A comprehensive analysis and optimization process for an integrated liquid cooling plate for a prismatic lithium-ion battery module

Chen, Siqi; Peng, Xiongbin; Bao, Nengsheng; Garg, Akhil

doi:10.1016/j.applthermaleng.2019.04.089

Cited by 160 publications

(42 citation statements)

References 31 publications

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“…There are some influential parameters that affect the cooling performance of this structure, input parameters and objectives are listed as Table 4. 37…”

Section: Optimization Designmentioning

confidence: 99%

“…The temperature and pressure distribution of the orginal design is shown in Figure 8. 37 Considering the cooling performance and energy consumption, constraints of the optimization design are set as follows: (a) the maximum temperature is lower than 301 K; (b) the maximum temperature SD is lower than 0.01 K; and (c) the maximum pressure is lower than 50 Pa; the constraints formulas are expressed as Equation (24) as follows: Table 6 shows three candidate designs of the optimization, and maximum temperature can be effectively decreased lower than 301 K after optimization work; the temperature SD and maximum pressure can also be controlled within the desired range.…”

Section: Optimal Design Selection Based On the Objectivesmentioning

confidence: 99%

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A thermal‐structure coupled optimization study of lithium‐ion battery modules with mist cooling

Qian

2020

Int J Energy Res

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Lithium-ion battery packs' discharging results in significant heat generation, which leads to safety issues and negative impact during the application of electric vehicles. Previous studies focused more on the configurations and design of cell packs/modules with a cooling system to control the battery cell's temperature. However, temperature differences are more difficult to control to ensure thermal uniformity. Maximum pressure is a significant parameter that affects energy consumption and the cost of the cooling system. This study proposed a comprehensive approach to design an efficient mist cooling system, which considered both maximum temperature and temperature standard deviation. This design method generates candidate design points by Latin Hypercubes Sampling and design of experiments (DOEs); effects of some significant parameters of the cooling structure on the cooling efficiency are evaluated by sensitivity analysis. Surrogate models are then selected and optimized by the multi-objective optimization approach to acquire an optimal scheme of the mist cooling structure. The optimized results showed that the optimized mist cooling battery module has better thermal performance with a lower cost. This approach can be used in the battery pack design process to control the temperature rise and temperature distribution, and energy consumption of the cooling system.

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“…There are some influential parameters that affect the cooling performance of this structure, input parameters and objectives are listed as Table 4. 37…”

Section: Optimization Designmentioning

confidence: 99%

Section: Optimal Design Selection Based On the Objectivesmentioning

confidence: 99%

A thermal‐structure coupled optimization study of lithium‐ion battery modules with mist cooling

Qian

2020

Int J Energy Res

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“…Efficient thermal management maintains the maximum temperature within the appropriate range and ensures the uniform temperature distribution of a battery module, and energy consumption should also be considered. Chen et al proposed a comprehensive optimization design for a liquid cooling–based cooling structure equipped with minichannels; sensitivity analysis about structural parameters was included in this study. The above three objectives are reached through the candidate design selection.…”

Section: Introductionmentioning

confidence: 99%

An experimental investigation of liquid cooling scheduling for a battery module

et al. 2020

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Summary The thermal safety of electric vehicle battery modules attracts public concern; controlling the severe temperature rise and ensuring uniform temperature distribution are essential to addressing this problem. In this research, a liquid cooling‐based cooling structure equipped with minichannels is proposed to prevent a battery module's overheating. A novel cooling scheduling study is proposed to arrange the coolant flow rates at different cooling stages. The temperature rise, temperature difference, and energy consumption of all the cooling schedules are measured in experiments. Experimental findings indicate that appropriate cooling scheduling achieves the thermal objectives and reduces energy consumption through scheduling the coolant flow rate in the cooling process. A comprehensive cooling schedule selection is carried out to select the optimal cooling schedule with the highest cooling efficiency through evaluating both the thermal and energy consumption objective parameters under different discharging current rates (0.5C, 1C, and 1.5C). The optimal cooling schedule maintains the maximum temperature of the battery module within 26°C, 32°C, and 40°C under 0.5C, 1C, and 1.5C discharging current rates, respectively. Moreover, the temperature SD and the energy consumption of the liquid cooling‐based battery pack can be controlled within 3.5°C and 40 J, respectively.

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“…Also, based on 3D models, Javani, Ping, and Bai investigated the influence of phase change materials. The liquid cooling method was also studied based on 3D thermal models . Currently, the 3D models are the most popular for Li‐ion battery simulations.…”

Section: Introductionmentioning

confidence: 99%

“…The liquid cooling method was also studied based on 3D thermal models. 10,12,51,52 Currently, the 3D models are the most popular for Li-ion battery simulations. For all 3D models introduced above, the battery cells are commonly homogenized to one anisotropic block.…”

Section: Introductionmentioning

confidence: 99%

Simplification strategy research on hard‐cased Li‐ion battery for thermal modeling

Xifeng

Zeng²,

Hong-liang

et al. 2020

Int J Energy Res

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Summary Numerical simulation is widely used in research and design of the battery thermal management system (BTMS). Battery cells are commonly homogenized to a block for module or pack level modeling. However, how the homogenization method affects results is not proven. This paper works on a hard‐cased Li‐ion battery to find a proper simplification method. First, a detailed three‐dimensional thermal model (model A) is set up and validated by experimental data. Then, the other three models with different simplification strategies are proposed. They are model B (the cell is homogenized to a block), model C (the cell preserves only core region and housing case), and model D (based on model C, the thin insulation films are also considered). Take the results of model A as a reference, the calculation accuracy and efficiency are summarized. It is found that model B shows the best efficiency and is a proper choice for evaluation of temperature dynamics, while model D is more recommended when the internal temperature distribution of the battery is more concerned.

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A comprehensive analysis and optimization process for an integrated liquid cooling plate for a prismatic lithium-ion battery module

Cited by 160 publications

References 31 publications

A thermal‐structure coupled optimization study of lithium‐ion battery modules with mist cooling

A thermal‐structure coupled optimization study of lithium‐ion battery modules with mist cooling

An experimental investigation of liquid cooling scheduling for a battery module

Simplification strategy research on hard‐cased Li‐ion battery for thermal modeling

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