Velocity prediction using Markov Chain combined with driving pattern recognition and applied to Dual-Motor Electric Vehicle energy consumption evaluation

Vehicle velocity prediction is of great significance in electric vehicle (EV) energy consumption evaluation. However, vehicle velocity prediction is complicated due to the uncertainty of vehicle velocity and driving pattern. To address the challenges, a vehicle velocity prediction model based on driving pattern recognition (DPR) and Markov Chain (MC) is proposed. Firstly, three typical driving cycles are adopted to constructed sample driving cycle. K-means algorithm is used for clustering the constructed driving cycle segments. And Learning Vector Quantization (LVQ) neural network (NN) is applied to recognizing the driving pattern in real-time, then the Markov Transition Matrix (MTM) corresponding to three clustered driving patterns are adopted to predict vehicle velocity. Finally, the velocity prediction results are applied to dual-motor EV energy consumption evaluation model which is established by Multiple Linear Regression (MLR). Velocity prediction results show that the Root Mean Square Error (RMSE) value of velocity prediction based on DPR and MC decreased by 24.1% compared with MC prediction without DPR. The velocity prediction is applied to energy consumption evaluation, the results shows that the error is 2.33%, which is sufficient to demonstrate the accuracy of the velocity prediction model and the energy consumption evaluation model. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

A vehicle velocity prediction model based on DPR and MC is proposed to reduce prediction errors significantly. The results demonstrate high accuracy of the velocity prediction model and its applicability in energy consumption evaluation.