Implementation of machine learning based real time range estimation method without destination knowledge for BEVs
Authors:
- H.A. YavasogluHYH.A. Yavasoglu
- Y.E. TetikYTY.E. Tetik
- K. GokceKGK. Gokce
Abstract
No abstract availableKeywords
Funding Information
- Scientific and Technological Research Council of Turkey (TUBITAK)
This publication has 22 references indexed in Scilit:
- Accurate range estimation for an electric vehicle including changing environmental conditions and traction system efficiencyIET Electrical Systems in Transportation, 2017
- Driver alerting system using range estimation of electric vehicles in real time under dynamically varying environmental conditionsIET Electrical Systems in Transportation, 2016
- Power reduction optimization with swarm based technique in electric power assist steering systemEnergy, 2016
- Impacts of Real-World Driving and Driver Aggressiveness on Fuel Consumption of 48V Mild Hybrid VehicleSAE International Journal of Alternative Powertrains, 2016
- Advanced Machine Learning Approach for Lithium-Ion Battery State Estimation in Electric VehiclesIEEE Transactions on Transportation Electrification, 2015
- Power Split Control Strategy for an EV Powertrain With Two Propulsion MachinesIEEE Transactions on Transportation Electrification, 2015
- Future vision for reduction of range anxiety by using an improved state of charge estimation algorithm for electric vehicle batteries implemented with low‐cost microcontrollersIET Electrical Systems in Transportation, 2015
- Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methodsJournal of Power Sources, 2013
- The vehicle emissions and performance monitoring system: analysis of tailpipe emissions and vehicle performanceTransportation Planning and Technology, 2004
- Techniques for estimating the residual range of an electric vehicleIEEE Transactions on Vehicular Technology, 2001