Abstract
An accurate prediction of the State of Charge (SOC) of an Electric Vehicle (EV) battery is important when determining the driving range of an EV. However, the majority of the studies in this field have either been focused on the standard driving cycle (SDC) or the internal parameters of the battery itself to predict the SOC results. Due to the significant difference between the real driving cycle (RDC) and SDC, a proper method of predicting the SOC results with RDCs is required. In this paper, RDCs and deep learning methods are used to accurately estimate the SOC of an EV battery. RDC data for an actual driving route have been directly collected by an On-Board Diagnostics (OBD)-II dongle connected to the author’s vehicle. The Global Positioning System (GPS) data of the traffic lights en route are used to segment each instance of the driving cycles where the Dynamic Time Warping (DTW) algorithm is adopted, to obtain the most similar patterns among the driving cycles. Finally, the acceleration values are predicted from deep learning models, and the SOC trajectory for the next trip will be obtained by a Functional Mock-Up Interface (FMI)-based EV simulation environment where the predicted accelerations are fed into the simulation model by each time step. As a result of the experiments, it was confirmed that the Temporal Attention Long–Short-Term Memory (TA-LSTM) model predicts the SOC more accurately than others.
Highlights
Since Electric Vehicles (EVs) do not emit CO2, they have a great potential to prevent air pollution [1]
The acceleration values are predicted from deep learning models, and the SOC trajectory for the trip will be obtained by a Functional Mock-Up Interface (FMI)-based EV simulation environment where the predicted accelerations are fed into the simulation model by each time step
The accuracy for the real driving cycle (RDC) is much lower than the standard driving cycle (SDC)
Summary
Since Electric Vehicles (EVs) do not emit CO2 , they have a great potential to prevent air pollution [1]. It is attractive for drivers to use EVs, because the cost of charging the battery of an electric vehicle is much cheaper than the cost of refueling [2]. The performance of electric vehicle batteries has greatly improved and, there are EVs capable of driving more than 500 km when fully charged. When driving an electric vehicle, it is very important to predict the driving distance through the state-of-charge (SOC) of the battery. Accurate battery SOC measurement is an important feature in the BMS of electric vehicles, which can be implemented with microcontrollers (MCUs). Among recent studies, a method to more accurately measure SOC using a cloud data center that provides high-capacity and high-performance calculations for big-data-based, data-driven
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have