Design and parameter identification of the wireless torque test system for hybrid vehicles
ObjectiveIn complex road conditions, the dynamic accuracy of the torque generated by the actual running of the vehicle is poor in the traditional bench test, it is difficult to simulate the real driving state, and the real car test has problems such as narrow space, large vibration and bad environme...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Transmission
2025-04-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2025.04.013 |
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| Summary: | ObjectiveIn complex road conditions, the dynamic accuracy of the torque generated by the actual running of the vehicle is poor in the traditional bench test, it is difficult to simulate the real driving state, and the real car test has problems such as narrow space, large vibration and bad environment. To solve this problem, an innovative embedded torque test system based on the wireless technology was designed.MethodsConsidering the internal spatial structure of the vehicle transmission system, a rotor-stator system was established based on the telemetry technology. A modular customized design was implemented for the test subject, resulting in a test system with strong dynamic performance and excellent anti-interference capability. Road tests were conducted to obtain the real vehicle torque and speed data. System identification method was employed for the iterative analysis of the test data, yielding a 6th-order identification value and establishing a torque identification model.ResultsThe calibration linearity of the test system reaches 0.703%, demonstrating small linear error and high accuracy. The root mean square error of the identification model is 1.678 5. Under three dynamic conditions—rapid acceleration, gradual acceleration, and emergency braking, the maximum model errors are 8.456 9 N·m, 1.78 N·m, and 5.4 N·m respectively, with average relative errors within 4%. These results validate the reliability of the test data and provide an effective data foundation for the accurate torque model prediction. |
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| ISSN: | 1004-2539 |