# Real Driving Cycle-Based State of Charge Prediction for EV Batteries Using Deep Learning Methods
###### 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.  
electric vehicle; real driving cycle; recurrent neural network; simulation; state of charge; temporal attention 2021 11 11285. 11.11285.  
field-programmable gate array (FPGA), and Application-Specific Integrated Circuits (ASIC) are employed. Specifically, a Tesla Full Self-Driving (FSD) computer, built on ASIC, meets 50 Tera floating point operations per second (TFLOPS) of the Neural Network model, and uses 100 W or less power consumption during computation [8]. To benefit from these state-of-the-art embedded systems adapted to the full CAVs, it is important to both estimate the current SOC and predict the future SOC to aid the drivers in decision-making.  
It is important to accurately measure the current SOC, but it is also necessary to predict the future SOC based on the current driving data. An accurate prediction of the SOC can help determine the possible driving distance and charging time. There are two main methods to predict the SOC of an EV battery. The first one is predicting the SOC through simulation based on the vehicle dynamics. This approach was implemented by [9], which used standard driving cycles (SDC) as input of the simulation model. The second method consists of predicting the SOC through Machine Learning (ML) techniques using the battery data collected while driving the electric vehicle [10]; for example, using information from the battery itself (voltage, current, temperature) and ML algorithms to predict the future SOC.  
Recently, there have been studies to predict SOC through complex mathematical formulas, considering the actual driving cycle of electric vehicles, but ML was not used.  
In this paper, we propose an algorithm that accurately predicts the SOC of an EV battery by acquiring actual driving information through OBD-II, dividing and preprocessing this driving information by section based on GPS information and DTW, training a neural network with the dataset and running simulations using Functional Mock-Up Units (FMU).