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Surrogate based multidisciplinary design optimization of lithium-ion battery thermal management system in electric vehicles

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Abstract

A battery thermal management system (BTMS) is a complex system that uses various heat removal and temperature control strategies to keep battery packs at optimal thermal conditions, thereby improving the lifetime and safety of lithium-ion battery packs in electric vehicles (EVs). However, an optimal design of BTMS is still challenging, due to its large number of sub-systems and/or disciplines involved. To address this challenge, an air-based BTMS is hierarchically decoupled into four sub-systems and/or sub-disciplines in this paper, including the battery thermodynamics, fluid dynamics, structure, and lifetime model. A high-fidelity computational fluid dynamics (CFD) model is first developed to analyze the effects of key design variables (i.e., heat flux, mass flow rate, and passage spacing size) on the performance of BTMS. Aiming to perform the multidisciplinary design optimization (MDO) of BTMS based on the high-fidelity CFD model, surrogate models are developed using an automatic model selection method, the Concurrent Surrogate Model Selection (COSMOS). The surrogate models represent the BTMS performance metrics (i.e., the pressure difference between air inlet and outlet, the maximum temperature difference among battery cells, and the average temperature of the battery pack) as functions of key design parameters. The objectives are to maximize the battery lifetime and to minimize the battery volume, the fan’s power, and the temperature difference among different cells. The MDO results show that the lifetime of the battery module is significantly improved by reducing the temperature difference and battery volume.

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References

  • Bahiraei F, Fartaj A, Nazri GA (2016) Numerical investigation of active and passive cooling systems of a lithium-ion battery module for electric vehicles. Tech. rep., SAE Technical Paper

  • Box GE (1954) The exploration and exploitation of response surfaces: some general considerations and examples. Biometrics 10(1):16–60

    Article  Google Scholar 

  • Buhmann MD (2003) Radial basis functions: theory and implementations. Camb Monogr Appl Comput Math 12:147–165

    MATH  MathSciNet  Google Scholar 

  • Choi YS, Kang DM (2014) Prediction of thermal behaviors of an air-cooled lithium-ion battery system for hybrid electric vehicles. J Power Sources 270:273–280

    Article  Google Scholar 

  • Chowdhury S, Mehmani A, Messac A (2014) Concurrent surrogate model selection (cosmos) based on predictive estimation of model fidelity. In: ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, pp V02BT03A026–V02BT03A026

  • Cramer EJ, Dennis J Jr, Frank PD, Lewis RM, Shubin GR (1994) Problem formulation for multidisciplinary optimization. SIAM J Optim 4(4):754–776

    Article  MATH  MathSciNet  Google Scholar 

  • Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  • Dennis J, Lewis RM (1994) Problem formulations and other optimization issues in multidisciplinary optimization, Colordao, vol, Colorado Springs

  • Fan L, Khodadadi J, Pesaran A (2013) A parametric study on thermal management of an air-cooled lithium-ion battery module for plug-in hybrid electric vehicles. J Power Sources 238:301–312

    Article  Google Scholar 

  • Fang KT, Lin DK, Winker P, Zhang Y (2000) Uniform design: theory and application. Technometrics 42(3):237–248

    Article  MATH  MathSciNet  Google Scholar 

  • Gage RBP, lan Sobieski IK (1996) Implementation and performance issues in collaborative optimization

  • GArtés D (2012) Baterías de coches eléctricos e híbridos, hoy [estado de la tecnología del automóvil]. Website. http://www.diariomotor.com/tecmovia/2012/03/14/baterias-de-coches-electricos-e-hibridos-hoy-estado-de-la-tecnologia-del-automovil

  • Giuliano MR, Prasad AK, Advani SG (2012) Experimental study of an air-cooled thermal management system for high capacity lithium–titanate batteries. J Power Sources 216:345–352

    Article  Google Scholar 

  • Greco A, Cao D, Jiang X, Yang H (2014) A theoretical and computational study of lithium-ion battery thermal management for electric vehicles using heat pipes. J Power Sources 257:344–355

    Article  Google Scholar 

  • Hu J, Hu J, Lin H, Li X, Jiang C, Qiu X, Li W (2014) State-of-charge estimation for battery management system using optimized support vector machine for regression. J Power Sources 269:682–693

    Article  Google Scholar 

  • Huo Y, Rao Z, Liu X, Zhao J (2015) Investigation of power battery thermal management by using mini-channel cold plate. Energy Convers Manag 89:387–395

    Article  Google Scholar 

  • Javani N, Dincer I, Naterer G, Rohrauer G (2014a) Modeling of passive thermal management for electric vehicle battery packs with pcm between cells. Appl Therm Eng 73(1):307–316

    Article  Google Scholar 

  • Javani N, Dincer I, Naterer G, Yilbas B (2014b) Exergy analysis and optimization of a thermal management system with phase change material for hybrid electric vehicles. Appl Therm Eng 64(1):471–482

    Article  Google Scholar 

  • Khan MR, Nielsen MP, Kær SK (2014) Feasibility study and techno-economic optimization model for battery thermal management system. In: Proceedings of the 55th conference on simulation and modelling (SIMS 55), modelling, simulation and optimization, 21-22 october 2014, vol 108. Linköping University Electronic Press, Aalborg, pp 16–27

    Google Scholar 

  • Lambe AB, Martins JR (2012) Extensions to the design structure matrix for the description of multidisciplinary design, analysis, and optimization processes. Struct Multidiscip Optim 46(2):273–284

    Article  MATH  Google Scholar 

  • Ling Z, Zhang Z, Shi G, Fang X, Wang L, Gao X, Fang Y, Xu T, Wang S, Liu X (2014) Review on thermal management systems using phase change materials for electronic components, li-ion batteries and photovoltaic modules. Renew Sust Energ Rev 31:427– 438

    Article  Google Scholar 

  • McAllister CD, Simpson TW, Hacker K, Lewis K, Messac A (2005) Integrating linear physical programming within collaborative optimization for multiobjective multidisciplinary design optimization. Struct Multidiscip Optim 29(3):178–189

    Article  Google Scholar 

  • McKay MD, Beckman RJ, Conover WJ (2000) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 42(1):55–61

    Article  MATH  Google Scholar 

  • Mehmani A, Chowdhury S, Messac A (2015) Predictive quantification of surrogate model fidelity based on modal variations with sample density. Struct Multidiscip Optim 52(2):353–373

    Article  Google Scholar 

  • Mohammadian SK, Rassoulinejad-Mousavi SM, Zhang Y (2015) Thermal management improvement of an air-cooled high-power lithium-ion battery by embedding metal foam. J Power Sources 296:305–313

    Article  Google Scholar 

  • Padovani TM, Debert M, Colin G, Chamaillard Y (2013) Optimal energy management strategy including battery health through thermal management for hybrid vehicles. IFAC Proc Vol 46(21):384–389

    Article  Google Scholar 

  • Park H (2013) A design of air flow configuration for cooling lithium ion battery in hybrid electric vehicles. J Power Sources 239:30–36

    Article  Google Scholar 

  • Park S, Jung D (2013) Battery cell arrangement and heat transfer fluid effects on the parasitic power consumption and the cell temperature distribution in a hybrid electric vehicle. J Power Sources 227:191–198

    Article  Google Scholar 

  • Perez RE, Liu HH, Behdinan K (2004) Evaluation of multidisciplinary optimization approaches for aircraft conceptual design. In: AIAA/ISSMO multidisciplinary analysis and optimization conference, Albany, NY

  • Ponchaut N, Colella F, Somandepalli V, Stern M (2014) Thermal management modeling for thermal runaway avoidance in lithium-ion batteries

  • Rao CR (1947) Factorial experiments derivable from combinatorial arrangements of arrays. Suppl J R Stat Soc 9(1):128–139

    Article  MATH  MathSciNet  Google Scholar 

  • Rao Z, Wang S (2011) A review of power battery thermal energy management. Renew Sust Energ Rev 15 (9):4554–4571

    Article  Google Scholar 

  • Sellar R, Batill S, Renaud J (1996) Response surface based, concurrent subspace optimization for multidisciplinary system design. AIAA Pap 714:1996

    Google Scholar 

  • Severino B, Gana F, Palma-Behnke R, Estévez P A, Calderón-Muñoz WR, Orchard ME, Reyes J, Cortés M (2014) Multi-objective optimal design of lithium-ion battery packs based on evolutionary algorithms. J Power Sources 267:288–299

    Article  Google Scholar 

  • Smith J, Hinterberger M, Hable P, Koehler J (2014) Simulative method for determining the optimal operating conditions for a cooling plate for lithium-ion battery cell modules. J Power Sources 267:784–792

    Article  Google Scholar 

  • Sobieszczanski-Sobieski J, Agte JS, Sandusky RR (2000) Bilevel integrated system synthesis. AIAA J 38(1):164–172

    Article  Google Scholar 

  • Sobol’ I M (1967) On the distribution of points in a cube and the approximate evaluation of integrals. Zhurnal Vychislitel’noi Matematiki i Matematicheskoi Fiziki 7(4):784–802

    MathSciNet  Google Scholar 

  • Somasundaram K, Birgersson E, Mujumdar AS (2012) Thermal–electrochemical model for passive thermal management of a spiral-wound lithium-ion battery. J Power Sources 203:84–96

    Article  Google Scholar 

  • Stein ML (2012) Interpolation of spatial data: some theory for kriging. Springer Science & Business Media

  • Sun H, Dixon R (2014) Development of cooling strategy for an air cooled lithium-ion battery pack. J Power Sources 272:404– 414

    Article  Google Scholar 

  • Sun W, Wang X, Wang L, Zhang J, Song X (2016) Multidisciplinary design optimization of tunnel boring machine considering both structure and control parameters under complex geological conditions. Struct Multidiscip Optim, 1–20

  • Vapnik VN, Vapnik V (1998) Statistical learning theory, vol 1. Wiley, New York

    MATH  Google Scholar 

  • Wang D, Coignard J, Zeng T, Zhang C, Saxena S (2016) Quantifying electric vehicle battery degradation from driving vs. vehicle-to-grid services. J Power Sources 332:193–203

    Article  Google Scholar 

  • Wang J, Purewal J, Liu P, Hicks-Garner J, Soukazian S, Sherman E, Sorenson A, Vu L, Tataria H, Verbrugge MW (2014) Degradation of lithium ion batteries employing graphite negatives and nickel–cobalt–manganese oxide+ spinel manganese oxide positives: Part 1, aging mechanisms and life estimation. J Power Sources 269:937–948

    Article  Google Scholar 

  • Ye Y, Saw LH, Shi Y, Tay AA (2015) Numerical analyses on optimizing a heat pipe thermal management system for lithium-ion batteries during fast charging. Appl Therm Eng 86:281–291

    Article  Google Scholar 

  • Zhang J, Chowdhury S, Zhang J, Messac A, Castillo L (2013) Adaptive hybrid surrogate modeling for complex systems. AIAA J 51(3):643–656

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the University of Texas at Dallas. The authors of Mao Li and Xiaobang Wang are supported by the China Scholarship Council.

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Authors and Affiliations

  1. Dalian University of Technology, Dalian, 116024, China

    Xiaobang Wang, Wei Sun & Xueguan Song

  2. The University of Texas at Dallas, Richardson, TX, 75080, USA

    Xiaobang Wang, Mao Li, Yuanzhi Liu & Jie Zhang

  3. Beijing Institute of Aerospace Testing Technology, Beijing, 100074, China

    Mao Li

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  1. Xiaobang Wang
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  2. Mao Li
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  3. Yuanzhi Liu
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  4. Wei Sun
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  5. Xueguan Song
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  6. Jie Zhang
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Correspondence to Jie Zhang.

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Wang, X., Li, M., Liu, Y. et al. Surrogate based multidisciplinary design optimization of lithium-ion battery thermal management system in electric vehicles. Struct Multidisc Optim 56, 1555–1570 (2017). https://doi.org/10.1007/s00158-017-1733-1

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  • DOI: https://doi.org/10.1007/s00158-017-1733-1

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