scholarly journals Conjugate Heat Transfer Analysis of Thermal Management of a Li-Ion Battery Pack

2017 ◽  
Vol 15 (1) ◽  
Author(s):  
Divya Chalise ◽  
Krishna Shah ◽  
Ravi Prasher ◽  
Ankur Jain
Keyword(s):  
Heat Transfer ◽  
Thermal Management ◽  
Conjugate Heat Transfer ◽  
Battery Pack ◽  
Heat Transfer Analysis ◽  
Li Ion Battery ◽  
Analytical Technique ◽  
Trade Offs ◽  
Battery Packs ◽  
Li Ion

Thermal management of Li-ion battery packs is a critical technological challenge that directly impacts safety and performance. Removal of heat generated in individual Li-ion cells into the ambient is a considerably complicated problem involving multiple heat transfer modes. This paper develops an iterative analytical technique to model conjugate heat transfer in coolant-based thermal management of a Li-ion battery pack. Solutions for the governing energy conservation equations for thermal conduction and convection are derived and coupled with each other in an iterative fashion to determine the final temperature distribution. The analytical model is used to investigate the dependence of the temperature field on various geometrical and material parameters. This work shows that the coolant flowrate required for effective cooling can be reduced significantly by improving the thermal conductivity of individual Li-ion cells. Further, this work helps understand key thermal–electrochemical trade-offs in the design of thermal management for Li-ion battery packs, such as the trade-off between temperature rise and energy storage density in the battery pack.

scholarly journals Multi-Physics Equivalent Circuit Models for a Cooling System of a Lithium Ion Battery Pack

Batteries ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. 44 ◽  
Author(s):  
Takumi Yamanaka ◽  
Daiki Kihara ◽  
Yoichi Takagishi ◽  
Tatsuya Yamaue
Keyword(s):  
Heat Transfer ◽  
Equivalent Circuit ◽  
Thermal Management ◽  
Flow Rate ◽  
3D Model ◽  
Management Systems ◽  
Li Ion Battery ◽  
Liquid Cooling ◽  
Battery Packs ◽  
Li Ion

Lithium (Li)-ion battery thermal management systems play an important role in electric vehicles because the performance and lifespan of the batteries are affected by the battery temperature. This study proposes a framework to establish equivalent circuit models (ECMs) that can reproduce the multi-physics phenomenon of Li-ion battery packs, which includes liquid cooling systems with a unified method. We also demonstrate its utility by establishing an ECM of the thermal management systems of the actual battery packs. Experiments simulating the liquid cooling of a battery pack are performed, and a three-dimensional (3D) model is established. The 3D model reproduces the heat generated by the battery and the heat transfer to the coolant. The results of the 3D model agree well with the experimental data. Further, the relationship between the flow rate and pressure drop or between the flow rate and heat transfer coefficients is predicted with the 3D model, and the data are used for the ECM, which is established using MATLAB Simulink. This investigation confirmed that the ECM’s accuracy is as high as the 3D model even though its computational costs are 96% lower than the 3D model.

Internal Resistance Based Assessment Model for the Degradation of Li-Ion Battery Pack

2020 ◽  
Author(s):  
Iffandya Popy Wulandari ◽  
Min-Chun Pan
Keyword(s):  
Internal Resistance ◽  
Remaining Useful Life ◽  
Assessment Model ◽  
Battery Pack ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Battery Management System ◽  
Li Ion Battery ◽  
Battery Packs ◽  
Li Ion

Abstract As one pioneer means for energy storage, Li-ion battery packs have a complex and critical issue about degradation monitoring and remaining useful life estimation. It induces challenges on condition characterization of Li-ion battery packs such as internal resistance (IR). The IR is an essential parameter of a Li-ion battery pack, relating to the energy efficiency, power performance, degradation, and physical life of the li-ion battery pack. This study aims to obtain reliable IR through applying an evaluation test that acquires data such as voltage, current, and temperature provided by the battery management system (BMS). Additionally, this paper proposes an approach to predict the degradation of Li-ion battery pack using support vector regression (SVR) with RBF kernel. The modeling approach using the relationship between internal resistance, different SOC levels 20%–100%, and cycle at the beginning of life 1 cycle until cycle 500. The data-driven method is used here to achieve battery life prediction.based on internal resistance behavior in every period using supervised machine learning, SVR. Our experiment result shows that the internal resistance was increasing non-linear, approximately 0.24%, and it happened if the cycle rise until 500 cycles. Besides, using SVR algorithm, the quality of the fitting was evaluated using coefficient determination R2, and the score is 0.96. In the proposed modeling process of the battery pack, the value of MSE is 0.000035.

scholarly journals Scheduled Pre-Heating of Li-Ion Battery Packs for Balanced Temperature and State-of-Charge Distribution

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2212
Author(s):  
Hien Vu ◽  
Donghwa Shin
Keyword(s):  
Thermal Characteristics ◽  
Lithium Ion ◽  
State Of Charge ◽  
Battery Pack ◽  
Li Ion Battery ◽  
Internal Heating ◽  
Heating Efficiency ◽  
Battery Packs ◽  
Unit Cells ◽  
Li Ion

Lithium-ion batteries exhibit significant performance degradation such as power/energy capacity loss and life cycle reduction in low-temperature conditions. Hence, the Li-ion battery pack is heated before usage to enhance its performance and lifetime. Recently, many internal heating methods have been proposed to provide fast and efficient pre-heating. However, the proposed methods only consider a combination of unit cells while the internal heating should be implemented for multiple groups within a battery pack. In this study, we investigated the possibility of timing control to simultaneously obtain balanced temperature and state of charge (SOC) between each cell by considering geometrical and thermal characteristics of the battery pack. The proposed method schedules the order and timing of the charge/discharge period for geometrical groups in a battery pack during internal pre-heating. We performed a pack-level simulation with realistic electro-thermal parameters of the unit battery cells by using the mutual pulse heating strategy for multi-layer geometry to acquire the highest heating efficiency. The simulation results for heating from −30 ∘ C to 10 ∘ C indicated that a balanced temperature-SOC status can be achieved via the proposed method. The temperature difference can be decreased to 0.38 ∘ C and 0.19% of the SOC difference in a heating range of 40 ∘ C with only a maximum SOC loss of 2.71% at the end of pre-heating.

Development of a Two-Dimensional Thermal Model for Li-Ion Battery Pack With Experimental Validation

2019 ◽  
Vol 12 (2) ◽  
Author(s):  
Haoting Wang ◽  
Ning Liu ◽  
Lin Ma
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Experimental Validation ◽  
Computational Cost ◽  
Battery Pack ◽  
Two Dimensional ◽  
Li Ion Battery ◽  
New Approach ◽  
Battery Packs ◽  
Li Ion

Abstract This paper reports the development of a two-dimensional two states (2D2S) model for the analysis of thermal behaviors of Li-ion battery packs and its experimental validation. This development was motivated by the need to fill a niche in our current modeling capabilities: the need to analyze 2D temperature (T) distributions in large-scale battery packs in real time. Past models were predominately developed to either provide detailed T information with high computational cost or provide real-time analysis but only 1D lumped T information. However, the capability to model 2D T field in real time is desirable in many applications ranging from the optimal design of cooling strategies to onboard monitoring and control. Therefore, this work developed a new approach to provide this desired capability. The key innovations in our new approach involved modeling the whole battery pack as a complete thermal-fluid network and at the same time calculating only two states (surface and core T) for each cell. Modeling the whole pack as a complete network captured the interactions between cells and enabled the accurate resolution of the 2D T distribution. Limiting the calculation to only the surface and core T controlled the computational cost at a manageable level and rendered the model suitable for packs at large scale with many cells.

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