Electric Vehicle User Behavior Prediction Using Hybrid Kernel Density Estimator

Machine Learning Approaches for EV Charging Behavior: A Review

“…Comparing correlation between charging start time, session duration and energy using Pearson correlation coefficient and Kendall rank correlation showed that correlation can only be noticed if the classifications into the 2 groups, i.e., intra-day and inter-day, were made. A Hybrid estimator that uses both GKDE and DKDE was proposed in [81] to predict charging session duration and energy consumption. Comparison of MED shows that accuracy of prediction is better using the hybrid model than the individual models.…”

Stochastic bidding strategy for electrical vehicle charging stations to participate in frequency containment reserves markets

“…In this regard, an energy management system using driving pattern prediction is proposed in [6]. Accordingly, several researchers focus on modelling and forecasting EVs using the driving behaviour [7][8][9][10][11][12][13][14] to mitigate the adverse influences of EVs on power systems. A Monte Carlo-based method combined with the national household travel survey is used in [7,8], while a modified Monte Carlo is proposed in [9] by removing less likely scenarios.…”

“…However, this research [6][7][8][9][10][11][12][13][14] uses the driving behaviour of conventional vehicles to estimate the EV driving model, which may lead to some errors due to differences between EVs and conventional cars. On the other hand, some researchers use real data of existing EV charging to model their behaviour [15][16][17][18].…”

Hierarchical distributed framework for EV charging scheduling using exchange problem

“…This is called vehicle-to-grid (V2G) which its potential was investigated by Kempton and Tomić [5]. Nevertheless, EV load management is challenging due to the uncertainty in arrival time, departure time and energy demand (Khaki et al [6], Chung et al [7]), limited capacity of the energy resources and distribution grid equipment (Khaki et al demand. Then, the optimal charging profiles are sent back to EVs: By Clement-Nyns et al [3], it is shown that the uncoordinated EV charging increases power loss and voltage deviation significantly, therefore the authors propose a centralized method where the EV owners have no control over the charging profile, and it is decided by DNO; a model predictive based algorithm is proposed by Tang and Zhang [9] for total charging cost reduction, where the authors use the truncated sample average approximation to reduce the complexity of their centralized method at the cost of performance degradation; Wang et al [10] introduce a centralized event-triggered receding horizon method to reduce EV charging cost in a campus parking; an optimal strategy for V2G aggregator is designed by Peng et al [11] to maximize the economic benefit by participation in frequency regulation while satisfying EV owners' demand; a centralized algorithm is designed by Bilh et al [12] to flatten the netalod fluctuations due to renewable energy resources using EV charging control; Zheng et al [13] propose a real-time EV charging scheduling where the computational complexity is reduced by introducing a capacity margin and the charging priority indices; a centralized mechanism is proposed by Perez-Diaz et al [14] in which a thirdparty entity coordinates a day-ahead bidding system to optimize the global bid; a transactive EV charging management is presented in Liu et al [15] to maximize the real-time profit based on the net electricity exchange with the grid; and a two-layer centralized EVCS is proposed by Mehta et al [16] where each aggregator optimizes active power of the EVs in the first layer, and the second layer provides reactive power management for loss reduction in the grid.…”