A Hybrid Machine Learning Model for Range Estimation of Electric Vehicles

Data-driven solutions to Electric Vehicle (EV) range estimation is attracting attention recently due to the prevalence of Internet of Things (IoT). However, there raise the Big Data problems with the increased volume and number of sensory sources of unstructured data collected from the EV equipped with In-Vehicle Networks. This means that traditional statistical analysis and Machine Learning tools are not suitable to be directly applied to analyse and interpret data. Hence, we aim to develop a Hybrid Machine Learning Model to predict the power consumption of EV trips practically considering multivariate high- dimensional data and meanwhile extract knowledge from the historical trip features for further applications. The proposed Hybrid Model is a modified Self-Organizing Maps (SOM) integrating Regression Trees (RT) to predict the power consumption of EV trips. The experimental results, including both cross-validation and mathematical accuracy measuring criteria, demonstrate that our Hybrid Model could not only provide a better power consumption estimation of EV trips but also reveal the inherent of the EV Big Data.

[1]  Yannis Manolopoulos,et al.  Feature-based classification of time-series data , 2001 .

[2]  Nick S. Jones,et al.  Highly Comparative Feature-Based Time-Series Classification , 2014, IEEE Transactions on Knowledge and Data Engineering.

[3]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[4]  R. Tibshirani,et al.  Prediction by Supervised Principal Components , 2006 .

[5]  Liang Wang,et al.  Structure-Based Statistical Features and Multivariate Time Series Clustering , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[6]  N. Altman.  An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression , 1992 .

[7]  Alfred Ultsch,et al.  Integration of neural networks with knowledge-based systems , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[8]  Yuhe Zhang,et al.  Remaining driving range estimation of electric vehicle , 2012, 2012 IEEE International Electric Vehicle Conference.

[9]  Hongwen He,et al.  A Method for Identification of Driving Patterns in Hybrid Electric Vehicles Based on a LVQ Neural Network , 2012 .

[10]  Ricardo A. Daziano,et al.  Conditional-logit Bayes estimators for consumer valuation of electric vehicle driving range , 2013 .

[11]  Angelos Amditis,et al.  Online prediction of an electric vehicle remaining range based on regression analysis , 2014, 2014 IEEE International Electric Vehicle Conference (IEVC).

[12]  Tsutomu Suzuki,et al.  To what extent can speed management alleviate the range anxiety of EV? , 2013, 2013 World Electric Vehicle Symposium and Exhibition (EVS27).

[13]  Hai Yu,et al.  Driving pattern identification for EV range estimation , 2012, 2012 IEEE International Electric Vehicle Conference.

[14]  Mo-Yuen Chow,et al.  Big-data framework for electric vehicle range estimation , 2014, IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society.

[15]  John G. Hayes,et al.  Simplified electric vehicle power train models and range estimation , 2011, 2011 IEEE Vehicle Power and Propulsion Conference.

[16]  B. Allen,et al.  Do you feel lucky? Why current range estimation methods are holding back EV adoption , 2013 .

[17]  George C. Runger,et al.  A time series forest for classification and feature extraction , 2013, Inf. Sci..

[18]  Chih-Hung Wu,et al.  A Novel Big Data Modeling Method for Improving Driving Range Estimation of EVs , 2015, IEEE Access.