A comprehensive assessment of working fluids selection in heat pipe-assisted battery thermal management systems: An integrated Multi-criteria decision-making approach
Restricted accessResearch articleFirst published online February, 2025
A comprehensive assessment of working fluids selection in heat pipe-assisted battery thermal management systems: An integrated Multi-criteria decision-making approach
Heat pipes are becoming increasingly popular for managing thermal conditions of electric car battery packs because of their durability, superconductive ability, low maintenance requirements, and lifespan. In this work, suitable working fluid for heat pipes has been identified using multi-criteria decision-making (MCDM) methods for 15 working fluids (Helium, Hydrogen, Oxygen, Nitrogen, Propane, Ethane, Ethanol, R123, Methanol, Toluene, Acetone, (FC-72), Ammonia, (HFE-7000), and water) as potential alternatives and nine decision attributes (thermal conductivity, specific heat capacity, compatibility with heat pipe material, phase change temperature, latent heat of vaporization (LHV), density, viscosity, surface tension, and cost). The criteria weights determined by Criteria importance through inter-criteria correlation (CRITIC) and analytical hierarchy process (AHP) schemes were utilized to prioritize considered working fluids through the application of Multi-Objective Optimization basis on Ratio Analysis (MOORA), Preference ranking organization method for enrichment evaluation (PROMETHEE) and weighted aggregated sum product assessment technique (WASPAS) ranking methods. The findings indicated that water is the best alternative, followed by ammonia. Cost emerged as the most convincing criterion, followed by the compatibility of heat pipe material. The qualitative analysis showed that the integrated weight-based PROMETHEE-II method is the most effective framework for this decision problem, followed by the MOORA and WASPAS methods.
RuhaniBAbidiAHusseinAK, et al.Numerical simulation of the effect of battery distance and inlet and outlet length on the cooling of cylindrical lithium-ion batteries and overall performance of thermal management system. J Energy Storage2022; 45: 103714. DOI: 10.1016/j.est.2021.103714.
2.
WuZHuX. SOC estimation of lithium battery based on online parameter identification and an improved particle filter algorithm. Proc Inst Mech Eng A J Power Energy2024; 238: 09576509241260085. DOI: 10.1177/09576509241260085.
KumarRGoelV. Experimental study on the thermal performance of lithium-ion battery cell using a heat pipe-assisted cooling system for electric vehicles. Exp Heat Tran2024; 37(6): 1–23. DOI: 10.1080/08916152.2024.2392812.
5.
MengJAzibTYueM. Early-Stage end-of-Life prediction of lithium-Ion battery using empirical mode decomposition and particle filter. Proc Inst Mech Eng A J Power Energy2023; 237(5): 1090–1099. DOI: 10.1177/09576509231153907.
6.
KumarRDwivediAGoelV. A review on heat pipe-assisted thermal management systems in electrical vehicles for lithium-ion batteries. In: Lecture notes in mechanical engineering. Singapore: Springer, 2022, Volume 1. DOI: 10.1007/978-981-99-7827-4_15.
7.
LiMSunWRaoM, et al.Parameter sensitivity analysis of an electrochemical-thermal model for energy-storage lithium-ion batteries. Proc Inst Mech Eng A J Power Energy2023; 237(7): 1585–1598. DOI: 10.1177/09576509231169555.
8.
YuefeiHDingJHuangS, et al.Improved coyote optimization algorithm for parameter estimation of lithium-ion batteries. Proc Inst Mech Eng A J Power Energy2023; 237(4): 787–796. DOI: 10.1177/09576509221147330.
9.
AmaleshTLakshmi NarasimhanN. Cooling of a lithium ion battery using phase change material with air/dielectric fluid media: a numerical study. Proc Inst Mech Eng A J Power Energy2020; 234(5): 722–738. DOI: 10.1177/0957650919859109.
10.
Geetha RaniMRangasamyR. Review of phase change material application in thermal management of electric vehicle battery pack. Proc Inst Mech Eng A J Power Energy2024; 238(1): 197–214. DOI: 10.1177/09576509231193195.
11.
kumar ChaudhariJSinghGKRathodMK, et al.Experimental and computational analysis on lithium-ion battery thermal management system utilizing air cooling with radial fins. J Therm Anal Calorim2024; 149(1): 203–218. DOI: 10.1007/s10973-023-12698-w.
12.
YangHWangZLiM, et al.A manifold channel liquid cooling system with low-cost and high temperature uniformity for lithium-ion battery pack thermal management. Therm Sci Eng Prog2023; 41: 101857. DOI: 10.1016/j.tsep.2023.101857.
13.
BernagozziMGeorgoulasAMicheN, et al.Heat pipes in battery thermal management systems for electric vehicles: a critical review. Appl Therm Eng2022; 219(A): 119495. DOI: 10.1016/j.applthermaleng.2022.119495.
14.
GoelVDwivediAKumarR, et al.PCM-assisted energy storage systems for solar-thermal applications: review of the associated problems and their mitigation strategies. J Energy Storage2023; 69: 107912. DOI: 10.1016/j.est.2023.107912.
15.
SalihSMAlsaberyAIHusseinAK, et al.Melting control of phase change material of semi-cylinders inside a horizontal baffled channel: convective laminar fluid–structure interaction. J Energy Storage2023; 58: 106312. DOI: 10.1016/j.est.2022.106312.
16.
GoelVSaxenaAKumarM, et al.Potential of phase change materials and their effective use in solar thermal applications: a critical review. Appl Therm Eng2022; 219(A): 119417. DOI: 10.1016/j.applthermaleng.2022.119417.
17.
ChenHAbidiAHusseinAK, et al.Investigation of the use of extended surfaces in paraffin wax phase change material in thermal management of a cylindrical lithium-ion battery: applicable in the aerospace industry. J Energy Storage2022; 45: 103685. DOI: 10.1016/j.est.2021.103685.
18.
DhaidanNSKokzSARashidFL, et al.Review of solidification of phase change materials dispersed with nanoparticles in different containers. J Energy Storage2022; 51: 104271. DOI: 10.1016/j.est.2022.104271.
19.
KumarRGoelV. A study on thermal management system of lithium-ion batteries for electrical vehicles: a critical review. J Energy Storage2023; 71: 108025. DOI: 10.1016/j.est.2023.108025.
20.
PathakSKKumarRGoelV, et al.Recent advancements in thermal performance of nano-fluids charged heat pipes used for thermal management applications: a comprehensive review. Appl Therm Eng2022; 216: 119023. DOI: 10.1016/j.applthermaleng.2022.119023.
21.
ArshadAAliHMYanW-M, et al.An experimental study of enhanced heat sinks for thermal management using n-eicosane as phase change material. Appl Therm Eng2018; 132: 52–66. DOI: 10.1016/j.applthermaleng.2017.12.066.
22.
YangRXieYLiK, et al.Thermal characteristics of solid-state battery and its thermal management system based on flat heat pipe. Appl Therm Eng2024; 252: 123575. DOI: 10.1016/j.applthermaleng.2024.123575.
23.
WangJYuYSongL, et al.Thermal management performance and optimization of a novel system combining heat pipe and composite fin for prismatic lithium-ion batteries. Energy Convers Manag2024; 302: 118106. DOI: 10.1016/j.enconman.2024.118106.
24.
AlihosseiniAShafaeeM. Experimental study and numerical simulation of a Lithium-ion battery thermal management system using a heat pipe. J Energy Storage2021; 39: 102616. DOI: 10.1016/j.est.2021.102616.
25.
WangLZhaoYQuanZ, et al.Investigation of thermal management of lithium-ion battery based on micro heat pipe array. J Energy Storage2021; 39: 102624. DOI: 10.1016/j.est.2021.102624.
26.
HusseinMLaoonualYWongwisesS. Experimental study on the thermal performance of a battery thermal management system using heat pipes. Case Stud Therm Eng2021; 26: 101029. DOI: 10.1016/j.csite.2021.101029.
27.
BehiHKarimiDBehiM, et al.A new concept of thermal management system in Li-ion battery using air cooling and heat pipe for electric vehicles. Appl Therm Eng2020; 174: 115280. DOI: 10.1016/j.applthermaleng.2020.115280.
28.
RaoZHuoYLiuX. Experimental study of an OHP-cooled thermal management system for electric vehicle power battery. Exp Therm Fluid Sci2014; 57: 20–26. DOI: 10.1016/j.expthermflusci.2014.03.017.
29.
YeYShiYSawLH, et al.Performance assessment and optimization of a heat pipe thermal management system for fast charging lithium ion battery packs. Int J Heat Mass Tran2016; 92: 893–903. DOI: 10.1016/j.ijheatmasstransfer.2015.09.052.
30.
ZhaoRGuJLiuJ. An experimental study of heat pipe thermal management system with wet cooling method for lithium ion batteries. J Power Sources2015; 273: 1089–1097. DOI: 10.1016/j.jpowsour.2014.10.007.
31.
HongSZhangXWangS, et al.Experiment study on heat transfer capability of an innovative gravity assisted ultra-thin looped heat pipe. Int J Therm Sci2015; 95: 106–114. DOI: 10.1016/j.ijthermalsci.2015.04.003.
32.
PutraNAriantaraBAji PamungkasR. Experimental investigation on performance of lithium-ion battery thermal management system using flat plate loop heat pipe for electric vehicle application. Appl Therm Eng2016; 99: 784–789. DOI: 10.1016/j.applthermaleng.2016.01.123.
33.
QuJWangCLiX, et al.Heat transfer performance of flexible oscillating heat pipes for electric/hybrid-electric vehicle battery thermal management. Appl Therm Eng2018; 135: 1–9. DOI: 10.1016/j.applthermaleng.2018.02.045.
34.
WeiAQuJQiuH, et al.Heat transfer characteristics of plug-in oscillating heat pipe with binary-fluid mixtures for electric vehicle battery thermal management. Int J Heat Mass Tran2019; 135: 746–760. DOI: 10.1016/j.ijheatmasstransfer.2019.02.021.
GrecoACaoDJiangX, et al.A theoretical and computational study of lithium-ion battery thermal management for electric vehicles using heat pipes. J Power Sources2014; 257: 344–355. DOI: 10.1016/j.jpowsour.2014.02.004.
37.
FatihE. A consolidated MCDM framework for performance assessment of battery electric vehicles based on ranking strategies. Renew Sustain Energy Rev2021; 143: 110916. DOI: 10.1016/j.rser.2021.110916.
38.
DwivediAKumarAGoelV. A consolidated decision-making framework for nano-additives selection in battery thermal management applications. J Energy Storage2023; 59: 106565. DOI: 10.1016/j.est.2022.106565.
39.
Prasad DwivediPSharmaDK. Evaluation and ranking of battery electric vehicles by Shannon’s entropy and TOPSIS methods. Math Comput Simulat2023; 212: 457–474. DOI: 10.1016/j.matcom.2023.05.013.
40.
HarshadCSKulkarniSD. An integrated AHP-MABAC approach for electric vehicle selection. Research in Transportation Business & Management2021; 41: 100665. DOI: 10.1016/j.rtbm.2021.100665.
41.
DwivediAKumarAGoelV. Selection of nanoparticles for battery thermal management system using integrated multiple criteria decision‐making approach. Int J Energy Res2022; 46(15): 22558–22584. DOI: 10.1002/er.8561.
42.
TajikMMakuiATosarkaniBM. Sustainable cathode material selection in lithium-ion batteries using a novel hybrid multi-criteria decision-making. J Energy Storage2023; 66: 107089. DOI: 10.1016/j.est.2023.107089.
43.
BulutEIsa AlbakESevilgenG, et al.A new approach for battery thermal management system design based on Grey Relational Analysis and Latin Hypercube Sampling. Case Stud Therm Eng2021; 28: 101452. DOI: 10.1016/j.csite.2021.101452.
44.
DongFChengZZhuJ, et al.Investigation and optimization on cooling performance of a novel double helix structure for cylindrical lithium-ion batteries. Appl Therm Eng2021; 189: 116758. DOI: 10.1016/j.applthermaleng.2021.116758.
45.
AliKKayaİErdoğanM. Location selection of electric vehicles charging stations by using a fuzzy MCDM method: a case study in Turkey. Neural Comput Appl2020; 32: 4553–4574. DOI: 10.1007/s00521-018-3752-2.
46.
RenXSunSYuanR. A study on selection strategies for battery electric vehicles based on sentiments, analysis, and the MCDM model. Math Probl Eng2021; 1: 9984343. DOI: 10.1155/2021/9984343.
47.
Pratik PradhanSPradhanS. Selection of electric vehicle using integrated Fuzzy-MCDM approach with analysis on challenges faced in hilly terrain. Energy Sources, Part A Recovery, Util Environ Eff2022; 44(2): 2651–2673. DOI: 10.1080/15567036.2022.2056665.
48.
WangRLiXXuC, et al.Study on location decision framework of electric vehicle battery swapping station: using a hybrid MCDM method. Sustain Cities Soc2020; 61: 102149. DOI: 10.1016/j.scs.2020.102149.
49.
MozumderAKAkonAFChowdhuryMSH, et al.Performance of heat pipe for different working fluids and fill ratios. J Mech Eng2010; 41(2): 96–102.
50.
BabuERGnanendra ReddyGV. Effect of working fluid and filling ratio on performance of a closed loop pulsating heat pipe. J Eng Sci Technol2016; 11(6): 872–880.
51.
XuYXueYQiH, et al.An updated review on working fluids, operation mechanisms, and applications of pulsating heat pipes. Renew Sustain Energy Rev2021; 144: 110995. DOI: 10.1016/j.rser.2021.110995.
52.
AlkhulaifiYMShujaSZYilbasBS. Techno-economic analysis of nitrogen-doped graphene/water nanofluid in various heat exchangers (A unified analysis). Proc Inst Mech Eng A J Power Energy2022; 236(4): 760–775. DOI: 10.1177/09576509211052119.
53.
KimJKimSJ. Experimental investigation on working fluid selection in a micro pulsating heat pipe. Energy Convers Manag2020; 205: 112462. DOI: 10.1016/j.enconman.2019.112462.
54.
Kumar MallaLVempenyDTDileepH, et al.Thermal performance comparison of flat plate pulsating heat pipes of different material thermal conductivity using ethanol-water mixtures. Appl Therm Eng2024; 236: 121475. DOI: 10.1016/j.applthermaleng.2023.121475.
55.
MohammadAAhmadiMHGhasempourR, et al.How to improve the thermal performance of pulsating heat pipes: a review on working fluid. Renew Sustain Energy Rev2018; 91: 630–638. DOI: 10.1016/j.rser.2018.04.042.
56.
PagliariniLIwataNBozzoliF. Pulsating heat pipes: critical review on different experimental techniques. Exp Therm Fluid Sci2023; 148: 110980. DOI: 10.1016/j.expthermflusci.2023.110980.
57.
QianNJiangFMarengoM, et al.Internal flow characteristics of radial rotating oscillating heat pipe filled with acetone or water. Int Commun Heat Mass Tran2024; 157: 107718. DOI: 10.1016/j.icheatmasstransfer.2024.107718.
58.
ShahYKimH-GChoiW-W, et al.Experimental pool boiling study on novel multistage cross-flow porous structure using FC-72 for high-heat-flux electronic applications. Int J Heat Mass Tran2023; 213: 124270. DOI: 10.1016/j.ijheatmasstransfer.2023.124270.
59.
WangHXuJShiH, et al.Thermal performance of a liquid-immersed battery thermal management system for lithium-ion pouch batteries. J Energy Storage2022; 46: 103835. DOI: 10.1016/j.est.2021.103835.
60.
El-GenkMSMahyarP. Experimental investigation of saturation boiling of HFE-7000 dielectric liquid on rough copper surfaces. Therm Sci Eng Prog2020; 15: 100428. DOI: 10.1016/j.tsep.2019.100428.
61.
HussamJChauhanANannouT, et al.Heat pipe based systems-Advances and applications. Energy2017; 128: 729–754. DOI: 10.1016/j.energy.2017.04.028.
62.
Sáenz-RoyoCChiclanaFHerrera-ViedmaE. Ordering vs. AHP. Does the intensity used in the decision support techniques compensate?Expert Syst Appl2024; 238: 121922. DOI: 10.1016/j.eswa.2023.121922.
63.
KumarRKumarSAğbulutÜ, et al.Parametric optimization of an impingement jet solar air heater for active green heating in buildings using hybrid CRITIC-COPRAS approach. Int J Therm Sci2024; 197: 108760. DOI: 10.1016/j.ijthermalsci.2023.108760.
64.
Santonab ChakrabortyNirjhar DattaHKalitaKChakrabortyS. A narrative review of multi-objective optimization on the basis of ratio analysis (MOORA) method in decision making. Opsearch2023; 60(4): 1844–1887. DOI: 10.1007/s12597-023-00676-7.
65.
ZorluKDedeV. Evaluation of nature-based tourism potential in protected and sensitive areas by CRITIC and PROMETHEE-GAIA methods. International Journal of Geoheritage and Parks2023; 11(3): 349–364. DOI: 10.1016/j.ijgeop.2023.05.004.
66.
KannanSVaratharajuluMHariharasakthisudhanP, et al.Identification of the effect of strontium and ytterbium addition over magnesium zinc alloy during the drilling process with ARAS and WASPAS techniques. Mater Chem Phys2023; 309: 128320. DOI: 10.1016/j.matchemphys.2023.128320.
67.
ThomasLS. A scaling method for priorities in hierarchical structures. J Math Psychol1977; 15(3): 234–281. DOI: 10.1016/0022-2496(77)90033-5.
68.
ThomasLS. Fundamentals of decision making and priority theory with the analytic hierarchy process. RWS publications, 1994.
69.
KamalKSinhaM. Development of a hierarchical model for significant barriers in grinding technology using integrated AHP-TISM approach. Int J Prod Qual Manag2021. DOI: 10.1504/IJPQM.2021.10045859.
70.
KumarAPantS. Analytical hierarchy process for sustainable agriculture: an overview. MethodsX2023; 10: 101954. DOI: 10.1016/j.mex.2022.101954.
71.
KumarVChoudharyAK. A hybrid response surface methodology and multi-criteria decision making model to investigate the performance and emission characteristics of a diesel engine fueled with phenolic antioxidant additive and biodiesel blends. J Energy Resour Technol2023; 145(9): 092302. DOI: 10.1115/1.4056939.
72.
SinghVKumarVSinghVB. A hybrid novel fuzzy AHP-Topsis technique for selecting parameter-influencing testing in software development. Decision Analytics Journal2023; 6: 100159. DOI: 10.1016/j.dajour.2022.100159.
73.
Raja ShekarPMathewA. Assessing groundwater potential zones and artificial recharge sites in the monsoon-fed Murredu river basin, India: an integrated approach using GIS, AHP, and Fuzzy-AHP. Groundwater for Sustainable Development2023; 23: 100994. DOI: 10.1016/j.gsd.2023.100994.
74.
PathakSKSharmaVChouguleSS, et al.Prioritization of barriers to the development of renewable energy technologies in India using integrated Modified Delphi and AHP method. Sustain Energy Technol Assessments2022; 50: 101818. DOI: 10.1016/j.seta.2021.101818.
75.
MehraKSGoelVSinghS, et al.Experimental investigation of emission characteristics of CI engine using biodiesel-diesel blends and best fuel selection: an AHP-TOPSIS approach. Mater Today Proc2023; 237. DOI: 10.1016/j.matpr.2023.02.237.
76.
DiakoulakiDGeorgeMPapayannakisL. Determining objective weights in multiple criteria problems: the critic method. Comput Oper Res1995; 22(7): 763–770. DOI: 10.1016/0305-0548(94)00059-H.
77.
ZhongSChenYMiaoY. Using improved CRITIC method to evaluate thermal coal suppliers. Sci Rep2023; 13(1): 195. DOI: 10.1038/s41598-023-27495-6.
78.
WuH-WJinZZhangJ. Urban rail transit operation safety evaluation based on an improved CRITIC method and cloud model. Journal of Rail Transport Planning & Management2020; 16: 100206. DOI: 10.1016/j.jrtpm.2020.100206.
79.
AkramMZahidSDeveciM. Enhanced CRITIC-REGIME method for decision making based on Pythagorean fuzzy rough number. Expert Syst Appl2024; 238: 122014. DOI: 10.1016/j.eswa.2023.122014.
80.
AytekinAOkothBOKorucukS, et al.Critical success factors of lean six sigma to select the most ideal critical business process using q-ROF CRITIC-ARAS technique: case study of food business. Expert Syst Appl2023; 224: 120057. DOI: 10.1016/j.eswa.2023.120057.
81.
IrikM. Specific character of objective methods for determining weights of criteria in MCDM problems: entropy, CRITIC and SD. Decision Making: Applications in Management and Engineering2021; 4(2): 76–105.
82.
Karel BrauersWKazimieras ZavadskasE. The MOORA method and its application to privatization in a transition economy. Control Cybern2006; 35(2): 445–469.
83.
Ghaemi-ZadehNEghbali-ZarchM. Evaluation of business strategies based on the financial performance of the corporation and investors' behavior using D-CRITIC and fuzzy MULTI-MOORA techniques: a real case study. Expert Syst Appl2024; 247: 123183. DOI: 10.1016/j.eswa.2024.123183.
84.
RajPBijuPLDeepanrajB, et al.Optimizing the machining conditions in turning hybrid aluminium nanocomposites adopting teaching–learning based optimization and MOORA technique. Int J Interact Des Manuf2023; 18: 1–13. DOI: 10.1007/s12008-023-01450-1.
85.
De SmetY. PROMETHEE methods. In: Multiple criteria decision analysis: state of the art surveys, 2016, pp. 187–219.
86.
Jean-PierreBVinckeP. Note—a preference ranking organisation method: (the PROMETHEE method for multiple criteria decision-making). Manag Sci1985; 31(6): 647–656. DOI: 10.1287/mnsc.31.6.647.
87.
SotiropoulouKFVavatsikosAPBotsarisPN. A hybrid AHP-PROMETHEE II onshore wind farms multicriteria suitability analysis using kNN and SVM regression models in northeastern Greece. Renew Energy2024; 221: 119795. DOI: 10.1016/j.renene.2023.119795.
88.
AkramMZahidKKahramanC. A PROMETHEE based outranking approach for the construction of Fangcang shelter hospital using spherical fuzzy sets. Artif Intell Med2023; 135: 102456. DOI: 10.1016/j.artmed.2022.102456.
89.
ZafarFSaleemMSAbdul MajeedI, et al.A new rough PROMETHEE approach for the evaluation of potential failure modes and their effects in a general anesthesia process. Soft Comput2024; 28(4): 3449–3463. DOI: 10.1007/s00500-023-08675-y.
90.
JananiKMohanrasuSSKashkynbayevA, et al.Minkowski distance measure in fuzzy PROMETHEE for ensemble feature selection. Math Comput Simulat2024; 222: 264–295. DOI: 10.1016/j.matcom.2023.08.027.
91.
SinghAGuptaAMehraA. Best criteria selection based PROMETHEE II method. Opsearch2021; 58: 160–180. DOI: 10.1007/s12597-020-00464-7.
92.
GurusamyPBhattacharjeeBDuttaH, et al.Study of microstructural, machining and tribological behaviour of AA-6061/SiC MMC fabricated through the squeeze casting method and optimized the machining parameters by using standard deviation-PROMETHEE technique. Silicon2024; 16(2): 675–686. DOI: 10.1007/s12633-023-02707-w.
93.
Nilay YücenurGIpekçiA. SWARA/WASPAS methods for a marine current energy plant location selection problem. Renew Energy2021; 163: 1287–1298. DOI: 10.1016/j.renene.2020.08.131.
94.
GoelVDwivediAChoudharyAK. Parametric optimization of hybrid artificial roughness used in solar air heaters using multiple criteria decision making techniques. Proc Inst Mech Eng A J Power Energy2023; 237(8): 09576509231183037. DOI: 10.1177/09576509231183037.
95.
de BordaJC. M'emoire sur les' elections au scrutin. Histoire de l'Acad'emie Royale des Sciences, 1781.
96.
CookWDLawrenceMS. On the Borda-Kendall consensus method for priority ranking problems. Manag Sci1982; 28(6): 621–637. DOI: 10.1287/mnsc.28.6.621.
97.
ArhurHC. A reasonable social welfare function. In: Seminar on applications of mathematics to social sciences. Ann Arbor: University of Michigan, 1951.
98.
Hossein RabieeATahmasbiVQasemiM. Experimental evaluation, modeling and sensitivity analysis of temperature and cutting force in bone micro-milling using support vector regression and EFAST methods. Eng Appl Artif Intell2023; 120: 105874. DOI: 10.1016/j.engappai.2023.105874.
99.
FengJXuSXLiM. A novel multi-criteria decision-making method for selecting the site of an electric-vehicle charging station from a sustainable perspective. Sustain Cities Soc2021; 65: 102623. DOI: 10.1016/j.scs.2020.102623.
100.
Singh MehraKGoelVKumarR. An integrated multi-attribute decision framework for sustainability assessment of renewable diesel fuel production pathways. Energy Convers Manag2024; 309: 118461. DOI: 10.1016/j.enconman.2024.118461.
101.
YagmahanBYılmazH. An integrated ranking approach based on group multi-criteria decision making and sensitivity analysis to evaluate charging stations under sustainability. Environ Dev Sustain2023; 25(1): 96–121. DOI: 10.1007/s10668-021-02044-1.
102.
ChenP. Effects of the entropy weight on TOPSIS. Expert Syst Appl2021; 168: 114186. DOI: 10.1016/j.eswa.2020.114186.
103.
DwivediAGoelVPathakSK, et al.Prioritization of potential barriers to the implementation of solar drying techniques using MCDM tools: a case study and mapping in India. Sol Energy2023; 253: 199–218. DOI: 10.1016/j.solener.2023.02.030.
