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Multi-Parameter Model Prediction of Braking Energy Tracking Strategy for Electric Vehicles
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Wei Zhang, Yan Gao, Hongjuan Zhang
Automobile Technology | 2025, (1) : 20 - 25
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Automobile Technology | 2025, (1): 20-25
Special Topic on Braking Energy Recovery Strategies for New Energy Vehicles
Multi-Parameter Model Prediction of Braking Energy Tracking Strategy for Electric Vehicles
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Wei Zhang, Yan Gao, Hongjuan Zhang
Affiliations
  • Taiyuan University of Technology, Taiyuan 030024
Published: 2025-01-24 doi: 10.19620/j.cnki.1000-3703.20231214
Outline
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To address the issues of a low recovery rate of motor braking energy and significant fluctuations in the DC bus voltage, a braking energy tracking strategy is put forward. This strategy can adjust the reference value of the supercapacitor current in real time by considering multiple parameters, including motor power, the efficiency of the bidirectional DC/DC converter, and the supercapacitor voltage. The discrete state space model of the energy storage unit is used to predict the current and voltage of the supercapacitor, and the current reference value of the supercapacitor is calculated by combining the other parameters in the system. The current loop based on Proportional-Integral (PI) is used to control the current of the supercapacitor and track the reference value of the current in real time. Simulation and experiments compared the tracking of regenerative energy under two different control strategies. The results indicate that the proposed strategy enhances the efficiency of regenerative energy recovery from 55.93% to 86.76%, and it restricts the voltage fluctuation of the DC bus within 0.9%.

State space model  /  Bidirectional DC/DC converter  /  Braking energy tracking
Wei Zhang, Yan Gao, Hongjuan Zhang. Multi-Parameter Model Prediction of Braking Energy Tracking Strategy for Electric Vehicles[J]. Automobile Technology, 2025 , (1) : 20 -25 . DOI: 10.19620/j.cnki.1000-3703.20231214
Year 2025 volume Issue 1
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Article Info
doi: 10.19620/j.cnki.1000-3703.20231214
  • Online Date:2025-11-18
  • Published:2025-01-24
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  • Revised:2024-03-19
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    Taiyuan University of Technology, Taiyuan 030024
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表12种不同金属材料的力学参数

Family
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Number of
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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