Affiliation:
1. Wuhan University of Technology
Abstract
<div class="section abstract"><div class="htmlview paragraph">To improve the braking energy recovery rate of pure electric garbage removal vehicles and ensure the braking effect of garbage removal vehicles, a strategy using particle swarm algorithm to optimize the regenerative braking fuzzy control of garbage removal vehicles is proposed. A multi-section front and rear wheel braking force distribution curve is designed considering the braking effect and braking energy recovery. A hierarchical regenerative braking fuzzy control strategy is established based on the braking force and braking intensity required by the vehicle. The first layer is based on the braking force required by the vehicle, based on the front and rear axle braking force distribution plan, and uses fuzzy controllers. Achieve one-time distribution of the front axle braking force; the second layer, according to the magnitude of the braking intensity, divides the braking conditions into light braking, moderate braking and emergency braking, and realizes braking under the three working conditions respectively. Secondary distribution of front axle braking force. Using the driving mileage contribution as the evaluation index and NEDC as the simulation working condition, it is verified that the regenerative braking control strategy can achieve energy recovery. To further improve the braking energy recovery and ensure the vehicle braking effect, the braking effect and braking energy recovery are used as the optimization objective function, the particle swarm algorithm is used to optimize the fuzzy rules, and the optimized fuzzy controller is reloaded into the regenerative braking control Simulation is carried out in the strategy to verify that the designed multi-section front and rear wheel braking force distribution curves and fuzzy rules optimized by particle swarm algorithm can effectively improve regenerative braking energy recovery and improve vehicle driving range, while ensuring braking safety.</div></div>
Reference21 articles.
1. Liang , L. Comparative Analysis of Global Energy Consumption Structure Data in Recent 10 Years World Petroleum Industry 2020 41 47
2. Yao , L. Automobile Production and Sales in 2022 to Achieve a Small year-on-year Growth Automobile Zongzong 2023 100 101
3. Liu , S. , Li , Z. , and Ji , H.H. A Novel Anti-Saturation Model-Free Adaptive Control Algorithm and Its Application in the Electric Vehicle Braking Energy Recovery System Symmetry 14 3 2022 580 580 10.3390/SYM14030580
4. Qi , L.F. , Wu , X.P. , and Zeng , X.H. An Electro-mechanical Braking Energy Recovery System Based on Coil Springs for Energy Saving Applications in Electric Vehicles Energy 2020 200 10.1016/j. energy.2020.117472
5. Ko , J.W. , Ko , S.Y. , Kim , I.S. , and Hyun , D.Y. Co-operative Control for Regenerative Braking and Friction Braking to Increase Energy Recovery without Wheel Lock International Journal of Automotive Technology 15 2 2014 10.1007/s12239-014-0026-6