Abstract
A torque management model (TMM) focused on the close matching of driving requirements and operating conditions for electric tractors was developed. The TMM used the accelerator pedal output signal to establish the torque demand to adapt for two conditions – field traction and road driving. Further, the TMM uses the limitations of a time-based torque ramp, maximum motor torque at the current speed, and battery state of charge. To verify the performance of the TMM, a tractor control unit (TCU) to support the TMM was designed, and a test bench comprising both driving and loading devices was built. The bench provided the TMM/TCU with the necessary operating states needed as inputs to determine the torque demand. Experiments were performed under five specific manoeuvring cases. The test results showed that the TMM was capable of accurately converting pedal manipulations into torque demands both in the field traction and road driving conditions. The torque changes during testing were properly smoothed by limiting the rising and falling time of the target torque, which was favourable for eliminating impacts due to torque fluctuations. Overloading operations that would occur in case of excessive torque request were avoided by the torque capacity limitation both in the field traction and road acceleration tests. When the bus voltage was deliberately adjusted below the alarm threshold, the TMM could scale down the torque demand in real time according to the deviation between the actual voltage and the threshold, and therefore, it always kept the voltage above the safe level.
| Original language | English |
|---|---|
| Pages (from-to) | 65-83 |
| Number of pages | 19 |
| Journal | Biosystems Engineering |
| Volume | 182 |
| DOIs | |
| Publication status | Published - Jun 1 2019 |
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Food Science
- Animal Science and Zoology
- Agronomy and Crop Science
- Soil Science
Cite this
Modelling and verification of driving torque management for electric tractor : Dual-mode driving intention interpretation with torque demand restriction. / Wu, Zhongbin; Xie, Bin; Li, Zhen; Chi, Ruijuan; Ren, Zhiyong; Du, Yuefeng; Inoue, Eiji; Muneshi, Mitsuoka; Okayasu, Takashi; Yasumaru, Hirai.
In: Biosystems Engineering, Vol. 182, 01.06.2019, p. 65-83.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Modelling and verification of driving torque management for electric tractor
T2 - Dual-mode driving intention interpretation with torque demand restriction
AU - Wu, Zhongbin
AU - Xie, Bin
AU - Li, Zhen
AU - Chi, Ruijuan
AU - Ren, Zhiyong
AU - Du, Yuefeng
AU - Inoue, Eiji
AU - Muneshi, Mitsuoka
AU - Okayasu, Takashi
AU - Yasumaru, Hirai
PY - 2019/6/1
Y1 - 2019/6/1
N2 - A torque management model (TMM) focused on the close matching of driving requirements and operating conditions for electric tractors was developed. The TMM used the accelerator pedal output signal to establish the torque demand to adapt for two conditions – field traction and road driving. Further, the TMM uses the limitations of a time-based torque ramp, maximum motor torque at the current speed, and battery state of charge. To verify the performance of the TMM, a tractor control unit (TCU) to support the TMM was designed, and a test bench comprising both driving and loading devices was built. The bench provided the TMM/TCU with the necessary operating states needed as inputs to determine the torque demand. Experiments were performed under five specific manoeuvring cases. The test results showed that the TMM was capable of accurately converting pedal manipulations into torque demands both in the field traction and road driving conditions. The torque changes during testing were properly smoothed by limiting the rising and falling time of the target torque, which was favourable for eliminating impacts due to torque fluctuations. Overloading operations that would occur in case of excessive torque request were avoided by the torque capacity limitation both in the field traction and road acceleration tests. When the bus voltage was deliberately adjusted below the alarm threshold, the TMM could scale down the torque demand in real time according to the deviation between the actual voltage and the threshold, and therefore, it always kept the voltage above the safe level.
AB - A torque management model (TMM) focused on the close matching of driving requirements and operating conditions for electric tractors was developed. The TMM used the accelerator pedal output signal to establish the torque demand to adapt for two conditions – field traction and road driving. Further, the TMM uses the limitations of a time-based torque ramp, maximum motor torque at the current speed, and battery state of charge. To verify the performance of the TMM, a tractor control unit (TCU) to support the TMM was designed, and a test bench comprising both driving and loading devices was built. The bench provided the TMM/TCU with the necessary operating states needed as inputs to determine the torque demand. Experiments were performed under five specific manoeuvring cases. The test results showed that the TMM was capable of accurately converting pedal manipulations into torque demands both in the field traction and road driving conditions. The torque changes during testing were properly smoothed by limiting the rising and falling time of the target torque, which was favourable for eliminating impacts due to torque fluctuations. Overloading operations that would occur in case of excessive torque request were avoided by the torque capacity limitation both in the field traction and road acceleration tests. When the bus voltage was deliberately adjusted below the alarm threshold, the TMM could scale down the torque demand in real time according to the deviation between the actual voltage and the threshold, and therefore, it always kept the voltage above the safe level.
UR - http://www.scopus.com/inward/record.url?scp=85064313687&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064313687&partnerID=8YFLogxK
U2 - 10.1016/j.biosystemseng.2019.04.002
DO - 10.1016/j.biosystemseng.2019.04.002
M3 - Article
VL - 182
SP - 65
EP - 83
JO - Biosystems Engineering
JF - Biosystems Engineering
SN - 1537-5110
ER -