Supplementary data for “Optimization-Based Tuning of a Hybrid UKF State Estimator with Tire Model Adaption for an All Wheel Drive Electric Vehicle”
| dc.contributor.author | Heidfeld, Hannes | |
| dc.contributor.author | Schünemann, Martin | |
| dc.date.accessioned | 2021-03-05T14:54:45Z | |
| dc.date.available | 2021-03-05T14:54:45Z | |
| dc.date.issued | 2021 | |
| dc.description | Training and test data for vehicle state estimator optimization and validation are included as MATLAB-files. The data was captured with the all-wheel drive electric vehicle “BugEE” of the Otto-von-Guericke-University Magdeburg and IKAM GmbH during different severe driving maneuvers on wet and dry road surfaces. Training data contains: Acceleration on wet road surface, slalom on wet road surface, steady state circular driving on dry road surface. Test data contains: Acceleration and emergency evasion on wet road surface, steady-state circular driving and slalom on dry road surface. Furthermore, results shown in “Optimization-Based Tuning of a Hybrid UKF State Estimator with Tire Model Adaption for an All Wheel Drive Electric Vehicle” are included. | de_DE |
| dc.description.abstract | Novel drivetrain concepts such as electric direct drives can improve vehicle dynamic control due to faster, more accurate, and more flexible generation of wheel individual propulsion and braking torques. Exact and robust estimation of vehicle state of motion in the presence of unknown disturbances, such as changes in road conditions, is crucial for realization of such control systems. This article shows the design, tuning, implementation, and test of a state estimator with individual tire model adaption for direct drive electric vehicles. The vehicle dynamics are modeled using a double-track model with an adaptive tire model. State-of-the-art sensors, an inertial measurement unit, steering angle, wheel speed, and motor current sensors are used as measurements. Due to the nonlinearity of the vehicle model, an Unscented Kalman Filter (UKF) is used for simultaneous state and parameter estimation. To simplify the difficult task of UKF tuning, an optimization-based method using real-vehicle data is utilized. The UKF is implemented on an electronic control unit and tested with real-vehicle data in a hardware-in-the-loop simulation. High precision even in severe driving maneuvers under various road conditions is achieved. Nonlinear state and parameter estimation for all wheel drive electric vehicles using UKF and optimization-based tuning is shown to provide high precision with minimal manual tuning effort. | de_DE |
| dc.identifier.uri | http://open-science.ub.ovgu.de/xmlui/handle/684882692/86 | |
| dc.identifier.uri | https://doi.org/10.24352/ub.ovgu-2021-044 | |
| dc.language.iso | en | de_DE |
| dc.publisher | Otto-von-Guericke Universität Magdeburg | |
| dc.relation.ispartof | https://doi.org/10.3390/en14051396 | |
| dc.relation.ispartof | https://www.mdpi.com/1996-1073/14/5/1396 | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-sa/4.0/ | |
| dc.subject | nonlinear state and parameter estimation | de_DE |
| dc.subject | electric vehicle | de_DE |
| dc.subject | vehicle dynamics | de_DE |
| dc.title | Supplementary data for “Optimization-Based Tuning of a Hybrid UKF State Estimator with Tire Model Adaption for an All Wheel Drive Electric Vehicle” | de_DE |
| dc.type | Dataset | de_DE |