Abstract
The share of energy consumption in the
transportation sector is projected to increase at an annual
average rate of 1.4% up to 2040. This is primarily due to a
transition towards electric vehicles (EVs) from internal
combustion engine- based modes of transportation. Batteries are
the most crucial component in EVs, constituting a significant
share of the price of the vehicle. With usage, batteries degrade,
thereby, limiting their ability to store energy which adversely
impacts the driving range offered by EVs. Therefore, the need is to
study the deterioration of batteries in electric means of
transportation. We have created data-driven models to monitor
battery health, predict the deterioration in batteries and give
insights to the EV owners to make better decisions. The dataset
used in this study is published by Sandia National Labs (SNL).
It is a result of experiments performed on NMC cells. We present
a comparison of three models - multiple linear regression, support
vector regression, and artificial neural network for battery health
monitoring with mean average percentage error (MAPE) of 1.99,
0.74, and 0.72 respectively.