A Novel Closed-Loop System for Vehicle Speed Prediction Based on APSO LSSVM and BP NN

Xiaokai Guo,Zhi Chen,Xianguo Yan,Zhiyu Meng

doi:10.3390/en15010021

Abstract

Vehicle speed prediction plays a critical role in energy management strategy (EMS). Based on the adaptive particle swarm optimization–least squares support vector machine (APSO-LSSVM) algorithm with BP neural network (BPNN), a novel closed-loop vehicle speed prediction system is proposed. The database of a vehicle internet platform was adopted to construct a speed prediction model based on the APSO-LSSVM algorithm. Furthermore, a BPNN is established according to the local high-precision nonlinear fitting relationship between the predicted value and error so as to correct the prediction value. Then, the results are returned to the APSO-LSSVM model for calculating the minimum fitness function, thus obtaining a closed-loop prediction system. Finally, equivalent fuel consumption minimization strategy (ECMS) based EMS was performed. According to the simulation results, the RMSE performance is 0.831 km/h within 5 s, which is over 20% higher than other performances. Additionally, the training time is 15 min within 5 s, which is advantageous over BPNN. Furthermore, fuel consumption increases by 6.95% compared with the dynamic-programming algorithm and decreased by 5.6%~10.9% compared with the low accuracy of speed prediction. Overall, the proposed method is crucial for optimizing EMS as it is not only effective in improving prediction accuracy but also capable of reducing training time.

Highlights

Electric passenger cars have been increasingly popularized, which aim to reduce oil consumption while simultaneously addressing air pollution [1]
Focusing on the characteristics of LS-SVM and BP neural networks, this paper proposes a novel closed-loop system intended for vehicle speed prediction based on the adaptive particle swarm optimization–least squares support vector machine (APSO-LSSVM) and BP
A novel closed-loop vehicle speed prediction system based on the APSOLSSVM and BP NN was proposed to improve the energy management strategy (EMS) of the fuel cell hybrid heavy truck

Summary

Introduction

Electric passenger cars have been increasingly popularized, which aim to reduce oil consumption while simultaneously addressing air pollution [1]. For electric heavy trucks, the main downside of short endurance mileage results in declining sales. Despite the capability of advanced battery storage system to ensure the sufficient mileage of heavy trucks, this type of battery remains immature and has not been verified in real-world conditions [2]. As a promising direction of transport electrification, fuel cell hybrid vehicle (FCHV) has been drawing widespread attention for research studies across the automotive industry due to advantages such as zero emission, low noise and high efficiency [3]. Fuel cell and electric battery technologies are considered as practical solutions for zero emission. For FCHV, given the immaturity of existing technology and the high cost of fuel, high-efficiency EMS can minimize fuel consumption and extends the service life of components [5]

Methods

Results

Conclusion