| Author: | Guo, Zengjia |
| Title: | The numerical investigation and optimization of battery thermal management system |
| Advisors: | Ni, Meng (BRE) |
| Degree: | Ph.D. |
| Year: | 2024 |
| Subject: | Lithium ion batteries Electric vehicles -- Batteries Storage batteries -- Design and construction Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Building and Real Estate |
| Pages: | xxxix, 182 pages : color illustrations |
| Language: | English |
| Abstract: | To date, the electric vehicle (EV) has become an ideal option for both private and public transportation due to the low pollutant emission and noise reduction. To ensure efficient and safe operation of Li-ion batteries inside the EVs, it is critical to control both the working temperature and temperature difference in the batteries. This is because that inadequate operating temperature can easily lead to performance reduction, capacity deterioration, and even thermal runaway. Therefore, the effective battery thermal management system (BTMS) is becoming an indispensable part of Li-ion battery. Thus, in this thesis, the significant strides are paid in the BTMS structural design and optimization, novel BTMS numerical model development and multi-variables global optimization processes of BTMS to ensure the efficient, stable and safe operation of battery pack even during the long-term cycling. In this thesis, a new BTMS is proposed by employing a mini-channel cold plate with pin fins. The performance of BTMS is evaluated by a 3D numerical model. The heat transfer characteristics, pressure loss and flow structure in the BTMS are analyzed, and the overall performance of BTMS is evaluated using efficiency index (EI) which considers both heat transfer performance and pressure loss. It is found that pin fins can improve the heat transfer performance of BTMS with acceptable pressure loss. Therefore, the EI of BTMS with pin fins is always greater than 1. The effects of pin fins arrangement and pin fins layout direction on the BTMS working performance are also systematically studied. The results show that BTMS with 4 x 3 staggered arranged pin fins and vertically arranged pin fins provide the best overall working performance. Furthermore, BTMSs with different pin fin shapes, such as square pin fins (SPFs), circular pin fins (CPFs) and ellipse pin fins (EPFs), also show the totally different heat transfer performance and pressure loss. CPFs can be the best choice for BTMS when pin fins are vertically arranged, due to the favorable cooling performance and acceptable pressure loss caused by CPFs. However, SPFs are recommended for BTMS because of the greater enhancement of heat transfer when pin fins are horizontally arranged. Battery aging is critical for the batteries in electric vehicles, significantly affecting both thermal characteristics and electrochemical performance. Designing BTMS without considering the effect of battery aging may cause the BTMS to be unable to provide effective thermal manage for the aged battery pack after long term operation, although the BTMS can achieve a favorable cooling performance at beginning. Therefore, a more realistic and generic numerical model combining the electrochemistry, capacity fade, heat transfer and fluid dynamics is developed for BTMS design and optimization. The thermal behaviors and electrochemical characteristics in different working cycles of liquid-based BTMS, nanofluid-based BTMS, heat pipe (HP)-based BTMS and micro heat pipe (MHP)-based BTMS are analyzed and compared. It is found that BTMSs are inadequate in addressing thermal issues that arise in aged battery pack, primarily because of the increased total heat generation rate resulting from battery aging effect. For liquid-based BTMS, BTMS with Y direction mini-channels always provides more effective cooling to batteries to achieve good electrochemical performance with acceptable higher pressure loss during battery cycling. Moreover, optimizing mini-channel arrangement and arranging pin fins can be helpful to provide effective thermal manage, achieve higher average potential and prevent capacity fade, although the pressure loss is also higher. As for nanofluid-based BTMS, BTMS with water-based nanofluid achieves the best performance, and the cooling performance of BTMS increases with increasing volume fraction of nanoparticles, but penalized by a higher pressure loss. It is also found that nanoparticle shapes have a significant effect on battery thermal behaviors and electrochemical performance. With brick-shaped nanoparticles, BTMS well cools the battery pack and reduces the battery capacity fade. For comparison, BTMS with spherical-shaped nanoparticles achieves the lowest pressure loss with providing favorable thermal manage for battery pack. As compared to HP-BTMS, MHP-BTMS achieves a better performance in controlling battery temperature and preventing the capacity fade, leading to a higher average potential after long term operation. Besides, the results show that MHP-BTMS with X direction MHP, non-equidistant arrangements and cold plates can effectively control the battery temperature and prevent the SEI formation and capacity decay. Finally, a new multi-variables global optimization framework which combined multi-physics modeling, deep neural network and multi-objective genetic algorithm is proposed, aiming to ensure the dependable and enduring performance of battery pack. During the multi-variables global optimization process, the dataset with inputs and outputs is generated from the numerical simulations based on the above-proposed model. The dataset is then used to train the deep neural network (DNN) model, which can quickly and accurately predict the battery thermal characteristics and electrochemical performance. Finally, multi-objective genetic algorithm (MOGA) based on DNN model is conducted by using NSGA-II and TOPSIS to obtain the optimal structure parameters, while satisfying various demands related to battery thermal behaviors, pressure loss, and electrochemical performance. The multi-variables global optimization is conducted based on the nanofluid-based BTMS and MHP-BTMS under the different combinations of weighted coefficients and optimization strategies. These optimized BTMSs demonstrate the ability to strike a balance between battery thermal behaviors, pressure loss, and electrochemical performance, and all the optimization schemes can maintain the desired temperature requirements even during long-term operation. In addition, it is demonstrated that the weighted coefficients and optimization strategies in this novel framework can be changed according to the actual needs in engineering applications. |
| Rights: | All rights reserved |
| Access: | open access |
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