Research on Kinetic Energy Recovery of Energy Vehicle ABS Solenoid Valve Based on the ELM Deep Learning Model

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 1 - 7
• Main Authors: Tu, Chaoqun, Zhang, Lingli
• Format: Journal Article
• Language: English
• Published: New York Hindawi 09.08.2022; Hindawi Limited
• Subjects: Antilock braking systems; Apathy; Automobiles; Braking systems; Deep learning; Electric vehicles; Energy; Energy consumption; Energy recovery; Errors; Kinetic energy; Neural networks; Optimization; Solenoid valves; Velocity; Weighting methods; Japan
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2022/6571085

Aiming at the energy vehicle ABS kinetic energy recovery, this study optimizes the ABS system through the IPSO-ELM model, so that the energy vehicle can recover the energy generated by the ABS system to the greatest extent, so as to achieve the purpose of kinetic energy recovery and reduce energy consumption and vehicle cost. Based on the PSO-ELM model, a linear decreasing weighting method is introduced, and then, an IPSO-ELM model is proposed for the optimization analysis of ABS brake kinetic energy recovery. The results show that compared with the simple ELM model and PSO-ELM model, the simulation mean square error and relative error are significantly smaller, the generalization ability and prediction accuracy are higher, and the maximum relative error of the prediction result is 5.43% and the average relative error is 2.72%. The results confirm that the use of IPSO-ELM for ABS kinetic energy recovery optimization is extremely effective, and the study of ABS kinetic energy recovery for energy vehicles based on IPSO-ELM model optimization has strong application prospects and application potential.