# A Fuzzy Demand-Profit Model for the Sustainable Development of Electric Vehicles in China from the Perspective of Three-Level Service Chain

^{*}

## Abstract

**:**

## 1. Introduction

_{2}) are eight times that of 2005. As part of the new energy drives, pure electric vehicles have great potential in reducing environmental pollution, which has become an important strategic direction of sustainable development in the future [2]. Meanwhile, in recognition of these problems brought by traditional automotive, the relevant government departments in China have issued a series of policies and implementation measures to support the rapid development of the new energy industries [3]. Among them, the “Energy Conservation and New Energy Vehicle Industry Development Plan (2012–2020)” issued by the State Council clarifies the strategic positioning of China’s new energy vehicle industry, mainly driven by pure electric drives [4]. The continuous and stable supply of new energy is a necessary condition for the development of the new energy industry. It is also the key to the recognition of new energy vehicles in the end consumption segment [5]. With the influence of policy support and market, the construction of new energy supply infrastructure has also achieved remarkable outcomes. According to the authoritative statistics, the total number of charging piles in China reached 450,000 in 2018, ranking first in the world [6]. However, it is commonly appreciated that the new energy automobile industry is still in the growth stage. At present, there are still many problems to be solved, such as the low coverage of charging infrastructure, uneven layout, inconvenience, and charging queuing time, which hinder the actual effective demand [7]. Furthermore, there is a severe imbalance between long queues and unattended charging stations in some areas in terms of the actual use of the new energy supply facilities. To solve the problem of imbalance, it is necessary to ensure information symmetry among new energy users, charging pile operators, and power suppliers, to carry out charging pile construction and energy supply according to the demand, and to improve the efficiency of resource allocation. Meanwhile, the demand of new energy users is the basis for the construction of charging pile operators, as well as the basis for power suppliers to provide energy supply to charging pile operators. Therefore, the study of their disharmony should be analyzed as a three-level service chain composed of these three participants.

## 2. New Energy Supply Service Chain

## 3. Model Development

#### 3.1. The Fuzzy Demand Model for the New Energy Supply Single-Format Service Chain

#### 3.2. The Profit Model for the New Energy Supply Single-Format Service Chain

- (a)
- The power supplier provides energy supply guarantee for the charging pile operator. In order to ensure the safe and orderly supply of energy, the power supplier needs to transform and increase the voltage of the network. This investment cannot be completed suddenly, and the charging pile operators need to provide a contract price and quantity to ensure the use of new investment can obtain a quick return on investment. Assume that before the investment of the new infrastructure, the charging pile operator and power suppliers arrive at the contract price of $p$ and quantity of $q$ and the energy supplier charges the charging pile operator at the energy supply price of ${p}_{1}$;
- (b)
- It is assumed that the power supplier adopts the same charging standard to the charging pile operator and provides a homogeneous energy supply service. The power supplier needs to invest a fixed cost of ${c}_{ef}$ to boost voltage for ensuring the quality and stability of the contract quantity and variable costs of ${c}_{ev}$ such as maintenance;
- (c)
- The charging price charged by the charging pile operator to provide energy supply to the end user is ${p}_{2}$. This price includes charging a service fee and the energy supply fee collected by the generation power supplier;
- (d)
- The charging pile operator provides end users with continuous and stable energy supply services. The fixed costs such as the construction of the charging infrastructure is ${c}_{cf}$, and the variable costs such as operation management is ${c}_{cv}$;
- (e)
- The price of end user using traditional fossil fuel energy is ${p}_{0}$, thus fulfilling the relationship of ${p}_{0}>{p}_{2}>{p}_{1}$;
- (f)
- The cost for the end user to choose alternative charging methods (e.g., home charging stations) is ${c}_{uv}$;
- (g)
- End users are advocates of low carbon emissions and will consider their contribution to the reducing carbon emissions. At the same time, they are rational economic people who pay attention to the cost of new energy use, hope to trade the reduced carbon emissions and convert them into their own economic benefits, and set the carbon emission reduction between new energy and traditional energy contribution value as $\xi $;
- (h)
- Fisker has announced that it will use the patented technology to run an electric vehicle for 800 km with a charging time of only 1 min. Volkswagen and many other car companies have released the latest technological breakthroughs, which can achieve a charging range of 450–6000 km within 15–30 min. From progress of these charging technologies, the charging time of new energy vehicles will not affect consumer demand. Therefore, this paper does not consider the impact of charging time on the charging demand.

## 4. Analysis Results

#### 4.1. Research Data

#### 4.1.1. Research Data for the Fuzzy Demand Model

^{2}of the polynomial function, exponential function, and power function are all greater than 0.9, and the fitting effect is good, which can be used for the prediction in the next step. The fitting data of the three methods are compared with the actual data from 2011 to 2018. The results are shown in Table 2.

#### 4.1.2. Research Data for the Profit Model

#### 4.2. Calculation Results

#### 4.2.1. Calculation Results of the Fuzzy Demand Model

#### 4.2.2. Calculation Results of the Profit Model

## 5. Discussion

#### 5.1. Changes of New Energy Demand and the Optimal Contract Number among the Main Entities of the Service Chain

#### 5.2. New Energy Demand and Changes in Expected Profit of Various Entities in the Service Chain

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Wang, H.; Fu, L.; Bi, J. CO
_{2}and pollutant emissions from passenger cars in China. Energy Policy**2011**, 39, 3005–3011. [Google Scholar] [CrossRef] - Wu, Y.; Zhang, L. Can the development of electric vehicles reduce the emission of air pollutants and greenhouse gases in developing countries. Transp. Res. Part D Transp. Environ.
**2017**, 51, 129–145. [Google Scholar] [CrossRef] - Wang, S.; Li, J.; Zhao, D. The impact of policy measures on consumer intention to adopt electric vehicles: Evidence from China. Transp. Res. Part A Policy Pract.
**2017**, 105, 14–26. [Google Scholar] [CrossRef] - National Energy Administration. Energy Conservation and New Energy Vehicle Industry Development Plan (2012–2020). Available online: http://www.nea.gov.cn/2012-07/10/c_131705726.htm (accessed on 10 July 2012).
- Brady, J.; Omahony, M. Development of a driving cycle to evaluate the energy economy of electric vehicles in urban areas. Appl. Energy
**2016**, 177, 165–178. [Google Scholar] [CrossRef] - Wang, Y.; Shi, J.; Wang, R.; Liu, Z.; Wang, L. Siting and sizing of fast charging stations in highway network with budget constraint. Appl. Energy
**2018**, 228, 1255–1271. [Google Scholar] [CrossRef] - Xiong, Y. Electric Vehicle Charging Station Placement and Management. Ph.D. Thesis, Nanyang Technological University, Singapore, 2018. [Google Scholar]
- Reyniers, D.J.; Tapiero, C.S. The Delivery and Control of Quality in Supplier-Producer Contracts. Manag. Sci.
**1995**, 41, 1581–1589. [Google Scholar] [CrossRef] - Reyniers, D.J.; Tapiero, C.S. Contract design and the control of quality in a conflictual environment. Eur. J. Oper. Res.
**1995**, 82, 373–382. [Google Scholar] [CrossRef] - Cachon, G.P.; Lariviere, M.A. Contracting to Assure Supply: How to Share Demand Forecasts in a Supply Chain. Manag. Sci.
**2001**, 47, 629–646. [Google Scholar] [CrossRef] [Green Version] - Cachon, G.P. Supply chain coordination with contracts. Handb. Oper. Res. Manag. Sci.
**2003**, 11, 227–339. [Google Scholar] - Cachon, G.P.; Larivice, M.A. Supply chain coordination with revenue sharing contracts: Strengths and limitations. Manag. Sci.
**2005**, 51, 30–44. [Google Scholar] [CrossRef] [Green Version] - de Kok, A.G.; Graves, S.C. Supply Chain Coordination with Contracts. In Supply Chain Management: Design, Coordination and Operation; Elsevier: Amsterdam, The Netherlands, 2003. [Google Scholar]
- Chen, F. Information sharing and supply chain coordination. Handb. Oper. Res. Manag. Sci.
**2003**, 11, 341–421. [Google Scholar] - Giannoccaro, I.; Pontrandolfo, P. Supply chain coordination by revenue sharing contracts. Int. J. Prod. Econ.
**2004**, 89, 131–139. [Google Scholar] [CrossRef] - Cachon, G.P. The allocation of inventory risk in a supply chain: Push, pull, and advance-purchase discount contracts. Manag. Sci.
**2004**, 50, 222–238. [Google Scholar] [CrossRef] [Green Version] - Rong, M.; Maiti, M. On an EOQ model with service level constraint under fuzzy-stochastic demand and variable lead-time. Appl. Math. Model.
**2015**, 39, 5230–5240. [Google Scholar] [CrossRef] - Soni, H.N.; Patel, K.A. Optimal policies for integrated inventory system under fuzzy random framework. Int. J. Adv. Manuf. Technol.
**2015**, 78, 947–959. [Google Scholar] [CrossRef] - Mahata, G.C.; Goswami, A. Fuzzy inventory models for items with imperfect quality and shortage backordering under crisp and fuzzy decision variables. Comput. Ind. Eng.
**2013**, 64, 190–199. [Google Scholar] [CrossRef] - Sadeghi, J.; Mousavi, S.M.; Niaki, S.T.A.; Sadeghi, S. Optimizing a bi-objective inventory model of a three-echelon supply chain using a tuned hybrid bat algorithm. Transp. Res. Part E Logist. Transp. Rev.
**2014**, 70, 274–292. [Google Scholar] [CrossRef] - Sadeghi, J. A multi-item integrated inventory model with different replenishment frequencies of retailers in a two-echelon supply chain management: A tuned-parameters hybrid meta-heuristic. Opsearch
**2015**, 52, 631–649. [Google Scholar] [CrossRef] - Sadeghi, J.; Mousavi, S.M.; Niaki, S.T.A. Optimizing an inventory model with fuzzy demand, backordering, and discount using a hybrid imperialist competitive algorithm. Appl. Math. Model.
**2016**, 40, 7318–7335. [Google Scholar] [CrossRef] - Tong, A.; Dao-zhi, Z. A supply chain model of vendor managed inventory with fuzzy demand. In Proceedings of the 2010 International Conference on System Science, Engineering Design and Manufacturing Informatization, Yichang, China, 12–14 November 2010; Volume 2, pp. 15–18. [Google Scholar]
- Xu, R.; Zhai, X. Optimal models for single-period supply chain problems with fuzzy demand. Inf. Sci.
**2008**, 178, 3374–3381. [Google Scholar] [CrossRef] - Xu, R.; Zhai, X. Analysis of supply chain coordination under fuzzy demand in a two-stage supply chain. Appl. Math. Model.
**2010**, 34, 129–139. [Google Scholar] [CrossRef] - Chakraborty, D.; Jana, D.K.; Roy, T.K. Multi-item integrated supply chain model for deteriorating items with stock dependent demand under fuzzy random and bifuzzy environments. Comput. Ind. Eng.
**2015**, 88, 166–180. [Google Scholar] [CrossRef] - Jana, D.K.; Das, B.; Maiti, M. Multi-item partial backlogging inventory models over random planninghorizon in random fuzzy environment. Appl. Soft Comput.
**2014**, 21, 12–27. [Google Scholar] [CrossRef] - Xu, W. Integrated inventory problem under trade credit in fuzzy random environment. Fuzzy Optim. Decis. Mak.
**2014**, 13, 329–344. [Google Scholar] [CrossRef] - Alsalloum, O.I.; Rand, G.K. Extensions to emergency vehicle location models. Comput. Oper. Res.
**2006**, 33, 2725–2743. [Google Scholar] [CrossRef] [Green Version] - Araz, C.; Selim, H.; Ozkarahan, I. A fuzzy multi-objective covering-based vehicle location model for emergency services. Comput. Oper. Res.
**2007**, 34, 705–726. [Google Scholar] [CrossRef] - Xing, H. The decision method of emergency supplies collection with fuzzy demand constraint under background of sudden disaster. Nat. Hazards
**2017**, 85, 869–886. [Google Scholar] [CrossRef] - Zheng, Y.-J.; Ling, H.-F. Emergency transportation planning in disaster relief supply chain management: A cooperative fuzzy optimization approach. Soft Comput.
**2013**, 17, 1301–1314. [Google Scholar] [CrossRef] - Ruan, J.; Wang, X.; Chan, F.T.; Shi, Y. Optimizing the intermodal transportation of emergency medical supplies using balanced fuzzy clustering. Int. J. Prod. Res.
**2016**, 54, 4368–4386. [Google Scholar] [CrossRef] - Tang, Z.; Qin, J.; Sun, J. Railway emergency resource dispatching optimization based on fuzzy satisfaction degree under the priority principle. J. Intell. Fuzzy Syst.
**2017**, 33, 2677–2686. [Google Scholar] [CrossRef] - Zarandi, M.F.; Hemmati, A.; Davari, S. The multi-depot capacitated location-routing problem with fuzzy travel times. Expert Syst. Appl.
**2011**, 38, 10075–10084. [Google Scholar] [CrossRef] - Mehrjerdi, Y.Z.; Nadizadeh, A. Using greedy clustering method to solve capacitated location-routing problem with fuzzy demands. Eur. J. Oper. Res.
**2013**, 229, 75–84. [Google Scholar] [CrossRef] - Ghaffari-Nasab, N.; Ahari, S.G.; Ghazanfari, M. A hybrid simulated annealing based heuristic for solving the location-routing problem with fuzzy demands. Sci. Iran.
**2013**, 20, 919–930. [Google Scholar] - Nadizadeh, A.; Nasab, H.H. Solving the dynamic capacitated location-routing problem with fuzzy demands by hybrid heuristic algorithm. Eur. J. Oper. Res.
**2014**, 238, 458–470. [Google Scholar] [CrossRef] - Fazayeli, S.; Eydi, A.; Kamalabadi, I.N. Location-routing problem in multimodal transportation network with time windows and fuzzy demands: Presenting a two-part genetic algorithm. Comput. Ind. Eng.
**2018**, 119, 233–246. [Google Scholar] [CrossRef] - Wang, J.; Zhao, R.; Tang, W. Supply chain coordination by revenue-sharing contract with fuzzy demand. J. Intell. Fuzzy Syst.
**2008**, 19, 409–420. [Google Scholar] - Wang, J.; Zhao, R.; Tang, W. Supply chain coordination by single-period and long-term contracts with fuzzy market demand. Tsinghua Sci. Technol.
**2009**, 14, 218–224. [Google Scholar] [CrossRef] - Govindan, K.; Popiuc, M.N. Reverse supply chain coordination by revenue sharing contract: A case for the personal computers industry. Eur. J. Oper. Res.
**2014**, 233, 326–336. [Google Scholar] [CrossRef] - Sang, S. Revenue Sharing Contract in a Multi-Echelon Supply Chain with Fuzzy Demand and Asymmetric Information. Int. J. Comput. Intell. Syst.
**2016**, 9, 1028–1040. [Google Scholar] [CrossRef] [Green Version] - Chang, S.-Y.; Yeh, T.-Y. A two-echelon supply chain of a returnable product with fuzzy demand. Appl. Math. Model.
**2013**, 37, 4305–4315. [Google Scholar] [CrossRef] - Yu, Y.; Jin, T. The return policy model with fuzzy demands and asymmetric information. Appl. Soft Comput.
**2011**, 11, 1669–1678. [Google Scholar] [CrossRef] - Yu, Y.; Zhu, J.; Wang, C. A newsvendor model with fuzzy price-dependent demand. Appl. Math. Model.
**2013**, 37, 2644–2661. [Google Scholar] [CrossRef] - Zhang, B.; Lu, S.; Zhang, D.; Wen, K. Supply chain coordination based on a buyback contract under fuzzy random variable demand. Fuzzy Sets Syst.
**2014**, 255, 1–16. [Google Scholar] [CrossRef] - Moon, H.; Park, S.Y.; Jeong, C.; Lee, J. Forecasting electricity demand of electric vehicles by analyzing consumers’ charging patterns. Transp. Res. Part D Transp. Environ.
**2018**, 62, 64–79. [Google Scholar] [CrossRef] - Ryan, P. Electricity Demand and Implications of Electric Vehicle and Battery Storage Adoption. In Transition Towards 100% Renewable Energy; Springer International Publishing: Cham, Switzerland, 2018; pp. 391–398. [Google Scholar]
- Yıldız, B.; Arslan, O.; Karaşan, O.E. A branch and price approach for routing and refueling station location model. Eur. J. Oper. Res.
**2016**, 248, 815–826. [Google Scholar] [CrossRef] [Green Version] - Kim, J.-G.; Kuby, M.J. The deviation-flow refueling location model for optimizing a network of refueling stations. Int. J. Hydrogen Energy
**2012**, 37, 5406–5420. [Google Scholar] [CrossRef] - Kim, J.-G.; Kuby, M. A network transformation heuristic approach for the deviation flow refueling location model. Comput. Oper. Res.
**2013**, 40, 1122–1131. [Google Scholar] [CrossRef] - Camus, C.; Farias, T.L.; Esteves, J. Potential impacts assessment of plug-in electric vehicles on the Portuguese energy market. Energy Policy
**2011**, 39, 5883–5897. [Google Scholar] [CrossRef] - Boqiang, L.; Xin, Y.; Xiying, L. China’s energy strategy adjustment under energy conservation and carbon emission constraints. Soc. Sci. China
**2010**, 31, 91–110. [Google Scholar] [CrossRef] - Wang, Y.; Yao, X.; Yuan, P. Strategic Adjustment of China’s Power Generation Capacity Structure under the Constraint of Carbon Emission. Comput. Econ.
**2015**, 46, 421–435. [Google Scholar] [CrossRef] - Grubb, M.; Butler, L.; Twomey, P. Diversity and security in UK electricity generation: The influence of low-carbon objectives. Energy Policy
**2006**, 34, 4050–4062. [Google Scholar] [CrossRef] [Green Version] - Ruggles, K. Technology and the Service Supply Chain. Supply Chain Manag. Rev.
**2005**, 9, 12–14. [Google Scholar] - Chen, W.; Xu, M.; Xing, Q. Esearch on the Two Part Dynamic Pricing Strategy of New Energy Service Chain for Single Format. Math. Pract. Theor.
**2019**, 49, 112–122. [Google Scholar] - Li, J.; Yu, K.; Gao, P. Recycling and pollution control of the End of Life Vehicles in China. J. Mater. Cycles Waste Manag.
**2014**, 16, 31–38. [Google Scholar] [CrossRef] - Jin, L.I.; Jian Hua, Z. Policy changes and policy instruments selection of China’s new energy vehicle industry. China Popul. Resour. Environ.
**2017**, 27, 198–208. [Google Scholar] - Zhou, M.; Zhu, Z. Life Cycle Sustainability Assessment of Battery Electric Vehicle in China. J. Ind. Technol. Econom.
**2018**, 37, 75–84. [Google Scholar] - Song, W.-X.; Hou, H.-S.; Ji, X. Progress in the Investigation and Application of Na
_{3}V_{2}(PO_{4})_{3}for Electrochemical Energy Storage. Acta Phys. Chim. Sin.**2017**, 33, 103–129. [Google Scholar] [CrossRef] - Li, M.; Hu, D.; Zhou, Y. Research and Practice of Renewable Energy Local Consumption Mode in Gansu Province Based on “Double Alternative” Strategy. Power Syst. Technol.
**2016**, 40, 2991–2997. [Google Scholar]

**Figure 1.**Diagram of new energy supply single-format service chain. ESE, electricity supply enterprise; CPO, charging pile operator; TCF, the charging facility.

**Figure 4.**Comparison of the number of new energy demand and the optimal contract number of each subject in the service chain. Note: “*” stands for the optimal solution.

**Figure 5.**Expected profit of each entity of the service chain. Note: “*” stands for the optimal solution.

Goodness of Fitting Model | Polynomial Function | Exponential Function | Power Function | Linear Function | Logarithm Function |
---|---|---|---|---|---|

${\mathrm{R}}^{2}$ | 0.9928 | 0.9862 | 0.9276 | 0.7924 | 0.5501 |

Year | Actual Amount of Energy Vehicles (10,000) | Cumulative Amount of Energy Vehicles (10,000) | Polynomial Function | Exponential Function | Power Function | |||
---|---|---|---|---|---|---|---|---|

Fitting Value (10,000) | Differences between the Fitting Value and Cumulative Amount (10,000) | Fitting Value (10,000) | Differences between the Fitting Value and Cumulative Amount (10,000) | Fitting Value (10,000) | Differences between the Fitting Value and Cumulative Amount (10,000) | |||

2011 | 0.82 | 0.82 | 11.49 | 10.67 | 0.87 | 0.05 | 0.37 | −0.45 |

2012 | 1.28 | 2.10 | −6.83 | −8.92 | 2.08 | −0.02 | 2.86 | 0.76 |

2013 | 1.76 | 3.86 | −7.03 | −10.89 | 4.98 | 1.12 | 9.46 | 5.60 |

2014 | 7.75 | 11.61 | 10.88 | −0.72 | 11.94 | 0.34 | 22.12 | 10.52 |

2015 | 30.60 | 42.21 | 46.91 | 4.70 | 28.62 | −13.58 | 42.74 | 0.54 |

2016 | 50.07 | 92.28 | 101.04 | 8.77 | 68.59 | −23.68 | 73.22 | −19.06 |

2017 | 76.51 | 168.79 | 173.29 | 4.51 | 164.38 | −4.41 | 115.40 | −53.38 |

2018 | 102.98 | 271.77 | 263.66 | −8.11 | 393.93 | 122.16 | 171.15 | −100.61 |

Demand | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 |
---|---|---|---|---|---|---|---|

$q$ (Unit: GWh) | 68.40 | 82.08 | 90.29 | 99.32 | 109.25 | 120.17 | 132.19 |

Variable | ${\mathit{p}}_{0}$ | ${\mathit{p}}_{1}$ | ${\mathit{p}}_{2}$ | $\mathit{\xi}$ | ${\mathit{c}}_{\mathit{e}\mathit{v}}$ | ${\mathit{c}}_{\mathit{e}\mathit{f}}$ | ${\mathit{c}}_{\mathit{c}\mathit{v}}$ | ${\mathit{c}}_{\mathit{c}\mathit{f}}$ | ${\mathit{c}}_{\mathit{u}\mathit{v}}$ |
---|---|---|---|---|---|---|---|---|---|

Value (Yuan/kw.h) | 2.1 | 0.78 | 1.7 | 0.08 | 0.14 | 0.22 | 0.16 | 0.24 | 0.6 |

Fuzzy Demand | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 |
---|---|---|---|---|---|---|---|

$\tilde{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$ (Unit: GWh) | (63.40, 73.87, 84.34) | (67.72, 82.51, 97.31) | (73.04, 91.16, 109.28) | (78.87, 99.81, 120.74) | (85.04, 108.45, 131.87) | (91.44, 117.10, 142.76) | (98.01, 125.74, 153.47) |

Variable | Unit | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 |
---|---|---|---|---|---|---|---|---|

$q$ | GWh | 68.40 | 82.08 | 90.29 | 99.32 | 109.25 | 120.17 | 132.19 |

${q}_{{}_{ESE}}^{*}$ | GWh | 77.14 | 91.07 | 96.82 | 106.35 | 115.77 | 125.12 | 134.41 |

${q}_{{}_{CPO}}^{*}$ | GWh | 77.73 | 92.06 | 97.84 | 107.52 | 117.08 | 126.55 | 135.96 |

${q}_{{}_{Users}}^{*}$ | GWh | 80.15 | 96.18 | 102.03 | 112.37 | 122.50 | 132.50 | 142.38 |

${q}^{*}$ | GWh | 79.92 | 95.79 | 101.63 | 111.91 | 121.98 | 131.93 | 141.76 |

$E{\left({{\displaystyle \tilde{\prod}}}_{Users}\right)}^{*}$ | 1 × 10^{6} Yuan | 4.93 | 5.48 | 6.05 | 6.62 | 7.19 | 7.76 | 4.68 |

$E{\left({{\displaystyle \tilde{\prod}}}_{CPO}\right)}^{*}$ | 1 × 10^{6} Yuan | 24.53 | 29.50 | 29.28 | 31.98 | 34.71 | 37.47 | 40.26 |

$E{\left({{\displaystyle \tilde{\prod}}}_{ESE}\right)}^{*}$ | 1 × 10^{6} Yuan | 19.61 | 23.55 | 23.22 | 25.34 | 27.51 | 29.70 | 31.91 |

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**MDPI and ACS Style**

Chen, W.; Xu, M.; Xing, Q.; Cui, L.; Jiao, L.
A Fuzzy Demand-Profit Model for the Sustainable Development of Electric Vehicles in China from the Perspective of Three-Level Service Chain. *Sustainability* **2020**, *12*, 6389.
https://doi.org/10.3390/su12166389

**AMA Style**

Chen W, Xu M, Xing Q, Cui L, Jiao L.
A Fuzzy Demand-Profit Model for the Sustainable Development of Electric Vehicles in China from the Perspective of Three-Level Service Chain. *Sustainability*. 2020; 12(16):6389.
https://doi.org/10.3390/su12166389

**Chicago/Turabian Style**

Chen, Weiwei, Maozeng Xu, Qingsong Xing, Ligang Cui, and Liudan Jiao.
2020. "A Fuzzy Demand-Profit Model for the Sustainable Development of Electric Vehicles in China from the Perspective of Three-Level Service Chain" *Sustainability* 12, no. 16: 6389.
https://doi.org/10.3390/su12166389