# Real Driving Cycle-Based State of Charge Prediction for EV Batteries Using Deep Learning Methods
## 2 Related Work  
This section classifies the related studies into two major categories: modeling and simulation, and SOC estimation and prediction in the EV domain. Since there have been significant research efforts in these fields, we are focused on work that has been conducted rather recently, and discuss the limitations of these works. First of all, in the field of modeling and simulation, extensive research has been carried out to improve the accuracy of EV energy consumption estimation with high-fidelity simulation models [11; 12; 13; 14; 15; 16; 17]. By using simulation environments such as MATLAB/Simulink and DACCOSIM, each physical component of an EV is mathematically modeled to form a subsystem block, which is composed of primitive blocks. The simulation is performed by the subsystem blocks mapping the input signals to the output values, which are passed to the subsequent blocks over time during the simulation. Recently, distributed co-simulation based on Functional Mock-up Interface (FMI), which provides a standardized interface to ease the sharing of different models [18], has been widely used. Techniques to reduce the simulation time while maintaining the simulation accuracy have also been developed. [PERSON] proposed a redundancy reduction algorithm (RRA) to increase the simulation speed by adaptively adjusting the simulation step size, increasing it when a specific pattern of the cycle is detected as repeated, and decreasing it when a zero-crossing point is detected [19]. However, this is mainly applicable to the SDCs, especially the New European Driving Cycle (NEDC), where many repetitive parts are known ahead of the simulation. These studies heavily rely on the SDCs to validate the methods. However, as mentioned above, SDCs have inherent limitations, as they are not real driving cycles (RDCs). Even though a variety of SDCs have been developed to emulate urban, rural, and highway environments, they do not reflect personalized factors, i.e., individual drivers' habits, which are highly complicated as they dynamically change over time [20; 21]. Moreover, they can hardly incorporate factors such as traffic conditions, traffic signals, and time of driving, which are unique to each driving instance [22]. Therefore, in order to provide more personalized simulation results, both the mathematically defined models and data-driven methods that learn the drivers' patterns from the previous RDCs are required. Next, there have been a myriad of studies in the area of SOC estimation and prediction. In the application of the pure EV, accurate prediction of the battery SOC consumption is highly important, since this would directly lead to an accurate prediction of the EV range [23]. According to the literature [24; 25; 26], methodological approaches to the SOC estimation can be divided into two: ML-based and non-ML-based methods. In brief, Coulomb counting and open-circuit voltage (OCV) methods have been used as conventional methods, while, recently, ML models consisting of deep neural networks (DNNs) have been intensely applied [24; 25; 26]. In the ML-based data-driven approach, the SOC values are predicted by automatically adjusting parameters to minimize the resultant error by learning from the pattern of the input features such as current, voltage, and temperature [27; 28; 29]. In this way, the models are fitted to the specific real-world measurement data, which enables predictions with enhanced accuracy under similar conditions. However, few studies have been conducted on the prediction of future SOC trends through simulation. Most of the research has been focused on the internal parameters of the battery management system (BMS) [30] to estimate the SOC value. That is, research on the relationship between the EV subsystems in an integrated view to improve the performance of the simulation, as well as reducing the overhead of the tedious model-based simulation, has been insufficient.