# Using an Intelligent Control Method for Electric Vehicle Charging in Microgrids

^{*}

## Abstract

**:**

## 1. Introduction

#### Literature Review

_{2}removal are not presented in this article.

- (A)
- The proposed method checks the time loop and charging level according to the objectives to reach the desired answer in the objective functions according to the predetermined scenarios.
- (B)
- The proposed method can perform intelligent charging with high safety without increasing the battery’s maximum voltage.
- (C)
- The proposed method designs the charging station with simultaneous consideration of the three goals: maximizing the charging demand every hour of the day and night, improving the network load profile, and minimizing the operation costs.

## 2. DC Bidirectional Fast Charging Station

#### 2.1. Comprehensive AC/DC Converter Control

#### 2.2. DC/DC Converter Control

#### 2.3. Two-Way Power Transmission

_{12}was maintained in the circuit, P and Q could be seen from the following equations:

#### 2.4. EV Battery Modeling

_{0}, B, A, and K were obtained as follows:

#### Sine Pulse Width Modulation

## 3. Proposed Method

#### 3.1. Paralleling Synchronous Generators with the Network and with Each Other

#### 3.2. Proposed System Modeling

## 4. Simulation of Bidirectional Reactive Power Exchange in the Studied Network

#### 4.1. Analysis and Simulation of CC/RCC Charging Modes with DC/DC Converters

#### 4.2. Simulation and Analysis of Bidirectional Reactive Power Exchange

#### 4.2.1. Fast Charging of EVs without Reactive Power Compensation

#### 4.2.2. Fast Charging of EVs with Reactive Power Compensation

- (A)
- Voltage adjustment for steady state voltage of 0.96 p.u

- (B) Voltage adjustment for the nominal voltage of 1 unit

- (C) Simulation and analysis of the proposed plan

#### 4.3. Autonomous Microgrid Simulation for Charging EV Batteries

#### 4.4. Simulating the Microgrid Connected to a Distribution Network and a Diesel Generator

#### 4.5. Simulation of Microgrid Connected to the Distribution Network and Diesel Generator Selectively

## 5. Conclusions and Future Work

#### 5.1. Conclusion

#### 5.2. Future Work

- The design of a power controller is suggested for the diesel generator so that with any change in the DC link voltage range, the diesel can maintain the link voltage by injecting sufficient power in the reference value.
- To improve the microgrid’s efficiency and increase its reliability, it is suggested to use other scattered products such as wind turbines, solar modules, and fuel cells.
- In the method of the selective feeding of the diesel and network bus, the basis for changing the source from the network bus to the diesel generator and vice versa is the voltage range of the DC link (in this study, the basis for changing and switching sources to each other was the voltage drop in the distribution network bus).

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Nomenclature and Abbreviation

Nomenclature | |||

I(t) | EV charging current | EV | Electric vehicle |

${Q}^{t}$ | Rated capacity of ith EV battery in Ah | BMS | Battery management system |

${N}_{EV}$ | Number of charging EVs in the time slot | NLP | Nonlinear programming problem |

C | Charging rate of EVs | CC/RCC | Constant current/constant reverse current |

$gen$ | Power of generation of PV system in parking lots | PCA | Principal component analysis |

$\mathrm{t}$ | Time | IOT | Internet of the things |

$I$ | The line connected to the bass | RFID | Radiofrequency identification |

$cost(.)$ | Upstream energy costs | DL | Deep learning |

${p}^{max},p{}^{min}$ | Maximum and minimum active power output of the upstream network | ML | Machine learning |

${q}^{max},q{}^{min}$ | Maximum and minimum reactive power produced by the upstream network | BD | Big data |

${v}^{max},v{}^{min}$ | Maximum and minimum voltage | LSTM | Long short-term memory |

$N$ | Number of network buses | R | Resiliency |

$Connect\left(\dots ..\right)$ | Connecting electric vehicles to the network | IPV | Internet protocol version |

${C}_{in}\left(\dots ..\right)$ | The initial charge level of the electric car when entering the parking lot | MG | Microgrid |

$B\left(\dots .\right)$ | Suspension | EV | Electric vehicle |

$G\left(\dots .\right)$ | Capacity | DG | Distributed generation |

$Lin(\dots )$ | Network line | EMS | Energy management strategy |

$qD$ | Reactive load | SAG CS | Stand-alone grid Charging station |

${P}_{D}$ | Active time | LIB | Lithium-ion battery |

$BC$ | Battery capacity | MPPT | Maximum power point tracking |

$CR$ | Electric car battery charge rate | PV | Photovoltaic |

${C}_{Total}$ | Total cost | PHEV | Plug-in hybrid electric vehicle |

$P{L}_{Total}$ | Total wasted power | DES | Distributed energy resource |

${V}_{R}(\dots )$ | The real part of the voltage | PWM | Pulse width modulation |

${V}_{I}\left(\dots .\right)$ | The imaginary part of the voltage | DAB | Dual active bridge |

${I}_{R}(\dots )$ | The real part of the flow | STC | Standard test condition |

${I}_{I}\left(\dots .\right)$ | The imaginary part of the flow | BMS | Battery management system |

P_{loss}(.) | Lost active power | SOC | State of charge |

${Q}_{loss}(.)$ | Lost active power | MIDC | Measurement and instrumentation Data center |

${N}_{loss}(.)$ | Total network losses per hour | OB | Off-board |

$T{N}_{loss}$ | Total network losses | RE | Renewable energy |

CB | Capacity of battery | PCA | Principal component analysis |

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**Figure 2.**Reactive power generation is based on the voltage difference between the EV system and the grid.

**Figure 7.**Battery charging waveforms in the charging method RCC/CC: (

**a**) battery voltage; (

**b**) battery current; (

**c**) battery charging status with SOC.

**Figure 9.**The proposed method for fast charging. (

**a**) Paralleling two independent generators. (

**b**) Paralleling the generator with the grid.

**Figure 13.**Reactive power exchange and battery charging (

**a**) the voltage difference between the EV system and the grid. (

**b**) Reactive power exchange in grid (

**c**) Battery charging waveforms in the CC/RCC are charging methods.

**Figure 14.**Fast charging results for EVs without reactive power compensation control. (

**a**) Bus voltage; (

**b**) exchange of active and reactive power; (

**c**) link voltage; (

**d**) battery current; (

**e**) battery voltage in the SOC; (

**f**) batteries.

**Figure 15.**The results of fast charging of EVs with reactive power compensation control at bus voltage of 0.96 p.u. (

**a**) Bass voltage; (

**b**) exchange of active and reactive power; (

**c**) link voltage; (

**d**) battery current; (

**e**) battery voltage; (

**f**) SOC.

**Figure 16.**EV fast charging results with reactive power compensation control at a bus voltage of 0.96 p.u. (

**a**) Bus voltage; (

**b**) active and reactive power exchange; (

**c**) DC link voltage; (

**d**) battery current; (

**e**) battery voltage; (

**f**) SOC.

**Figure 17.**Fast charging results of EVs in microgrid and stand-alone and off-grid conditions (

**a**) generator voltage; (

**b**) active and reactive power exchange; (

**c**) DC link voltage; (

**d**) battery current; (

**e**) battery voltage; (

**f**) SOC.

**Figure 18.**Diesel generator operation in a microgrid separated from the distribution network (off-grid); (

**a**) angular speed; (

**b**) stimulation voltage; (

**c**) DC electromechanical torque.

**Figure 19.**The results of fast charging of EVs connected to the grid and diesel (

**a**) grid bus voltage; (

**b**) active and reactive power exchange of the grid bus with the EV system; (

**c**) DC link voltage; (

**d**) battery current; (

**e**) battery voltage; (

**f**) SOC.

**Figure 20.**The behavior of the diesel generator in the microgrid. (

**a**) Generator bus voltage; (

**b**) active and reactive power exchange of the generator with the EV system; (

**c**) angular speed of the generator; (

**d**) excitation voltage; (

**e**) electromechanical torque.

**Figure 21.**The results of fast charging of EVs in the microgrid in the state of connection to the grid and selected diesel (

**a**) network bus voltage; (

**b**) exchange of active and reactive power of the network bus with the EV system; (

**c**) DC link voltage; (

**d**) battery current; (

**e**) voltage of batteries; (

**f**) SOC.

**Figure 22.**The behavior of the diesel generator in the microgrid in the state of connection to the grid and selected diesel (on-grid)—(

**a**) generator bus voltage; (

**b**) active and reactive power exchange of the generator with the system; (

**c**) angular speed of the generator; (

**d**) excitation voltage; (

**e**) electromechanical torque.

Condition | Power Transfer | |
---|---|---|

${\delta}_{1}>{\delta}_{2}$ | Active power of bass | Transfer from two to one |

${\delta}_{1}<{\delta}_{2}$ | Active power of bass | Transfer from one to two |

${V}_{1}>{V}_{2}$ | Reactive power of bass | Transfer from two to one |

${V}_{1}<{V}_{2}$ | Reactive power of bass | Transfer from one to two |

Parameters | Lithium Battery 360 Volt and 66.2201 Amp/h |
---|---|

${V}_{full}$ $\left(V\right)$ | 403.2643 |

${Q}_{exp}\text{}\left(Ah\right)$ | 6.004 |

${V}_{exp}\text{}\left(V\right)$ | 384.864 |

${Q}_{nom}\text{}\left(Ah\right)$ | 58.32 |

${V}_{nom}\text{}\left(V\right)$ | 345.62 |

Generator Specification | Values |
---|---|

Nominated Demand and Power Factor | 200 kVA, 0.85 lag |

Voltage and Nominated Frequency | 440 V, 50 Hz |

H (Inertia Constant) | 24 s |

Number of Poles | 4 |

Xd, Xd′, Xd″ | 1.0305, 0.296, 0.252 (p.u) |

Xq, Xq″, Xl | 0.474, 0.243, 0.18 (p.u) |

Td′, Td″, Tqo″ | 1.01, 0.053, 0.1 (s) |

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## Share and Cite

**MDPI and ACS Style**

Rastgoo, S.; Mahdavi, Z.; Azimi Nasab, M.; Zand, M.; Padmanaban, S.
Using an Intelligent Control Method for Electric Vehicle Charging in Microgrids. *World Electr. Veh. J.* **2022**, *13*, 222.
https://doi.org/10.3390/wevj13120222

**AMA Style**

Rastgoo S, Mahdavi Z, Azimi Nasab M, Zand M, Padmanaban S.
Using an Intelligent Control Method for Electric Vehicle Charging in Microgrids. *World Electric Vehicle Journal*. 2022; 13(12):222.
https://doi.org/10.3390/wevj13120222

**Chicago/Turabian Style**

Rastgoo, Samaneh, Zahra Mahdavi, Morteza Azimi Nasab, Mohammad Zand, and Sanjeevikumar Padmanaban.
2022. "Using an Intelligent Control Method for Electric Vehicle Charging in Microgrids" *World Electric Vehicle Journal* 13, no. 12: 222.
https://doi.org/10.3390/wevj13120222