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

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.

[1]  Sungjin Park,et al.  Battery cell arrangement and heat transfer fluid effects on the parasitic power consumption and the cell temperature distribution in a hybrid electric vehicle , 2013 .

[2]  Kemper Lewis,et al.  Integrating linear physical programming within collaborative optimization for multiobjective multidisciplinary design optimization , 2005 .

[3]  Lei Wang,et al.  Review on thermal management systems using phase change materials for electronic components, Li-ion batteries and photovoltaic modules , 2014 .

[4]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[5]  H. Laborit,et al.  [Experimental study]. , 1958, Bulletin mensuel - Societe de medecine militaire francaise.

[6]  Zhonghao Rao,et al.  A review of power battery thermal energy management , 2011 .

[7]  Yann Chamaillard,et al.  Optimal Energy Management Strategy including Battery Health through Thermal Management for Hybrid Vehicles , 2013 .

[8]  Wei Sun,et al.  Multidisciplinary design optimization of tunnel boring machine considering both structure and control parameters under complex geological conditions , 2016 .

[9]  Arun S. Mujumdar,et al.  Thermal–electrochemical model for passive thermal management of a spiral-wound lithium-ion battery , 2012 .

[10]  John E. Renaud,et al.  Response surface based, concurrent subspace optimization for multidisciplinary system design , 1996 .

[11]  Ibrahim Dincer,et al.  Modeling of passive thermal management for electric vehicle battery packs with PCM between cells , 2014 .

[12]  Amir Fartaj,et al.  Numerical Investigation of Active and Passive Cooling Systems of a Lithium-Ion Battery Module for Electric Vehicles , 2016 .

[13]  John E. Dennis,et al.  Problem Formulation for Multidisciplinary Optimization , 1994, SIAM J. Optim..

[14]  Francesco Colella,et al.  Thermal Management Modeling for Avoidance of Thermal Runaway Conditions in Lithium-Ion Batteries , 2014 .

[15]  Joaquim R. R. A. Martins,et al.  Extensions to the design structure matrix for the description of multidisciplinary design, analysis, and optimization processes , 2012, Structural and Multidisciplinary Optimization.

[16]  Richard J. Beckman,et al.  A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.

[17]  Martin D. Buhmann,et al.  Radial Basis Functions: Theory and Implementations: Preface , 2003 .

[18]  Xiaoping Li,et al.  State-of-charge estimation for battery management system using optimized support vector machine for regression , 2014 .

[19]  Roger Woodard,et al.  Interpolation of Spatial Data: Some Theory for Kriging , 1999, Technometrics.

[20]  A. Greco,et al.  A theoretical and computational study of lithium-ion battery thermal management for electric vehicles using heat pipes , 2014 .

[21]  I. Sobol.  On the distribution of points in a cube and the approximate evaluation of integrals , 1967 .

[22]  Choi Yong-Seok,et al.  Prediction of thermal behaviors of an air-cooled lithium-ion battery system for hybrid electric vehicles , 2014 .

[23]  Kamran Behdinan,et al.  Evaluation of Multidisciplinary Optimization Approaches for Aircraft Conceptual D esign , 2004 .

[24]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[25]  G. Box.  The Exploration and Exploitation of Response Surfaces: Some General Considerations and Examples , 1954 .

[26]  M. Verbrugge,et al.  Degradation of lithium ion batteries employing graphite negatives and nickel-cobalt-manganese oxide + spinel manganese oxide positives: Part 1, aging mechanisms and life estimation , 2014 .

[27]  Samveg Saxena,et al.  Quantifying electric vehicle battery degradation from driving vs. vehicle-to-grid services , 2016 .

[28]  Jiateng Zhao,et al.  Investigation of power battery thermal management by using mini-channel cold plate , 2015 .

[29]  A. Messac,et al.  Adaptive Hybrid Surrogate Modeling for Complex Systems , 2013 .

[30]  A. Messac,et al.  Predictive quantification of surrogate model fidelity based on modal variations with sample density , 2015 .

[31]  Heesung Park,et al.  A design of air flow configuration for cooling lithium ion battery in hybrid electric vehicles , 2013 .

[32]  Ilan Kroo,et al.  Implementation and Performance Issues in Collaborative Optimization , 1996 .

[33]  Michael Hinterberger,et al.  Simulative method for determining the optimal operating conditions for a cooling plate for lithium-ion battery cell modules , 2014 .

[34]  Lip Huat Saw,et al.  Numerical analyses on optimizing a heat pipe thermal management system for lithium-ion batteries during fast charging , 2015 .

[35]  Suresh G. Advani,et al.  Experimental study of an air-cooled thermal management system for high capacity lithium–titanate batteries , 2012 .

[36]  A. Pesaran,et al.  A parametric study on thermal management of an air-cooled lithium-ion battery module for plug-in hybrid electric vehicles , 2013 .

[37]  Rodrigo Palma-Behnke,et al.  Multi-objective optimal design of lithium-ion battery packs based on evolutionary algorithms , 2014 .

[38]  Hongguang Sun,et al.  Development of cooling strategy for an air cooled lithium-ion battery pack , 2014 .

[39]  A. Messac,et al.  Concurrent Surrogate Model Selection (COSMOS) Based on Predictive Estimation of Model Fidelity , 2014, DAC 2014.

[40]  Yong Zhang,et al.  Uniform Design: Theory and Application , 2000, Technometrics.

[41]  Jaroslaw Sobieszczanski-Sobieski,et al.  Bilevel Integrated System Synthesis with Response Surfaces , 2000 .

[42]  Yuwen Zhang,et al.  Thermal management improvement of an air-cooled high-power lithium-ion battery by embedding metal foam , 2015 .

[43]  Mohammad Rezwan Khan,et al.  FEASIBILITY STUDY AND TECHNO-ECONOMIC OPTIMIZATION MODEL FOR BATTERY THERMAL MANAGEMENT SYSTEM , 2014 .

[44]  C. R. Rao,et al.  Factorial Experiments Derivable from Combinatorial Arrangements of Arrays , 1947 .

[45]  Ibrahim Dincer,et al.  Exergy analysis and optimization of a thermal management system with phase change material for hybrid electric vehicles , 2014 .