Electric Vehicle Battery Remaining Charging Time Estimation Considering Charging Accuracy and Charging Profile Prediction

by   Junzhe Shi, et al.

Electric vehicles (EVs) have been growing rapidly in popularity in recent years and have become a future trend. It is an important aspect of user experience to know the Remaining Charging Time (RCT) of an EV with confidence. However, it is difficult to find an algorithm that accurately estimates the RCT for vehicles in the current EV market. The maximum RCT estimation error of the Tesla Model X can be as high as 60 minutes from a 10 (SOC) while charging at direct current (DC). A highly accurate RCT estimation algorithm for electric vehicles is in high demand and will continue to be as EVs become more popular. There are currently two challenges to arriving at an accurate RCT estimate. First, most commercial chargers cannot provide requested charging currents during a constant current (CC) stage. Second, it is hard to predict the charging current profile in a constant voltage (CV) stage. To address the first issue, this study proposes an RCT algorithm that updates the charging accuracy online in the CC stage by considering the confidence interval between the historical charging accuracy and real-time charging accuracy data. To solve the second issue, this study proposes a battery resistance prediction model to predict charging current profiles in the CV stage, using a Radial Basis Function (RBF) neural network (NN). The test results demonstrate that the RCT algorithm proposed in this study achieves an error rate improvement of 73.6



There are no comments yet.


page 3

page 10


Machine learning pipeline for battery state of health estimation

Lithium-ion batteries are ubiquitous in modern day applications ranging ...

Cloud-Based Dynamic Programming for an Electric City Bus Energy Management Considering Real-Time Passenger Load Prediction

Electric city bus gains popularity in recent years for its low greenhous...

EVScout2.0: Electric Vehicle Profiling Through Charging Profile

EVs (Electric Vehicles) represent a green alternative to traditional fue...

Analysis of NARXNN for State of Charge Estimation for Li-ion Batteries on various Drive Cycles

Electric Vehicles (EVs) are rapidly increasing in popularity as they are...

Estimating State of Charge for xEV batteries using 1D Convolutional Neural Networks and Transfer Learning

A state of charge estimator is an essential component of battery managem...

On-the-Fly Power-Aware Rendering

Power saving is a prevailing concern in desktop computers and, especiall...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.