of vehicle speed and mass have been realised using a Kalman filter (KF) and a recursive least-squares estimator, and validated with experimental results. The study also shows the importance of finding the most optimal process noise matrix Q for the KF, this has been carried out using a genetic algorithm, with the estimation accuracy then compared with varying vehicle mass.

[1]  P. Chevrel,et al.  Active damping of automotive powertrain oscillations by a partial torque compensator , 2007, 2007 American Control Conference.

[2]  Nils Hoffmann,et al.  PI Control, PI-Based State Space Control, and Model-Based Predictive Control for Drive Systems With Elastically Coupled Loads—A Comparative Study , 2011, IEEE Transactions on Industrial Electronics.

[3]  Bengt Schmidtbauer,et al.  Road Slope and Vehicle Mass Estimation Using Kalman Filtering , 2002 .

[4]  Per-Olof Gutman,et al.  New models for backlash and gear play , 1997 .

[5]  Teresa Orlowska-Kowalska,et al.  Vibration Suppression in a Two-Mass Drive System Using PI Speed Controller and Additional Feedbacks—Comparative Study , 2007, IEEE Transactions on Industrial Electronics.

[6]  Dong-Seok Hyun,et al.  High-performance speed control of electric machine using low-precision shaft encoder , 1999 .

[7]  Ming Yang,et al.  Low Speed Control of PMAC Servo System Based on Reduced-order Observer , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Damian Giaouris,et al.  Accurate estimation of electric vehicle speed using Kalman filtering in the presence of parameter variations , 2012 .

[9]  Bo Egardt,et al.  Backlash Estimation With Application to Automotive Powertrains , 2007, IEEE Transactions on Control Systems Technology.

[10]  Anna G. Stefanopoulou,et al.  Recursive least squares with forgetting for online estimation of vehicle mass and road grade: theory and experiments , 2005 .

[11]  F.W. Fuchs,et al.  Speed control of torsional drive systems with backlash , 2009, 2009 13th European Conference on Power Electronics and Applications.

[12]  M. Fadel,et al.  Design of robust controllers for PMSM drive fed with PWM inverter with inertia load variation , 2006, 2006 IEEE International Symposium on Industrial Electronics.

[13]  Shady Gadoue,et al.  Artificial intelligence-based speed control of DTC induction motor drives—A comparative study , 2009 .

[14]  Xiaoming Hu,et al.  An optimization approach to adaptive Kalman filtering , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[15]  J. Bocker,et al.  Active damping of drive train oscillations for an electrically driven vehicle , 2004, IEEE/ASME Transactions on Mechatronics.

[16]  Hiroshi Ohnishi,et al.  A study on road slope estimation for automatic transmission control , 2000 .

[17]  Teresa Orlowska-Kowalska,et al.  Performance Improvement of Industrial Drives With Mechanical Elasticity Using Nonlinear Adaptive Kalman Filter , 2008, IEEE Transactions on Industrial Electronics.

[18]  Gao Lin,et al.  Sensorless Permanent Magnet Synchronous Motor drive using an optimized and normalized Extended Kalman filter , 2011, 2011 International Conference on Electrical Machines and Systems.

[19]  Dan Simon,et al.  Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches , 2006 .