  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 % to 99 % state-of-charge
(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
% and 84.4 % over the traditional method in the CC and CV stages, respectively.
