Online Estimation of Three-Directional Tire Forces Based on a Self-Organizing Neural Network
The road friction coefficient and the forces between the tire and the road have a significant impact on the stability and precise control of the vehicle. For four-wheel independent drive electric vehicles, an adaptive tire force calculation method based on the improved Levenberg–Marquarelt multi-mod...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/11/3/344 |
| _version_ | 1797610577753800704 |
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| author | Guiyang Wang Shaohua Li Guizhen Feng |
| author_facet | Guiyang Wang Shaohua Li Guizhen Feng |
| author_sort | Guiyang Wang |
| collection | DOAJ |
| description | The road friction coefficient and the forces between the tire and the road have a significant impact on the stability and precise control of the vehicle. For four-wheel independent drive electric vehicles, an adaptive tire force calculation method based on the improved Levenberg–Marquarelt multi-module and self-organizing feedforward neural networks (LM-MMSOFNN) was proposed to estimate the three-directional tire forces of four wheels. The input data was provided by common sensors amounted on the autonomous vehicle, including the inertial measurement unit (IMU) and the wheel speed/rotation angle sensors (WSS, WAS). The road type was recognized through the road friction coefficient based on the vehicle dynamics model and Dugoff tire model, and then the tire force was calculated by the neural network. The computational complexity and storage space of the system were also reduced by the improved LM learning algorithm and self-organizing neurons. The estimation accuracy was further improved by using the Extended Kalman Filter (EKF) and Moving Average (MA). The performance of the proposed LM-MMSOFNN was verified through simulations and experiments. The results confirmed that the proposed method was capable of extracting important information from the sensors to estimate three-directional tire forces and accurately adapt to different road surfaces. |
| first_indexed | 2024-03-11T06:16:10Z |
| format | Article |
| id | doaj.art-535dfad5a5354a3fbcc0a23bb154b7a2 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-11T06:16:10Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-535dfad5a5354a3fbcc0a23bb154b7a22023-11-17T12:15:12ZengMDPI AGMachines2075-17022023-03-0111334410.3390/machines11030344Online Estimation of Three-Directional Tire Forces Based on a Self-Organizing Neural NetworkGuiyang Wang0Shaohua Li1Guizhen Feng2State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang 050043, ChinaState Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang 050043, ChinaSchool of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, ChinaThe road friction coefficient and the forces between the tire and the road have a significant impact on the stability and precise control of the vehicle. For four-wheel independent drive electric vehicles, an adaptive tire force calculation method based on the improved Levenberg–Marquarelt multi-module and self-organizing feedforward neural networks (LM-MMSOFNN) was proposed to estimate the three-directional tire forces of four wheels. The input data was provided by common sensors amounted on the autonomous vehicle, including the inertial measurement unit (IMU) and the wheel speed/rotation angle sensors (WSS, WAS). The road type was recognized through the road friction coefficient based on the vehicle dynamics model and Dugoff tire model, and then the tire force was calculated by the neural network. The computational complexity and storage space of the system were also reduced by the improved LM learning algorithm and self-organizing neurons. The estimation accuracy was further improved by using the Extended Kalman Filter (EKF) and Moving Average (MA). The performance of the proposed LM-MMSOFNN was verified through simulations and experiments. The results confirmed that the proposed method was capable of extracting important information from the sensors to estimate three-directional tire forces and accurately adapt to different road surfaces.https://www.mdpi.com/2075-1702/11/3/344electric vehiclefour-wheel independent driveroad adhesion coefficientlongitudinal-lateral-vertical tire forceself-organizing neural network |
| spellingShingle | Guiyang Wang Shaohua Li Guizhen Feng Online Estimation of Three-Directional Tire Forces Based on a Self-Organizing Neural Network Machines electric vehicle four-wheel independent drive road adhesion coefficient longitudinal-lateral-vertical tire force self-organizing neural network |
| title | Online Estimation of Three-Directional Tire Forces Based on a Self-Organizing Neural Network |
| title_full | Online Estimation of Three-Directional Tire Forces Based on a Self-Organizing Neural Network |
| title_fullStr | Online Estimation of Three-Directional Tire Forces Based on a Self-Organizing Neural Network |
| title_full_unstemmed | Online Estimation of Three-Directional Tire Forces Based on a Self-Organizing Neural Network |
| title_short | Online Estimation of Three-Directional Tire Forces Based on a Self-Organizing Neural Network |
| title_sort | online estimation of three directional tire forces based on a self organizing neural network |
| topic | electric vehicle four-wheel independent drive road adhesion coefficient longitudinal-lateral-vertical tire force self-organizing neural network |
| url | https://www.mdpi.com/2075-1702/11/3/344 |
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