# An EV Charging Guidance Strategy Based on the Hierarchical Comprehensive Evaluation Method

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Construction of the Network-Related Capability Evaluation System for Fast Charging Stations

#### 2.1. Evaluation Target of the Distribution Network Operation Level

#### 2.1.1. Voltage Offset Rate

#### 2.1.2. Voltage Violation Rate

#### 2.1.3. Load Fluctuation

#### 2.1.4. Active Power Loss

#### 2.2. Evaluation Target of the Road Network Operation Level

#### 2.2.1. Traffic Flow

#### 2.2.2. Link Travel Time

#### 2.2.3. Road Service Level

#### 2.3. Evaluation Target of the FCS Operation Level

#### 2.3.1. Charging Waiting Time

#### 2.3.2. Utilization Rate of FCSs

## 3. Comprehensive Evaluation Method for the Network Connection Capability of FCSs

#### 3.1. Index Layer

#### 3.1.1. Objective Weighting Method

#### 3.1.2. Subjective Weighting Method

#### 3.1.3. Fuzzy Comprehensive Evaluation Method

#### 3.2. Rule Layer

#### 3.2.1. Score Calculation of the Rule Layer

#### 3.2.2. Weight Coefficient of the Rule Layer

#### 3.3. Target Layer

## 4. Charging Guidance Strategy Based on the Maximum Comprehensive Evaluation Score

## 5. Case Study

#### 5.1. Evaluation Results of Index Layer

#### 5.1.1. Evaluation Results of the Distribution Network

#### 5.1.2. Evaluation Results of the Road Network

#### 5.1.3. Evaluation Results of FCSs

#### 5.2. Evaluation Results of the Rule Layer

#### 5.3. Evaluation Results of Target Layer

## 6. Conclusions

- (1)
- By the application of the proposed evaluation method, the network-related capacity of FCSs can be quantitatively analyzed in multiple dimensions. The network-related capacity of FCSs is evaluated comprehensively based on the state of the distribution network, road network, and FCSs.
- (2)
- By the application of the proposed EV guidance strategy, the state relationships among the distribution network, road network, and charging station are balanced, and the network-related capacity of FCSs is improved.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

- Zenginis, I.; Vardakas, J.; Zorba, N.; Verikoukis, C. Performance Evaluation of a Multi-standard Fast Charging Station for Electric Vehicles. IEEE Trans. Smart Grid
**2018**, 9, 4480–4489. [Google Scholar] [CrossRef] - Yuan, L.; Chen, H.; Gong, J. Saving energy and protecting environment of electric vehicles. IOP Conf. Ser. Earth Environ. Sci.
**2017**, 64, 012051. [Google Scholar] [CrossRef] - Kakillioglu, E.A.; Aktaş, M.Y.; Fescioglu-Unver, N. Self-controlling resource management model for electric vehicle fast charging stations with priority service. Energy
**2022**, 239, 122276. [Google Scholar] [CrossRef] - Aghapour, R.; Sepasian, M.S.; Arasteh, H.; Vahidinasab, V.; Catalão, J.P. Probabilistic planning of electric vehicles charging stations in an integrated electricity-transport system. Electr. Power Syst. Res.
**2020**, 189, 106698. [Google Scholar] [CrossRef] - Kumar, Y.A.; Kim, H.J. Effect of time on a hierarchical corn skeleton-like composite of CoO@ZnO as capacitive electrode material for high specific performance supercapacitors. Energies
**2018**, 11, 3285. [Google Scholar] [CrossRef][Green Version] - Moniruzzaman, M.; Anil Kumar, Y.; Pallavolu, M.R.; Arbi, H.M.; Alzahmi, S.; Obaidat, I.M. Two-dimensional core-shell structure of cobalt-doped@ MnO
_{2}nanosheets grown on nickel foam as a binder-free battery-type electrode for supercapacitor application. Nanomaterials**2022**, 12, 3187. [Google Scholar] [CrossRef] - Wang, H.; Yu, Z.; Li, X.; Bian, J. Comprehensive risk assessment of multiple types of electric vehticles connected to distribution network based on principal component analysis method. Electr. Power Autom. Equip.
**2021**, 41, 57–65. [Google Scholar] - Chen, W.; Yang, B.; Zhang, Z.; Wen, M.; Chen, X. Distribution networks supportability evaluation and optimization considering electric vehicles charging stations. Trans. China Electrotech. Soc.
**2014**, 29, 27–35. [Google Scholar] - Wang, J.; Ming, L.; Yu, D. Technical and economic evaluation of the electric vehicle charging network planning scheme. J. Clean. Energy Technol.
**2015**, 3, 317–320. [Google Scholar] [CrossRef][Green Version] - Li, D.D.; Duan, W.Y.; Lin, S.F. User guidance based matching strategy for electric vehicle-charging pile in condition of real-time electricity price. Autom. Electr. Power Syst.
**2020**, 44, 74–82. [Google Scholar] - Ding, Z.; Zhang, Y.; Tan, W.; Pan, X.; Tang, H. Pricing based Charging Navigation Scheme for Highway Transportation to Enhance Renewable Generation Integration. IEEE Trans. Ind. Appl.
**2023**, 59, 108–117. [Google Scholar] [CrossRef] - Zhao, T.; Li, Y.; Pan, X.; Wang, P.; Zhang, J. Real-Time Optimal Energy and Reserve Management of Electric Vehicle Fast Charging Station: Hierarchical Game Approach. IEEE Trans. Smart Grid
**2018**, 9, 5357–5370. [Google Scholar] [CrossRef] - Sun, X.; Qiu, J. Hierarchical Voltage Control Strategy in Distribution Networks Considering Customized Charging Navigation of Electric Vehicles. IEEE Trans. Smart Grid
**2021**, 12, 4752–4764. [Google Scholar] [CrossRef] - Yang, H.; Deng, Y.; Qiu, J.; Li, M.; Lai, M.; Dong, Z.Y. Electric Vehicle Route Selection and Charging Navigation Strategy Based on Crowd Sensing. IEEE Trans. Ind. Inform.
**2017**, 13, 2214–2226. [Google Scholar] [CrossRef] - Li, X.; Xiang, Y.; Lyu, L.; Ji, C.; Zhang, Q.; Teng, F.; Liu, Y. Price Incentive-Based Charging Navigation Strategy for Electric Vehicles. IEEE Trans. Ind. Appl.
**2020**, 56, 5762–5774. [Google Scholar] [CrossRef] - Tan, J.; Wang, L. Real-Time Charging Navigation of Electric Vehicles to Fast Charging Stations: A Hierarchical Game Approach. IEEE Trans. Smart Grid
**2017**, 8, 846–856. [Google Scholar] [CrossRef] - Moghaddam, Z.; Ahmad, I.; Habibi, D.; Phung, Q.V. Smart Charging Strategy for Electric Vehicle Charging Stations. IEEE Trans. Transp. Electrif.
**2018**, 4, 76–88. [Google Scholar] [CrossRef] - Shi, X.; Xu, Y.; Guo, Q.; Sun, H.; Gu, W. A Distributed EV Navigation Strategy Considering the Interaction between Power System and Traffic Network. IEEE Trans. Smart Grid
**2020**, 11, 3545–3557. [Google Scholar] [CrossRef] - Jin, Z.; Wu, R.; Chen, X.; Li, G. Charging Guiding Strategy for Electric Taxis Based on Consortium Blockchain. IEEE Access
**2019**, 7, 144144–144153. [Google Scholar] [CrossRef] - Qian, T.; Shao, C.; Wang, X.; Shahidehpour, M. Deep Reinforcement Learning for EV Charging Navigation by Coordinating Smart Grid and Intelligent Transportation System. IEEE Trans. Smart Grid
**2019**, 11, 1717–1723. [Google Scholar] [CrossRef] - Xiang, Y.; Yang, J.; Li, X.; Gu, C.; Zhang, S. Routing Optimization of Electric Vehicles for Charging with Event-Driven Pricing Strategy. IEEE Trans. Autom. Sci. Eng.
**2022**, 19, 7–20. [Google Scholar] [CrossRef] - Eisner, J.; Funke, S.; Storandt, S. Optimal route planning for electric vehicles in large networks. In Proceedings of the AAAI Conference on Artificial Intelligence, San Francisco, CA, USA, 7–11 August 2011; Volume 25. [Google Scholar]
- Luo, Y.; Zhu, T.; Wan, S.; Zhang, S.; Li, K. Optimal charging scheduling for large-scale EV (electric vehicle) deployment based on the interaction of the smart-grid and intelligent-transport systems. Energy
**2016**, 97, 359–368. [Google Scholar] [CrossRef] - Wang, Y.; Wang, X.; Cong, R.; Wang, L.; Huang, Y.; Chen, D. Charging Guidance Strategy for Electric Vehicles Considering the Operation Safety of Distribution Network. High Volt. Eng.
**2023**. Available online: https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C45S0n9fL2suRadTyEVl2pW9UrhTDCdPD669CjSZCpCUE3OMBkpP_QeqKlNgCewvqYd8Xode39hwyfRmfXyL9CP_&uniplatform=NZKPT (accessed on 30 November 2022). - Technical Standard of High Engineering. Available online: https://xxgk.mot.gov.cn/2020/jigou/glj/202006/t20200623_3312363.html (accessed on 21 February 2023).

Strategy | System Operation Status Considered | ||
---|---|---|---|

Distribution Network | Road Network | FCSs | |

This paper | ✓ | ✓ | ✓ |

Literature [22] | ✓ | - | - |

Literature [23] | - | ✓ | - |

Literature [24] | - | - | ✓ |

**Table 2.**The road service level [25].

Road Service Level | $\mathit{\omega}$ Value | Road Service Level | $\mathit{\omega}$ Value |
---|---|---|---|

1 | $\omega \le 0.3$ | 4 | $0.7<\omega \le 0.9$ |

2 | $0.3<\omega \le 0.5$ | 5 | $0.9<\omega \le 1$ |

3 | $0.5<\omega \le 0.7$ | 6 | $\omega >1$ |

${N}_{j}$ | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |

${R}_{I}$ | 0 | 0 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 |

FCS ID | Distribution Network ID (Node) | Rode Network Node | Charging Pile Number | Charging Pile Power (kW) |
---|---|---|---|---|

1 | 1 (11) | 5 | 8 | 60 |

2 | 2 (17) | 12 | 8 | 60 |

3 | 3 (21) | 15 | 8 | 60 |

4 | 1 (25) | 24 | 8 | 60 |

5 | 3 (33) | 31 | 8 | 60 |

Calculation Result | Guidance Strategy |
---|---|

(a) | The proposed strategy |

(b) | The shortest path guidance strategy [22] |

(c) | The shortest travel time guidance strategy [23] |

(d) | The lowest charging price guidance strategy [24] |

Index Name | Type | Index Name | Type |
---|---|---|---|

Voltage offset rate | negative index | Link travel time | negative index |

Voltage violation rate | negative index | Road service level | negative index |

Load fluctuation | negative index | Charging waiting time | negative index |

Active power loss | negative index | Utilization rate of FCSs | positive index |

Traffic flow | negative index |

Voltage Offset Rate | Voltage Violation Rate | Active Power Loss | Load Fluctuation | |
---|---|---|---|---|

Objective weight coefficient | 0.15 | 0.40 | 0.30 | 0.15 |

Subjective weight coefficient | 0.05 | 0.48 | 0.08 | 0.39 |

Link Travel Time | Traffic Flow | Road Service Level | |
---|---|---|---|

Objective weight coefficient | 0.35 | 0.25 | 0.40 |

Subjective weight coefficient | 0.16 | 0.42 | 0.42 |

Charging Waiting Time | Utilization Rate of FCSs | |
---|---|---|

Objective weight coefficient | 0.42 | 0.58 |

Subjective weight coefficient | 0.75 | 0.25 |

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Zhang, C.; Gao, Q.; Peng, K.; Jiang, Y. An EV Charging Guidance Strategy Based on the Hierarchical Comprehensive Evaluation Method. *Energies* **2023**, *16*, 3113.
https://doi.org/10.3390/en16073113

**AMA Style**

Zhang C, Gao Q, Peng K, Jiang Y. An EV Charging Guidance Strategy Based on the Hierarchical Comprehensive Evaluation Method. *Energies*. 2023; 16(7):3113.
https://doi.org/10.3390/en16073113

**Chicago/Turabian Style**

Zhang, Cong, Qun Gao, Ke Peng, and Yan Jiang. 2023. "An EV Charging Guidance Strategy Based on the Hierarchical Comprehensive Evaluation Method" *Energies* 16, no. 7: 3113.
https://doi.org/10.3390/en16073113