Parameter Matching, Optimization, and Classification of Hybrid Electric Emergency Rescue Vehicles Based on Support Vector Machines

Agyeman, Philip; Tan, Guojun; Alex, Frimpong J.; Valiev, Jamshid; Owusu-Ansah, Prince; Olayode, Isaac Oyeyemi; Hassan, Md. Imtaiyaz

doi:10.3390/en15197071

Cited by 6 publications

(1 citation statement)

References 53 publications

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“…The sample vehicle is loaded to the rated load and the load is evenly arranged to ensure that the load is evenly distributed to each wheel [30]. The vehicle is started at the initial position of the test, and then the accelerator pedal is quickly pushed to the maximum and then maintained [33]. At the same time, the start time is recorded, the speed reaches 50 km/h and 100 km/h, respectively, with the maximum acceleration, and the end time is recorded.…”

Section: Implementation Of Vehicle Drivingmentioning

confidence: 99%

Parameter Matching of Power Systems and Design of Vehicle Control Strategies for Mini-Electric Trucks

Ma¹,

Gu²,

Feng³

et al. 2023

WEVJ

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Mini-electric trucks have been widely used because of their high efficiency and zero emission with the rapid development of electronic commerce and express industry. So, improvement of dynamic performance and economy becomes crucial. The research in this field mainly focuses on passenger vehicles at present. However, most passenger vehicles are front−engine, front-drive vehicles; for mini trucks of front−engine and rear-drive, if the dynamics model of passenger vehicles is applied to mini−electric trucks, the dynamic parameters calculated will not be accurate. To enhance the accuracy of the dynamic parameters of mini-electric trucks, by combining the characteristics of mini trucks, the dynamic parameters are designed, and the types of drive motors and power batteries are selected, the dynamic model of mini−electric trucks is established. To improve the economy, control strategies, with five working modes switching, were established. On this basis, the simulation model is established, and the dynamic and economy simulation analysis and performance test were carried out. In applying the method, the error rate of maximum speed, acceleration time, and maximum gradient between simulation results and test results are 0.641% and 5.63% (15.328%), respectively, proving that the dynamic index has reached the expected value and endurance mileage is up to 295 Km under UDC conditions, increased by 5% after the vehicle control strategy was adopted. The results show that the parameter matching is reasonable and the vehicle control strategy is suitable for mini-electric trucks. The research method and conclusions can provide valuable references for the development of power systems for mini−electric trucks.

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Section: Implementation Of Vehicle Drivingmentioning

confidence: 99%