Plug-in Electric Vehicle (PEV) user charging behavior has a significant influence on a distribution network and its reliability. Generally, monitoring energy consumption has become one of the most important factors in green and micro grids; therefore, predicting the charging demand of PEVs (the energy consumed during the charging session) could help to efficiently manage the electric grid. Consequently, three machine learning methods are applied in this research to predict the charging demand for the PEV user after a charging session starts. This approach is validated using a dataset consisting of seven years of charging events collected from public charging stations in the state of Nebraska, USA. The results show that the regression method, XGBoost, slightly outperforms the other methods in predicting the charging demand, with an RMSE equal to 6.68 kWh and R² equal to 51.9%. The relative importance of input variables is also discussed, showing that the user’s historical average demand has the most predictive value. Accurate prediction of session charging demand, as opposed to the daily or hourly demand of multiple users, has many possible applications for utility companies and charging networks, including scheduling, grid stability, and smart grid integration.

In the era of burgeoning electric vehicle (EV) popularity, understanding the patterns of EV users’ behavior is imperative. This paper examines the trends in household charging sessions’ timing, duration, and energy consumption by analyzing real-world residential charging data. By leveraging the information collected from each session, a novel framework is introduced for the efficient, real-time prediction of important charging characteristics. Utilizing historical data and user-specific features, machine learning models are trained to predict the connection duration, charging duration, charging demand, and time until the next session. These models enhance the understanding of EV users’ behavior and provide practical tools for optimizing the EV charging infrastructure and effectively managing the charging demand. As the transportation sector becomes increasingly electrified, this work aims to empower stakeholders with insights and reliable models, enabling them to anticipate the localized demand and contribute to the sustainable integration of electric vehicles into the grid.

Battery Electric Vehicles (BEVs) have seen a substantial growth in the recent past, and this trend is expected to continue. This growth has been far from uniform geographically, with large differences in BEV uptake between countries, states, and cities. This non-uniform growth can be attributed to the demographic and non-demographic factors that characterize a geographical location. In this paper, the demographic factors that affect BEV uptake at the Zone Improvement Plan (ZIP) code level are studied extensively across several states in the United States to understand BEV readiness at its most granular form. Demographic statistics at the ZIP code level more accurately describe the local population than national-, state-, or city-level demographics. This study compiled and preprocessed 242 demographic features to study the impact on BEV uptake in 7155 ZIP codes in 11 states. These demographic features are categorized based on the type of information they convey. The initial demographic features are subjected to feature engineering using various formed hypotheses to extract the optimal level of information. The hypotheses are tested and a total of 82 statistically significant features are selected. This study used correlation analysis to validate the feature engineering and understand the degree of correlation of these features to BEV uptake, both within individual states and at the national level. Results from this study indicate that higher BEV adoption in a state results in a stronger correlation between demographic factors and BEV uptake. Features related to the number of individuals in a ZIP code with an annual income greater than USD 75 thousand are strongly correlated with BEV uptake, followed by the number of owner-occupied housing units, individuals driving alone, and working from home. Features containing compounded information from distinct categories are often better correlated than features containing information from a single category. In-depth knowledge of local BEV uptake is important for applications related to the accommodation of BEVs, and understanding what causes differences in local uptake can allow for both the prediction of future growth and the stimulation of it.

Accurate estimation of lithium-ion battery State of Health (SoH) is critical for emerging applications such as reconfigurable battery systems. Although data-driven machine learning methods are promising, they often rely on costly, time-intensive aging experiments and extensive feature engineering. This work proposes a lightweight SoH-prediction framework validated on both physics-informed synthetic aging data and the NASA battery aging dataset. We evaluated Random Forest (RF) and Feedforward Neural Network (FNN) models that use only a limited number of samples from an early segment of the raw discharge voltage curve as input. Results show that RF consistently outperforms FNN across input sizes in deterministic or noise-free environments, achieving an RMSE of 0.07% SoH using just 5 voltage samples. In inherently stochastic experimental data, however, FNN can achieve an RMSE 50% lower than RF (1.28 vs. 2.87), but requires 37× more mathematical operations per inference. These findings emphasize the predictive value of the early-discharge-voltage region and demonstrate that compact, low-feature-complexity models can deliver accurate SoH estimates. Overall, the approach supports a goal of combining informed synthetic data with limited real measurements to build robust, scalable SoH predictors, reducing dependence on labor-intensive degradation testing and feature-heavy pipelines.