Energy Management and Control Techniques for Electric Vehicle

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This entry is adapted from the peer-reviewed paper 10.3390/app13158919

Electric vehicles (EVs) are universally recognized as an incredibly effective method of lowering gas emissions and dependence on oil for transportation. Electricity, rather than more traditional fuels like gasoline or diesel, is used as the main source of energy to recharge the batteries in EVs. Future oil demand should decline as a result of the predicted rise in the number of EVs on the road. The charging infrastructure is considered as a key element of EV technology where the recent research is mostly focused. A strong charging infrastructure that serves both urban and rural areas, especially those with an unstable or nonexistent electrical supply, is essential in promoting the global adoption of EVs.

electric vehicle charging infrastructure Energy management Control techniques EVs charging infrastructure

1. Introduction

The government, industry, and academia are actively promoting the development of an electric vehicle (EV)-based transportation system. This system utilizes renewable energy or the electric grid for charging purposes. The primary objective is to address the increasing environmental concerns associated with daily transportation, which currently contributes to 25–30% of India’s greenhouse gas emissions. By adopting EVs, there is a significant reduction in the consumption of fossil fuels. Worldwide governments are actively promoting the EV industry by implementing subsidies and legislation. This is in response to consumer demand for low-emission transportation options as a viable alternative to conventional fossil-fuel-powered vehicles. The motivation behind this shift is the recognition that fossil-fuel-based transportation poses significant risks to the environment and the planet. EVs offer several positive societal consequences, including enhanced safety, improved public health, a thriving domestic economy, and a cleaner and safer environment. Many nations are transitioning to renewable energy sources due to the advantages they offer in terms of both environmental sustainability and economic viability. Fossil fuels, due to their significant contribution to carbon dioxide (CO₂) emissions, present a substantial risk to the Earth’s ecosystem. Figure 1 depicts the proportional distribution of CO₂ emissions contributed by different sectors, as classified by the International Energy Agency (IEA). The sectors encompassed are the electricity and heat sector, the transportation sector, the industry sector, the residential sector, and various other areas ^[1]^[2]^[3]^[4].

Figure 1. The percentage contribution of CO₂ emissions by different sector.

Currently, every nation is diligently employing various measures to safeguard the environment. The presence of a clean environment is crucial for the existence of human life, rendering it inconceivable to envision a world devoid of such conditions. In addition, it is important to note that the availability of fossil fuels is finite and gradually depleting daily. Currently, there is a global focus on EVs to address various significant concerns. EVs are widely recognized as a highly efficient mode of transportation owing to their ability to produce zero residual emissions. Due to several advantage of EVs, it is projected that, by the year 2030, the number of EVs on the road will exceed 100 million, driven by an increasing awareness among individuals regarding the benefits associated with these vehicles ^[5]. The EV industry in Asia is projected to experience significant growth as multiple countries implement measures to promote the adoption of EVs, reduce emissions, and attract investments in EV manufacturing facilities. By the year 2022, it is projected that the number of non-electric vehicles in operation on Indian roads will reach 278,169,631 while the number of EVs will be 1,334,385. The information was acquired from the e-Vahan portal, which is managed by the Ministry of Road Transport and Highways. Figure 2 presented below offers comprehensive data regarding the sales figures of EVs in India and worldwide ^[6]^[7].

Figure 2. EV sales scenario in (a) Indian market and (b) World market.

To successfully achieve the future-oriented goal of EV sales, a significant charging infrastructure and significant electric energy generation must be developed. Additionally, the electricity required for EV charging is produced exclusively from clean and renewable energy sources. Because generating electricity from burning fossil fuels just changes the source of emissions from cars to power plants, it does not result in a reduction in emissions. Hence, the mitigation of pollution and the subsequent environmental benefits can be achieved through the utilization of renewable energy sources for electricity generation. Solar photovoltaic (PV) generation, along with other renewable energy sources like wind energy, hydro energy, and fuel-cell-based energy, is considered the most viable option for EV charging due to its widespread availability in both rural and urban areas ^[8]^[9]^[10]. The availability of the product is extended throughout a significant duration of the year on the Indian subcontinent. Wind energy and hydro energy, unlike solar PV arrays, rely on geographical factors. Wind power is typically most suitable for coastal regions, whereas hydro power is more practical for inland areas. Additionally, solar power is readily available on the Indian subcontinent for a significant portion of the year. The development of a charging infrastructure is crucial in facilitating the accessibility of EVs for individuals. An efficient and proper charging infrastructure is essential for realizing the full potential of EVs ^[11]^[12].

Electric vehicles consist of various emerging manufacturing technologies, including an electric motor, a battery, and a charging facility. As a result, they have become an essential component of modern transportation systems. Nevertheless, the transition to EVs is not advancing at the anticipated pace. The limited driving range and extended charging durations of EVs are widely recognized as the primary barriers hindering their widespread adoption ^[13]. EVs typically have a higher initial cost compared to their gasoline-powered counterparts. However, they offer the advantage of requiring significantly less energy for operation and producing lower emissions. The significant increase in demand for EVs and the corresponding rise in EV charging infrastructures have led to extensive discussions among research institutions and energy supply companies on how to mitigate the strain on local electrical networks. This is due to the expanding number of EV charging stations ^[14]^[15]. EV charging stations can potentially leverage renewable energy sources such as wind and solar power to assist local electricity networks in compensating for any deficiencies. Several key global factors are contributing to the swift growth of EVs. The progress in electric motor and electronic control system technologies has led to the direct control of EV propulsion. Additionally, there have been notable advancements in supporting technologies for EVs, including grid-to-vehicle (G2V) and vehicle-to-grid (V2G) systems ^[16].

An increasing public awareness and commitment to addressing climate change have been observed ^[17]. Commercial vehicle electrification is a crucial area of research due to its potential to significantly reduce CO₂ emissions ^[18]^[19]. The primary focus of commercial vehicle research in the field of electrification has been on hybridization. This is primarily due to considerations such as battery capacity, EV range, and the limited availability of public charging infrastructure ^[20]^[21]. Light-duty trucks (LDTs) have demonstrated a successful conversion without significant alterations to travel behavior ^[22]. As a result, they have become the primary focus for the initial implementation of zero-emission commercial electric vehicles (CEVs), such as electric trucks (ETs). Advancements in lithium battery technology ^[23] have facilitated the technical and economic feasibility of electric trucks when compared to diesel and alternative-fuel trucks ^[24]. In their study, Chaudhari et al. ^[25] introduced a hybrid optimization model that aims to effectively manage battery storage in order to maximize the utilization of power generated by a solar PV array, while simultaneously minimizing the operational expenses associated with the control system. In their study, Kandasamy et al. ^[26] investigated the underlying factors contributing to the premature failure of a storage battery within a solar photovoltaic (PV) array system installed in a commercial building. The wind-energy-driven charging station (CS) is advantageous for EVs due to its availability both during the day and at night. A plethora of papers pertaining to this field can be found ^[27]^[28]^[29]. The infrastructure and control mechanism for PV, wind, and fuel-cell-based EV charging are detailed in Reference ^[30]. In their study, Ugirumurera and Haas ^[31] discussed the significance of renewable energy in ensuring the sustainable future of EV charging stations. In their study, Chandra Mouli et al. ^[32] successfully charged solar-powered vehicles using a high-powered bidirectional EV charger. The charger in question does not support AC charging. In their study, Monteiro et al. ^[33] incorporated a three-port converter for the purpose of establishing a connection between a photovoltaic array and an EV charger. The design of the charger did not take into account the distortions in the grid current. Singh et al. ^[34] have introduced a modified z-source converter that can be utilized in the development of a PV array/grid-connected EV charger. However, the charger was not designed to operate in island mode. Due to its limitations, the system lacks the capability to facilitate EV charging in off-grid locations. The PV-array-based charging station was introduced by Singh et al. ^[35] to offer various functionalities in vehicle-to-grid (V2G) systems. These functionalities include the provision of a charging facility, reactive/active power support, active power filtering, and the enabling of vehicle-to-home operation. In their study, Saxena et al. ^[36] constructed a grid-tied photovoltaic (PV) array system designed specifically for residential applications and integration with EVs. In their study, Razmi and Doagou-Mojarrad ^[37] introduced a power management technique for a residential integrated PV-storage battery system. This technique allows for both grid-connected and off-grid operation, and incorporates multimode control. The concept of integrating a smart house system with an EV as a storage device has been proposed. This system enables vehicle-to-home and vehicle-to-gadget (V2G) operations, providing advantages for both utility companies and consumers ^[38]^[39]^[40]. Renewable-energy-based charging stations are considered the most viable solution for the charging of EVs. However, their integration into the existing charging system necessitates an additional power conversion stage. This, in turn, leads to an increase in system complexity and power loss. Furthermore, it is imperative to integrate the current control system with the controllers dedicated to each stage of the conversion process. Hence, the establishment of a cohesive control and co-ordination mechanism is imperative for facilitating the development of a versatile and multifaceted operational integrated system ^[41]. The proliferation of EVs has given rise to various challenges within the domains of energy, transportation, and manufacturing. The development of charging platforms and infrastructure is necessary to facilitate the charging of EVs, both in residential settings and public locations. With the growing prevalence of EVs in circulation, there arises a pressing requirement to allocate resources towards the development of an intelligent grid infrastructure. The significant dimensions of EV batteries result in substantial power consumption during the charging process ^[42]. The existing body of literature provides various recommendations for mitigating the impact of EV charging on the distribution system. There are two primary categories of mitigation methods. The initial approach involves the implementation of time-of-use (TOU) pricing by utilities to discreetly manage EV charging ^[43]^[44]. This approach incentivizes EV owners to charge their vehicles during non-peak hours by reducing electricity rates during off-peak periods in a time-of-use pricing framework ^[45]. The utilization of this technique results in a significant reduction in peak load demand, thereby mitigating concerns related to transformer overloading and heating. The second strategy involves the utilization of smart charging algorithms by utilities to actively control the rates and start times of EV charging ^[46]. The advantages of an EV charging system encompass the optimization of customer benefits and enhancement of utility benefits through the strategic scheduling of EV charging during off-peak-load hours. These are some of the articles whose contributions are highlighted here, but there are many more in which numerous experts in the field of EVs have produced high-caliber work. Currently, the field of research pertaining to EVs and their associated charging infrastructure is experiencing significant growth and development. Even so, EVs will encounter several difficulties, which have been explained further.

2. Energy Management and Control Techniques for EV Systems

By utilizing a variety of power sources and storage facilities, it is possible to build a system that can charge electric vehicles (EVs) while mitigating the consequences of the intermittent nature of the renewable energy supply. Therefore, suitable hybrid power system control and energy management strategies are crucial to enhance the charging system’s stability, dependability, and load scheduling. Planning an EV charging station in this location presents several obstacles. A wide range of EV chargers are compatible with AC and DC charging stations. At EV charging stations, it is crucial but challenging to manage the infrastructure and electricity ^[47]^[48]. Whether it is grid-connected or standalone, the charging station is built to operate dependably as a microgrid in a range of situations (in the case of a grid outage). By optimizing EMS performance during system development, people can cut costs across the board for both systems.

2.1. Energy Management Strategies for EV Charging Systems

Energy management strategies (EMSs) have a crucial role in multi-energy system applications as they are responsible for controlling the power delivery to powertrains. This control directly impacts the performance, efficiency, and longevity of the vehicle. The primary objective of energy management techniques in EV charging systems is to optimize the utilization of available energy resources, facilitate efficient charging operations, and facilitate the seamless integration with the power grid ^[49]. The following section presents a compilation of prevalent energy management techniques employed in EV charging systems:

Demand Response (DR): Demand response systems enable the synchronized charging of EVs in accordance with the requirements of the electrical grid and the amount of electricity being utilized. Charging can be effectively managed through various methods, such as implementing scheduled charges during off-peak hours or adjusting charging patterns based on signals received from the grid operator. Utilizing this approach during non-peak periods enables users to leverage reduced electricity expenses while simultaneously contributing to grid stability.

By 2050, the IEA estimates that demand response strategies will have successfully shifted as much as 15% of the annual average power demand. The term “demand response” refers to a broad category of measures taken to lower electricity demand (peak demand) and prevent a blackout. Utility providers and industrial and household customers alike will need to take part.

❖: Smart meters and smart grids can help utilities track consumption and identify peak demand.
❖: Consumers can reduce peak demand by turning off lights, air conditioning, and other superfluous electrical products and machinery.

When compared to building more generation capacity to handle demand spikes, demand response is a more cost-effective option. The International Energy Agency (IEA) suggests implementing new business models and setting standards for the degree of controllability of equipment and appliances in order to lower demand.

Time-of-Use (TOU) Pricing: The time-of-use (TOU) pricing model provides different electricity costs at different times of the day. Customers are advised to utilize charging stations for their EVs during periods of the day characterized by reduced electricity demand. This is because charging stations can take advantage of lower rates offered during off-peak hours. The proposed plan aims to mitigate the peak load on the system and enhance energy efficiency simultaneously.

Vehicle-to-Grid (V2G) Integration: Vehicle-to-grid (V2G) technology facilitates the exchange of power in both directions between EVs and the power grid. Consequently, the energy that is stored within the batteries of EVs can be harnessed for the purpose of supplying power to adjacent residential and commercial establishments. The electrical energy stored in the battery of an EV can be utilized for non-vehicular applications, such as providing power to residential or commercial buildings, or even feeding it back into the grid during periods of high demand. In both scenarios, this facilitates grid maintenance and promotes the integration of renewable energy sources.

Smart Charging Algorithms: Smart charging algorithms are designed to optimize the charging process by considering various factors. These factors include the cost of electricity, the demand on the grid, and the preferences of the user. To achieve energy optimization, cost reduction, and grid stability preservation, the algorithms have the potential to modify the charge rate, introduce charging delays, or prioritize charging for vehicles.

Grid Integration and Load Management: The integration of EV charging infrastructure with grid management systems enables the monitoring and control of the charging demand for EVs. Load management techniques are employed to ensure the even distribution of the charging load among the available charging stations while adhering to the capacity limitations of the grid. This is crucial for maintaining optimal charging efficiency and preventing the overloading of the grid.

Renewable Energy Integration: The compatibility of EV charging infrastructure with various renewable energy sources, such as solar or wind power, can be achieved. The technologies mentioned above have the capability to effectively prioritize the charging process during periods characterized by significant levels of generation from renewable energy sources. By optimizing the utilization of renewable energy sources, people can effectively decrease our dependence on the power grid. This approach enables us to maximize the benefits of renewable energy while minimizing our reliance on conventional energy sources.

Energy Storage Integration: The utilization of energy storage devices in EV charging enhances the adaptability and stability of the grid. Energy storage systems hold great potential in their capacity to store excess renewable energy or grid electricity during periods of low demand. This stored energy can then be discharged during times of peak demand or utilized for charging the EVs. By implementing enhanced control over the electrical current, this feature is expected to alleviate the strain on the system and enhance overall efficiency.

Predictive and Adaptive Charging: Predictive and adaptive charging methods leverage sophisticated algorithms and data analysis techniques to enhance charging schedules and dynamically adapt to fluctuating environmental conditions. These methods aim to estimate the required energy consumption accurately and respond promptly in real time. The tactics employed consider various factors, including users’ preferences, traffic patterns, and grid conditions, in order to ensure effective energy management and billing.

The implementation of energy management techniques facilitates the optimization of energy resource utilization and reduction of grid impact, and enables the seamless integration of EV charging with renewable energy sources and grid infrastructure. Although there are multiple papers available that discuss the different energy management systems (EMSs) used in EVs, the research literature in this area is still relatively new. The significance of EMS technology is underscored by EVs and the diverse charging infrastructures they require. Table 1 presents an analysis of energy management techniques that have been developed and implemented by different researchers.

Table 1. Summary of energy management strategies implemented for EV charging systems.

Ref. No.	Charging System Architecture	Energy Source	Control Techniques	Advantage	Disadvantage	Review and Comments
^[50]	PEV charging with smart grid	Solar, wind, and grid	Model predictive control	Rapid dynamic responsiveness and mode switching.	Algorithm for converting linear models has limitations.	Creates a model predictive-control-based strategy for managing power and charging schedules for plug-in EVs in tandem to cut down on energy costs associated with charging and satisfying residential and vehicular power needs.
^[51]	Scheduled charging	Grid and battery	Frequency droop control	Stabilizes power system demand, supply, and frequency. Fault-tolerant, versatile, and low-maintenance.	Its model contains load disruptions, instability, and non-linearity. PID controllers may quickly stabilize load disturbances.	The suggested V2G control can meet varied charging demands including holding and raising battery energy levels, unlike existing approaches that require multiple V2G control strategies. Proposed methods guarantee EV charging with frequency regulating.
^[52]	Microgrid-based off-board charger	Solar, wind, battery, and diesel generator	Decentralized adaptive control	The suggested adaptive control strategy benefits both EVs and microgrids. Provides better SoC and reduces charging time.	More dependency on parameters, and more challenges in terms of scalability.	This research presents a unique decentralized adaptive control technique to govern EV contributions to primary frequency regulation in an islanded microgrid. The framework adjusts the droop parameter for microgrid and EV issues. The EV charger monitors frequency and adapts its contribution to load-generation balance changes.
^[53]	Hybrid-microgrid-based EV charging station	PV diesel generators and grid	Virtual synchronous machine control	Virtual inertia improves system stability and allows flexible control with many variables. Communication is unnecessary.	Complex controller implementation and parameter sensitivity cause non-linearity in its state space model.	The virtual synchronous generator (VSG) technique employing a CS to create inertia uses a fleet of EVs parked in the CS as energy storage for MG. The proposed strategy will be an effective answer for maintaining the regularity of an isolated MG.
^[54]	PEV charging with grid	Flywheel and grid	Droop-based hysteresis control	Optimizes the dynamic performance by controlling the peak-to-peak value of the current ripple.	Fluctuating frequency; delayed response in voltage fluctuation condition.	A hysteresis-type active power support approach from an FCS with the FESS was theoretically and empirically validated in this paper. The grid and FESS converters are not digitally connected while using droop-based DBS control. The approach effectively responds to system-level DSO signals without interrupting PEV battery-charging schedules.
^[55]	Two charging stations	PV, battery, and grid	Decentralized fuzzy logic control	Presents a robust response approach for addressing non-linear uncertainty in parameter variable systems.	Possessing a high level of expertise sensitivity.	This author proposes an MVDC bus-based DCM for charging stations (CSs). The key contribution is a novel decentralized control using fuzzy logic controllers as a decentralized EMS to manage the converters of two system components separately and co-ordinate power flow, MVDC voltage, and BESS SOC performance.
^[56]	Three 60 KW charging stations	PV, battery, and grid	Droop control techniques	Increases stability and power sharing	Unbalanced distributed generation impedance reduces load-sharing accuracy.	This work provides better decentralized virtual-battery-based droop control with bus voltage maintenance, load power dispatch, and energy storage system (ESS) SOC balance for autonomous and stable DC microgrid operation. The PV–ESS–grid integrated system’s core bus-signalling control switches PV array and grid control modes based on the ESS’s virtual OCV.
^[57]	EV charging station	PV and grid	Multi-agent-based decentralized scheduling algorithm	Controls a vast area and can boost grid resiliency and meet grid requirements in real time.	Requires two-way communication between agents and utilities and significant EV user authorization.	This paper offers a decentralized scheduling framework for charging EVs based on MAS, the charging control model. The MAS has “responsive” or “unresponsive” EV agents as well as an EV/DG aggregator agent. Based on forecasts of power consumption and generation, the EV/DG aggregator agent creates the virtual pricing strategy to maximize profit.
^[58]	DC-microgrid-based EV charger	PV and battery	Droop and master–slave control strategy	The system stability is enhanced when compared to using only a conventional master control or conventional droop control scheme.	More dependency on solar energy; constant DC bus voltage maintaining is challenging task.	This work proposes an EVCS combination control method that combines the benefits of droop and master control strategies. An isolated bidirectional DC–DC converter, snubber circuits, and a three-level boost converter with capacitance-voltage control further improve system stability.

2.2. Control Techniques for EV Charging Systems

The widespread adoption of EVs has the potential to raise load demand, boost system losses, and lower grid voltage. Overloading service transformers, reducing their lifespan, and increasing system losses are all possible outcomes of the increased load demand caused by EV loads. The charging of EVs causes new load peaks that may exceed the service transformer’s rated capacity, hastening the aging process. The daily expansion and contraction of the transformer can be mitigated if EVs are largely charged during off-peak hours, which is good for the transformer’s life ^[59].

In addition to this, the increased adoption of EVs in our daily lives will give rise to numerous challenges. In order to address the issues, it is imperative to implement effective control techniques throughout the entire process, starting from the grid and extending to the vehicles. In an EV system, various control techniques are employed to manage different aspects of the vehicle’s operation ^[60].

Motor Control: EVs utilize electric motors for propulsion. Motor control techniques include:

❖: Field-Oriented Control (FOC): FOC is a technique that accurately controls the torque and speed of the motor by decoupling the torque and flux components. It maximizes motor efficiency and performance.
❖: Direct Torque Control (DTC): DTC is a control method that directly controls the torque and flux of the motor without needing to decouple them. It provides fast and precise control response.
❖: Pulse-Width Modulation (PWM): PWM is used to control the motor drive by adjusting the duty cycle of the voltage pulses applied to the motor. It regulates the motor’s speed and torque output.

Battery Management System (BMS) Control: The importance of the battery management system (BMS) in ensuring the safety and protection of an EV cannot be overstated. The BMS is responsible for overseeing the operation of the rechargeable battery pack or individual cells, thereby exerting control over the associated electronics. By implementing this mechanism, the battery is protected from overcharging, which ensures the user’s safety from potential electrocution. The BMS utilizes various control methods, which are as follows:

❖: State-of-Charge (SOC) Estimation: SOC estimation techniques are utilized to determine the remaining energy in a battery pack by considering various factors such as voltage, current, temperature, and additional parameters. The provided information is essential for the optimization of battery usage.
❖: State-of-Health (SOH) Estimation: The estimation techniques for the state of health (SOH) evaluate the condition and deterioration of the battery pack. The measurement assists in determining the remaining capacity of the battery and its power delivery capability.
❖: Cell Balancing: Cell-balancing techniques are implemented to ensure uniform charging and discharging of each individual battery cell within a pack. The prevention of cell voltage imbalances is crucial in order to maintain optimal battery performance and prolong its lifespan.

Thermal Management Control: The thermal management system in an EV is responsible for keeping the battery packs, power electronics drives, and electric motors at their optimal working temperatures regardless of external or internal temperature fluctuations. Thermal management plays a crucial role in the safety and usability of EVs. Thermal management control strategies include:

❖: Cooling System Control: The cooling system is responsible for controlling and maintaining the temperature of the battery pack, motor, and power electronics. The control algorithms are responsible for regulating fan speeds, coolant flow rates, and various other parameters in order to ensure that the temperatures are maintained at the appropriate levels.
❖: Heating System Control: EVs necessitate the implementation of heating systems in regions with cold climates to ensure the warming of the battery pack, cabin, and other essential components. Control methods are employed to regulate the heating system in order to maintain comfortable temperatures while minimizing energy consumption.

Regenerative Braking Control: Regenerative braking is a feature found in the majority of hybrid and all-electric vehicles. The system converts the kinetic energy generated during braking into electrical power, which is then used to charge the high voltage battery installed in the vehicle. The control strategies are responsible for regulating the braking force and ensuring a balance between mechanical braking and regenerating energy in order to achieve the highest-possible energy recovery.

Charging Station Control: A charging station, also known as an EV charging facility, is a dedicated location where electric cars can conveniently access and receive electrical power. A standard EV charging station requires a minimum of one smart controller board and one power socket board. The power socket board is responsible for the distribution and measurement of energy, whereas the smart controller manages the security, services, and connectivity to remote servers. EV charging stations are required to adhere to rigorous standards in order to ensure optimal performance, accuracy, connectivity, and safeties; charging stations employ various control methods to manage charging sessions and establish communication with the grid:

❖: Communication Protocols: Charging stations utilize communication protocols such as OCPP to facilitate interaction with the grid and enable control over charging sessions. The utilization of this technology enables the incorporation of functionalities such as billing, load management, and authentication.
❖: Power and Load Management: Charging stations equipped with advanced technology facilitate load balancing and power management in order to mitigate the risk of system overloading. Real-time adjustments can be made to the charging rates, considering the operational status of the grid and the preferences of individual users.
❖: Smart Charging: Smart charging techniques consider various factors such as energy pricing, the availability of renewable energy, and grid demand in order to optimize charging sessions for both cost-effectiveness and grid stability.

Control systems play a vital role in ensuring the secure and efficient functioning of EVs and their charging infrastructure. They are instrumental in optimizing the performance, range, and overall reliability of EVs. The analysis presented in the Table 2 provides an overview of the control techniques employed by different users for an EV charging infrastructure.

Table 2. Analysis of control techniques used for EV charging systems.

Ref. No.	Charging System Architecture	Energy Source	Control Techniques	Advantage	Disadvantage	Review and Comments
^[50]	PEV charging with smart grid	Solar, wind, and grid	Model predictive control	Rapid dynamic responsiveness and mode switching.	Algorithm for converting linear models has limitations.	Creates a model predictive-control-based strategy for managing power and charging schedules for plug-in EVs in tandem to cut down on energy costs associated with charging and satisfying residential and vehicular power needs.
^[51]	Scheduled charging	Grid and battery	Frequency droop control	Stabilizes power system demand, supply, and frequency. Fault-tolerant, versatile, and low-maintenance.	Its model contains load disruptions, instability, and non-linearity. PID controllers may quickly stabilize load disturbances.	The suggested V2G control can meet varied charging demands including holding and raising battery energy levels, unlike existing approaches that require multiple V2G control strategies. Proposed methods guarantee EV charging with frequency regulating.
^[52]	Microgrid-based off-board charger	Solar, wind, battery, and diesel generator	Decentralized adaptive control	The suggested adaptive control strategy benefits both EVs and microgrids. Provides better SoC and reduces charging time.	More dependency on parameters, and more challenges in terms of scalability.	This research presents a unique decentralized adaptive control technique to govern EV contributions to primary frequency regulation in an islanded microgrid. The framework adjusts the droop parameter for microgrid and EV issues. The EV charger monitors frequency and adapts its contribution to load-generation balance changes.
^[53]	Hybrid-microgrid-based EV charging station	PV diesel generators and grid	Virtual synchronous machine control	Virtual inertia improves system stability and allows flexible control with many variables. Communication is unnecessary.	Complex controller implementation and parameter sensitivity cause non-linearity in its state space model.	The virtual synchronous generator (VSG) technique employing a CS to create inertia uses a fleet of EVs parked in the CS as energy storage for MG. The proposed strategy will be an effective answer for maintaining the regularity of an isolated MG.
^[54]	PEV charging with grid	Flywheel and grid	Droop-based hysteresis control	Optimizes the dynamic performance by controlling the peak-to-peak value of the current ripple.	Fluctuating frequency; delayed response in voltage fluctuation condition.	A hysteresis-type active power support approach from an FCS with the FESS was theoretically and empirically validated in this paper. The grid and FESS converters are not digitally connected while using droop-based DBS control. The approach effectively responds to system-level DSO signals without interrupting PEV battery-charging schedules.
^[55]	Two charging stations	PV, battery, and grid	Decentralized fuzzy logic control	Presents a robust response approach for addressing non-linear uncertainty in parameter variable systems.	Possessing a high level of expertise sensitivity.	This author proposes an MVDC bus-based DCM for charging stations (CSs). The key contribution is a novel decentralized control using fuzzy logic controllers as a decentralized EMS to manage the converters of two system components separately and co-ordinate power flow, MVDC voltage, and BESS SOC performance.
^[56]	Three 60 KW charging stations	PV, battery, and grid	Droop control techniques	Increases stability and power sharing	Unbalanced distributed generation impedance reduces load-sharing accuracy.	This work provides better decentralized virtual-battery-based droop control with bus voltage maintenance, load power dispatch, and energy storage system (ESS) SOC balance for autonomous and stable DC microgrid operation. The PV–ESS–grid integrated system’s core bus-signalling control switches PV array and grid control modes based on the ESS’s virtual OCV.
^[57]	EV charging station	PV and grid	Multi-agent-based decentralized scheduling algorithm	Controls a vast area and can boost grid resiliency and meet grid requirements in real time.	Requires two-way communication between agents and utilities and significant EV user authorization.	This paper offers a decentralized scheduling framework for charging EVs based on MAS, the charging control model. The MAS has “responsive” or “unresponsive” EV agents as well as an EV/DG aggregator agent. Based on forecasts of power consumption and generation, the EV/DG aggregator agent creates the virtual pricing strategy to maximize profit.
^[58]	DC-microgrid-based EV charger	PV and battery	Droop and master–slave control strategy	The system stability is enhanced when compared to using only a conventional master control or conventional droop control scheme.	More dependency on solar energy; constant DC bus voltage maintaining is challenging task.	This work proposes an EVCS combination control method that combines the benefits of droop and master control strategies. An isolated bidirectional DC–DC converter, snubber circuits, and a three-level boost converter with capacitance-voltage control further improve system stability.

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