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Batteries have been considered a key element in several applications, ranging from grid-scale storage systems through electric vehicles to daily-use small-scale electronic devices. However, excessive charging and discharging will impair their capabilities and could cause their applications to fail catastrophically. Among several diagnostic indices, state-of-charge estimation is essential for evaluating a battery's capabilities. Various approaches have been introduced to reach this target, including white, gray, and black box or data-driven battery models. The main objective of this work is to provide an extensive comparison of currently highly utilized machine learning-based estimation techniques. The paper thoroughly investigates these models' architectures, computational burdens, advantages, drawbacks, and robustness validation. The evaluation's main criteria were based on measurements recorded under various operating conditions at the Energy Systems Research Laboratory (ESRL) at FIU for the eFlex 52.8 V/5.4 kWh lithium iron phosphate battery pack. The primary outcome of this research is that, while the random forest regression (RFR) model emerges as the most effective tool for SoC estimation in lithium-ion batteries, there is potential to enhance the performance of simpler models through strategic adjustments and optimizations. Additionally, the choice of model ultimately depends on the specific requirements of the task at hand, balancing the need for accuracy with the complexity and computational resources available and how it can be merged with other SoC estimation approaches to achieve high precision. [ABSTRACT FROM AUTHOR]

Batteries are considered critical elements in most applications nowadays due to their power and energy density features. However, uncontrolled charging and discharging will negatively affect their functions and might result in a catastrophic failure of their applications. Hence, a battery management system (BMS) is mandated for their proper operation. One of the critical elements of any BMS is the state of charge (SoC) estimation process, which highly determines the needed action to maintain the battery's health and efficiency. Several methods were used to estimate the Lithium‐ion batteries (LIBs) SoC, depending on the LIBs model or any other suitable technique. This article provides a critical review of the existing SoC estimation approaches and the main LIB models with their pros and cons, their possibility to integrate with each other's for precise estimation results, and the applicability of these techniques in electric vehicles and utility applications with the commonly used standards and codes in these sectors. Moreover, this study will also explore a future framework for integrating digital twins (DTs) with BMSs for improved and advanced management. Based on this comprehensive review, it can be concluded that merging the model‐based estimation techniques with the data‐driven approaches with their promising development to determine the dynamic patterns inside the battery can efficiently achieve precise estimation results while reducing the complexity of these models. This integration will align also with the integration of digital twin technology to provide complete and accurate supervision for the BMSs. This article is categorized under:Emerging Technologies > Energy StorageEmerging Technologies > DigitalizationEnergy and Power Systems > Distributed Generation [ABSTRACT FROM AUTHOR]

Emerging electric vehicle (EV) technology requires high-voltage energy storage systems, efficient electric motors, electrified power trains, and power converters. If we consider forecasts for EV demand and driving applications, this article comprehensively reviewed power converter topologies, control schemes, output power, reliability, losses, switching frequency, operations, charging systems, advantages, and disadvantages. This article is intended to help engineers and researchers forecast typical recharging/discharging durations, the lifetime of energy storage with the help of control systems and machine learning, and the performance probability of using AlGaN/GaN heterojunction-based high-electron-mobility transistors (HEMTs) in EV systems. The analysis of this extensive review paper suggests that the Vienna rectifier provides significant performance among all AC–DC rectifier converters. Moreover, the multi-device interleaved DC–DC boost converter is best suited for the DC–DC conversion stage. Among DC–AC converters, the third harmonic injected seven-level inverter is found to be one of the best in EV driving. Furthermore, the utilization of multi-level inverters can terminate the requirement of the intermediate DC–DC converter. In addition, the current status, opportunities, challenges, and applications of wireless power transfer in hybrid and all-electric vehicles were also discussed in this paper. Moreover, the adoption of wide bandgap semiconductors was considered. Because of their higher power density, breakdown voltage, and switching frequency characteristics, a light yet efficient power converter design can be achieved for EVs. Finally, the article's intent was to provide a reference for engineers and researchers in the automobile industry for forecasting calculations. [ABSTRACT FROM AUTHOR]

A voltage-quadrupler interleaved bidirectional dc–dc converter with intrinsic equal current sharing is presented in this article. The proposed converter offers a wide voltage conversion ratio, which makes it suitable for interconnecting the energy storage unit with dc bus for electric vehicle applications. A high voltage gain and low voltage stress across switches are achieved by adopting the capacitive voltage-divider stage, which enables the designer to employ a low-voltage switch with a small on-resistance RDS(ON). As a result, it allows the converter to run more efficiently and cooler. An interleaved connection at the low-voltage side paves the way for reducing the current ripple, which reduces the filter size and increases the power-density. Moreover, a built-in equal current sharing is achieved for two interleaved phases without using a current-sharing control scheme. The operating principle, steady-state characteristics including the current and voltage stress of the switches, and comparison with other state-of-the-art bidirectional converters are carried out in detail. Finally, to verify theoretical analysis, a 2-kW scaled-down laboratory prototype of the proposed converter using Gallium-Nitride switches is implemented. [ABSTRACT FROM AUTHOR]

In this paper, a new bidirectional dc-dc converter that is based on the quadratic and switched-capacitor structures is proposed for Electric Vehicle applications. The proposed converter has high voltage conversion ratios, low voltage stress on the semiconductor devices, constant potential difference between the grounds of its low voltage and high voltage ports, and continuous current at its low voltage port. Synchronous rectification is utilized to enhance the efficiency of the converter. In addition, an extended version of the proposed converter is discussed in this paper. Finally, the experimental results obtained from a 2-kW/800-V scaled-down prototype validate the feasibility of the proposed topology and the correctness of its theoretical analysis. [ABSTRACT FROM AUTHOR]

A modular quasi-resonant bidirectional dc–dc converter composed of half-bridge gallium nitride modules (HBGM) with reduced switch voltage stress is proposed in this paper. By using an auxiliary capacitor, a resonant circuit is formed to shape the current and voltage so that zero-voltage switching at turn-on instant is achieved. Since the switching loss dominates the power losses in a high-frequency dc–dc converter, the soft-switching performance leads to a noticeable reduction in the total loss; so, the operating temperature will decrease, and consequently, size of the heatsink will be reduced. The proposed circuit takes advantage of enhancement-mode gallium nitride (eGaN) switches regarding their small on-resistance RDS(on), low gate charge, and fast switching speed to improve both efficiency and power density. The main performance issue of adopting GaN switches in a bidirectional dc–dc converter is the high voltage stress of GaN devices, which is handled by connecting the HBGM in the active-clamped stacked configuration, as well as optimizing printed circuit board layout design. Finally, a 1 kW, 600 V laboratory prototype operating at 100 kHz is implemented to validate the proposed concept. [ABSTRACT FROM AUTHOR]

The increasing deployment of electric vehicles (EVs) across the United States has introduced many new opportunities and challenges with regards to energy management and control. In smart and connected communities (SCCs), where advanced communication infrastructures are in place, optimal coordination of EVs can significantly impact EV owners, power systems, and charging station owners. This paper develops two scheduling frameworks (static and dynamic) for optimal coordination of a fleet of cooperative EVs in a community with many charging stations and potentially different types of chargers (e.g., level 1, level 2, and DC fast). The scheduling problems are formulated as mixed-integer multi-objective optimization models and then multi-objective solution methods are utilized to find the optimal solution for each of the two scheduling frameworks. Numerical experiments simulated based on the State of Florida verify the usefulness of smart charging for better energy management and satisfying key players’ objectives and constraints. [ABSTRACT FROM AUTHOR]

A new transformerless bidirectional buck-boost converter is proposed in this paper. The proposed converter has a simple circuit structure, low component count, low voltage stress on the power transistors, and a wide voltage gain range. This makes it applicable in the energy storage charge/discharge systems, such as electric vehicles (EV), microgrids and nanogrids with energy storage units, and uninterruptible power supplies. In addition, synchronous rectification between the complementary transistors is employed to improve the converter efficiency. A comprehensive analysis of the steady-state operation, small-signal model, component parameters design, and efficiency analysis of the proposed converter operating in a continuous conduction mode is given. Finally, a 1.6-kW scaled-down prototype was built using Silicon Carbide MOSFETS to validate the effectiveness and feasibility of the proposed converter. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive solution that allows a wirelessly connected electric vehicle (EV) to be autonomously charged and discharged, during long-term parking and/or transient stops. A bilayer power flow controller for bidirectional wireless power transfer system (BWPTS) in EVs applications is proposed. The proposed controller can manage the power flow between the EV and surrounding infrastructures, such as utility grid, home micro-grid, building micro-grid, road, or another vehicle. It consists of two control layers; the first is responsible for communicating with the surrounding infrastructures and gathering information from the vehicle operator, charging station, utility grid, and battery management system. These data are analyzed to generate the reference power signal with information about the mode of operation (charge, discharge, or abstain) and rate. The second layer receives the reference signal and predicts the control parameters for the resonant converters (on both the vehicle and grid sides) to track the desired power. The proposed algorithm is adaptively estimating the mutual inductance to consider the misalignments in the system, based on a new simple real-time mutual estimation algorithm using a single voltage measurement from the vehicle side. The second control layer is designed based on an accurate analytical model for the BWPTS's power flow. For verification purposes, a prototype for BWPTS was built and driven by the proposed controller, which was implemented using a field-programmable gate array integrated circuit. Also, a vehicle-level simulation was performed to validate the operation of the proposed controller in quasi-dynamic wireless charging at traffic signals. The proposed controller shows fast and stable response during both the transient and steady-state operation in comparison with the conventional proportional-integral controller. [ABSTRACT FROM AUTHOR]

In this paper, we develop formulation of a multi-objective optimization problem (MOOP) to optimally size a battery unit (BU) ultracapacitor (UC) hybrid energy storage system (HESS) for plug-in electric vehicle (EV). In this application, the objectives were to minimize cost, weight, volume of the HESS simultaneously maximizing the remaining cycle life of the BU at the end of the driving cycle. The MOOP is solved by the non-dominated sorting genetic algorithm type 2 algorithm. Detailed mathematical models for the BU and UC are given. The thermal effect on performance and sizing are also included in the formulation. The power demand by the EV powertrain is shared amongst the BU and HESS by two methods: First is by using wavelet transformation, while the second is by using power split ratio. The ratio of the power (i.e., power split) handled by each storage unit was determined by the optimizer. A sensitivity analysis was conducted for the power splitting ratio verification. The problem was solved for using the urban dynamometer driving schedule and the highway fuel economy test driving profiles. This has resulted in sizing of an HESS with lower cost, volume, and weight than those existing in literature. Finally, the effect of changing the motor type on the MOOP result was investigated. [ABSTRACT FROM AUTHOR]

A new single-switch high step-up dc–dc converter is proposed, in this paper, for fuel cell vehicles. The proposed topology utilizes an L2C3D2 network to obtain high voltage gain and reduce the voltage stress on the power switch. Additionally, the proposed converter has a universal input voltage in order to suit the soft output characteristics of the fuel cell. Comprehensive analyses of the steady-state operation in continuous conduction mode and discontinuous conduction mode, and design considerations of the proposed converter are given. Finally, a 400 V/1.6 kW scaled-down prototype is developed to validate the effectiveness and feasibility of the proposed converter. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are recently gaining traction in the power sector due to various challenges and opportunities they provide to utility operators. For electric utilities that incorporate demand side management (DSM) programs, EVs could become either a burden or an advantage depending on their charging control strategy and the signaling of the DSM program. This paper proposes a decentralized fuzzy-based controller to successfully integrate and coordinate the charging of EVs that ensures fair charging of EVs at different point of connections (POCs) in the distribution grid where voltage conditions might not be the same. The controller operates in an autonomous mode that reduces the monetary cost of the communication overhead and preserves bandwidth. The proposed controller takes into consideration the owner requirements in terms of energy needed and time to charge, the voltage at the POC with the grid, and the pricing signal coming from the utility. The controller is tested under different DSM programs that exist in the literature. This paper also proposes a new DSM program that is capable of benefiting from EVs as prosumers that can provide grid services. The controller's performance was validated through MATLAB simulations that showed the controller's ability to successfully coordinate the charging of EVs in a fair manner and achieve flat system peaks without any rebound effect. The controller was also successfully tested in the presence of voltage control units, such as capacitor banks, and in the presence of distributed generations. [ABSTRACT FROM AUTHOR]

As electric vehicles (EVs) proliferate and dominate the roads, public charging will be required extensively. Wide range coverage of energy information is required on fingertips for taking decisions at different levels and for all stakeholders. For this reason, a need arises for a correlated and all-inclusive framework that trades the unilateral discriminatory benefits with the combined benefit of all. At the heart of such frameworks lies communication technologies to support and manage energy transfer between vehicles and the power grid, known as the Internet of EVs. This paper adds two main contributions to the work presented in the literature on the subject matter. First, the communication requirements, standards, and protocols for applications involving this mobile prosumer management in the pre-connection stage to the electric grid are outlined. Second, and based on the aforementioned assessment, this paper proposes a decentralized hierarchical multiagent system that serves as a framework for real-time bilateral decision support for EVs and the power system. The effectiveness of the proposed framework and the credibility of using the communication middleware and means are experimentally verified on a group of connected vehicles communicating with a higher end server. Also, the seamless integration between the on-board and the higher end decision support systems is demonstrated in case studies. [ABSTRACT FROM AUTHOR]

The anticipated increase in electric vehicles (EVs) adoption necessitates the need for electrified transportation infrastructure to charge these vehicles. Although the EV parking garage can represent a good investment opportunity, it brings more challenges to the distribution system operator. Therefore, the allocation and sizing of the parking garage should be carefully planned. The planning process should take into consideration the economic aspects of the investor, as well as the technical aspects of the distribution system. In this paper, a bilayer Pareto multiobjective optimization problem is formulated to optimally allocate and size an EV parking garage. The optimization formulation tries to maximize the profits of the EV parking garage investor, as well as minimize the losses and voltage deviations for the distribution system operator. Dealing with these contradicting objectives simultaneously will results in a set of Pareto solutions. The results showed the different tradeoffs that might be induced while dealing with these contradictory objectives. A decision-making criterion based on statistics was used to decide on the optimal location and size of the parking garage. Sensitivity analysis to show the effect of the different objectives on the selection of the optimal size and location was also performed. [ABSTRACT FROM PUBLISHER]

Inductive power transfer (IPT) technology has been approved to be convenient and reliable interface for charging and discharging the electric vehicles (EVs). Precise model for such system can help designers and researchers to anticipate, optimize, and evaluate its behavior amid the development. Thus, this paper presents a physics-based co-simulation platform for the bidirectional IPT system (BIPTS) in EVs’ applications. The platform is established through the coupling between finite element and circuit analysis. The power electronic converters and controllers are developed in Simulink; and the power pads are modeled in Magnet environment. The two parts are lined together through the compatible Simulink plug-in tool. In addition, a state-space dynamic mathematical model for the same BIPTS is derived and implemented in MATLAB environment. A 1.2 kW BIPTS is analyzed under different dynamics by both models and the results are compared. The effect of the nonlinearities and the magnetic material characteristics on the BIPTS's performance is assessed, in terms of errors and harmonics analysis. The analysis considered both the full and light loading operating conditions in the system. Finally, a small-scale prototype for a BIPTS is built, tested, and compared with the co-simulation results for verification purposes. The proposed co-simulation could provide accurate prediction for the system's dynamics, during both charging and discharging operation. The scheme is generic and can be easily expanded to different pad structures, compensation networks, and converter topologies. [ABSTRACT FROM PUBLISHER]

Electric vehicle (EV) can act as an energy consuming device or a distributed energy resource in the concepts known as vehicle-to-grid (V2G) and grid-to-vehicle (G2V). The bidirectional wireless power interface is a successful choice for this application, since it provides automatic, reliable, and safe operation. Therefore, this paper presents modeling and assessment analysis for the steady-state performance of bidirectional inductive wireless power transfer system (BIWPTS) in EV implementations. The analysis is presented for the three main compensation configurations: LC-series, LC-parallel, and LCL-topology. Moreover, the steady-state equivalent circuit-based mathematical models for all topologies are developed. These models were used to precisely determine the system response during V2G and G2V operations based on Fourier series. The study presents evident criteria to pick among the different BIWPTS structures in the diverse applications. The proposed analyses were verified both in simulation and experiment. The results demonstrate the ability of the proposed models to provide accurate estimation for BIWPTS performance under various operating and control conditions. Also, the evaluation analysis shows that LCL-topology is more appropriate for the bidirectional operation due to the simple design and control requirements, and being less sensitive to the misalignment. [ABSTRACT FROM AUTHOR]

A flexible, automatic, reliable, and safe charging and discharging system for electric vehicles (EVs) and hybrid EVs is crucial in vehicle-to-grid (V2G) operation. The bidirectional inductive wireless power transfer system (BIWPTS) is an ideal solution in this situation. With authentic power-flow models for the BIWPTS in hand, designers and operators can predict, optimize, and analyze the interaction performance between the EV and the grid. Thus, this paper presents a harmonics-based theoretical power-flow model for BIWPTS for charging and discharging EVs in V2G applications. The proposed model provides accurate estimation for the active and reactive power flow during V2G and grid-to-vehicle modes. Moreover, the impact of the circuit losses and harmonics, due to the high-frequency power inverters, on the power-flow performance is investigated. This effect is stated mathematically by four novel power-flow criteria formulas. New conditions for achieving maximum active and reactive power, and unity and zero power-factor system operation, are also developed. The sensitivity of the circuit performance to the variation of its parameters has been studied. For validation purposes, a computer-based model and experimental platform of BIWPTS for EV applications are developed and tested. The comparison between the theoretical, simulation, and experimental results verified the proposed models and analysis. [ABSTRACT FROM PUBLISHER]

A good magnetic design of a wireless electric vehicle (EV) charging system requires accurate characterization of the equivalent circuit parameters. This is while considering the effect of the magnetic core and the conductive shielding layers. In the literature, such an analysis is done using 3-D finite element analysis, which makes the design and optimization process time-consuming and computationally expensive. In this paper, an integrated analytical model for the characterization of a circular wireless power transfer EV pads is utilized. This analytical model provides a valuable tool to optimize the design process. This model is utilized when evaluating the objective function for each individual in the multiobjective Pareto Optimality optimization algorithm. The objective is optimizing the pads with three decision variables and four constraints. [ABSTRACT FROM AUTHOR]

This paper proposes an intelligent workplace parking garage for plug-in hybrid electric vehicles (PHEVs). The system involves a developed smart power charging controller, a 75-kW photovoltaic (PV) panel, a dc distribution bus, and an ac utility grid. Stochastic models of the power that is demanded by PHEVs in the parking garage and the output power of the PV panel are presented. In order to limit the impact of the PHEVs' charging on the utility ac grid, a fuzzy logic power-flow controller was designed. Based on their power requirements, the PHEVs were classified into five charging priorities with different rates according to the developed controller. The charging rates depend on the predicted PV output power, the power demand by the PHEVs, and the price of the energy from the utility grid. The developed system can dramatically limit the impact of PHEVs on the utility grid and reduce the charging cost. The system structure and the developed PHEV smart charging algorithm are described. Moreover, a comparison between the impact of the charging process of the PHEVs on the grid with and without the developed smart charging technique is presented and analyzed. [ABSTRACT FROM AUTHOR]

Energy management and control of hybrid microgrids is a challenging task due to the varying nature of operation between AC and DC components which leads to voltage and frequency issues. This work utilizes a metaheuristic-based vector-decoupled algorithm to balance the control and operation of hybrid microgrids in the presence of stochastic renewable energy sources and electric vehicles charging structure. The AC and DC parts of the microgrid are coupled via a bidirectional interlinking converter, with the AC side connected to a synchronous generator and portable AC loads, while the DC side is connected to a photovoltaic system and an electric vehicle charging system. To properly ensure safe and efficient exchange of power within allowable voltage and frequency levels, the vector-decoupled control parameters of the bidirectional converter are tuned via hybridization of particle swarm optimization and artificial physics optimization. The proposed control algorithm ensures the stability of both voltage and frequency levels during the severe condition of islanding operation and high pulsed demands conditions as well as the variability of renewable source production. The proposed methodology is verified in a state-of-the-art hardware-in-the-loop testbed. The results show robustness and effectiveness of the proposed algorithm in managing the real and reactive power exchange between the AC and DC parts of the microgrid within safe and acceptable voltage and frequency levels. [ABSTRACT FROM AUTHOR]

• Proposing 2 layer optimization stackelberg incorporating timely decision for control scheme. • Developing MIQP considering variable for realistic modeling of energy market operation. • To the best of knowledge, previous literature considered either assumed or Time-of-Use (ToU) rates. • Such assumptions may constitute a serious flaw as impact of sudden large EVs population. • Integrating practical variables to model the dynamic and realistic operation of the energy grid. The stochastic nature of electric vehicles (EVs) has made predicting and ultimately controlling their integration on a large scale a very challenging task. This work proposes a two-layer optimization framework based on the Stackelberg leader and follower to manage a tri-level energy management strategy to coordinate EVs charging. The first layer incorporates an aggregator that collects the energy requirements of all EVs in a decentralized manner, and sends them to an attached microgrid, which constitutes the second layer of the proposed scheme, for further processing. Then, the microgrid runs a lower-level energy optimization problem in a centralized manner based on the inputs from its aggregators downstream and a system operator upstream, which operates in the third level. Simultaneously, the coordination between the system operator and its attached microgrids is formulated as an upper-level energy optimization problem. The work incorporates the dynamics of the energy system by modeling practical economic, technical, and operational variables. The formulated problem is solved via mixed-integer quadratic programming (MIQP). The results show that the proposed strategy has successfully influenced the charging requirements of EVs due to the dynamic energy price signals issued following the system's timely operation. In addition, the results demonstrated an optimal energy exchange to support optimal operation and reduce overall costs. [ABSTRACT FROM AUTHOR]

Electric Vehicles (EVs) and hybrid Electric vehicles (HEVs) are going to reshape the future of the transportation sector. However, adopting large numbers of EVs and HEVs will impact the electric utilities as well. Managing the charging/discharging of substantial numbers of distributed batteries will be critical for the successful adoption of EVs and HEVs. Therefore, this paper presents a review study about the recent control and optimization strategies for managing the charging/discharging of EVs. The paper covers different control and operation strategies reported in the literature as well as issues related to the real time dispatching of EVs in the smart grids. In addition, challenges related to the stochastic nature of the driving characteristics of EVs are considered. Finally, some open problems related to the energy management of EVs will be presented. [ABSTRACT FROM AUTHOR]

The wide deployment of grid connected large scale photovoltaic (PV) systems and the rapid growth in the electric vehicle (EV) market opens the avenue for the PV based EV stations to participate in the energy as well as the EV arbitrage markets. This participation should comply with the utility restrictions with respect to power oscillations at the point of common coupling (PCC). Consequently, there is a need for battery energy storage system (BESS) to fulfill this task. This paper proposes a multi-objective optimization (MOO) methodology that aims at maximizing the revenues of the PV based EV (PV-EV) station while minimizing the BESS capacity fading simultaneously. The objectives are to be met under the power fluctuations restriction imposed by the hosting grid at the point of common coupling (PCC). The proposed methodology provides detailed modelling of the dynamics of the BESS as well as the EV parking garage behavior. The MOO problem is solved by the augmented ε-constrained (AUGMECON2). The results showed the effectiveness of the proposed methodology in achieving the aforementioned objectives. Furthermore, sensitivity analysis was performed to show the effect of neglecting one of the objectives on the other, and the effect of the power fluctuations limit constraint on the objectives of concern in this study. Moreover, the results showed the adverse effect of neglecting the battery’s detailed modelling on the battery’s lifetime. Additionally, the effect of uncertainties in the BESS’s internal parameters coefficients of both objectives was investigated. [ABSTRACT FROM AUTHOR]

Integrating a fleet of electric vehicles (EVs) in the distribution system without a communication infrastructure exhibits several issues that need to be handled by electric utilities. Robust autonomous controllers are crucial in this case to manage the charging operation without violating the grid standard limits. This paper proposes a communication-free autonomous charging controller for EVs in distribution systems. The controller is based on fuzzy logic considering both the system voltage profile and the EV’s battery state of charge (SOC). The fuzzy inference system is designed to avoid under voltage issues in the grid, flatten the system loading profile and accomplish fair charging among different EVs connected to the system. An 18-bus distribution system driven by the proposed controller was implemented in a simulation environment. The proposed controller performance was investigated under different distribution system operating conditions such as different loading, different levels of EV penetration, system reconfiguration as well as with distributed generation. In all cases, the controller demonstrated faster and better charging performance without violating the standard voltage limits compared to other research reported in the literature. [ABSTRACT FROM AUTHOR]

• An energy consumption minimization framework for electric vehicles (EVs) routing optimization is proposed. • The concept of DDQN as a learning tool to find the maximum travel policy of the EV as an agent is presented. • Dynamic traffic model based on Markov chain is used, with real-time data import from Google's API platform. • The proposed framework yields lower energy requirement for EVs to reach their destinations compared with Google's map original routes. A real-time, data-driven electric vehicle (EVs) routing optimization to achieve energy consumption minimization is proposed in this work. The proposed framework utilizes the concept of Double Deep Q-learning Network (DDQN) in learning the maximum travel policy of the EV as an agent. The policy model is trained to estimate the agent's optimal action per the obtained reward signals and Q-values, representing the feasible routing options. The agent's energy requirement on the road is assessed following Markov Chain Model (MCM), with Markov's unit step represented as the average energy consumption that takes into consideration the different driving patterns, agent's surrounding environment, road conditions, and applicable restrictions. The framework offers a better exploration strategy, continuous learning ability, and the adoption of individual routing preferences. A real-time simulation in the python environment that considered real-life driving data from Google's API platform is performed. Results obtained for two geographically different drives show that the proposed energy consumption minimization framework reduced the energy utilization of the EVs to reach its intended destination by 5.89% and 11.82%, compared with Google's proposed routes originally. Both drives started at 4.30 PM on April 25th, 2019, in Los Angeles, California, and Miami, Florida, to reach EV's charging stations that are located six miles away from both of the starting locations. [ABSTRACT FROM AUTHOR]

The modern revolutionary changes in power delivery systems with the advent of smart and flexible grids require systems interoperability, seamless integration of technologies and functionalities. This scenario is paving the way to new prospective of energy clusters that call for new methodologies for the dynamic energy management of distributed energy resources. This paper proposes a new management scheme of energy clusters (e.g., smart building, energy community or virtual power plant) that is based on a data driven decision-maker that daily deploys the activities of the day ahead, providing an optimized scheduling which will be the base for the operations for the next day. The new aggregator relies on some innovative features. It leverages an optimization process based on the elastic net regularization that proves to be an effective support for finding the best scheduling of the distributed resources according to specific key performance indicators, that presently is the unbalance. The decision process works on predicated data obtained through a recently assessed short term forecasting based on long short-term memory neural networks properly adapted to distributed environments. The method allows to extract insight from data and run what-if scenarios to define the best scheduling of resources. The technique uses a set of feasible rules to optimally aggregate the distributed resources and demand-side management programs. We tested the proposed aggregator on a real energy cluster making use of real measured data over two years period. The results show the effectiveness of the proposed approach that is able to predict any sort of unit and manage any sort of program. The significance of this work is to approach the energy management as an optimization and decision problem in a more robust way of reasoning, by employing forecasting/optimization over all the quantities in the community, resulting in a thoroughly complex and efficient method. • Innovative and comprehensive decision-making scheme for the energy management of real ECs. • Day-ahead data provided by data driven learning to optimize the scheduling for next day operations. • Optimization process based on elastic net regularization according to key performance indicators. • Decision process associated with forecasting data obtained by using LSTM networks. • Aggregator tested on a real EC making use of measured data. [ABSTRACT FROM AUTHOR]