Experiments and Simulation on the Performance of a Liquid-Cooling Thermal Management System including Composite Silica Gel and Mini-Channel Cold Plates for a Battery Module

Abstract

Lithium batteries in the electric vehicles (EVs) reveal that the operating temperature and temperature uniformity within the battery pack significantly affect its performance. An efficient thermal management system is urgently needed to protect the battery module within suitable temperature range. In this study, the composite silica gel (CSG), coupled with cross-structure mini-channel cold plate (MCP) as the cooling system, has been proposed and applied in a battery module, which can provide a reliable method of controlling battery temperature with low energy consumption. The experimental and simulation results reveal that a composite silica gel-based liquid system can control the temperature below 45 °C and maintain the temperature difference within 2 °C at a 3C discharge rate. Besides, the CSG, coupled with the structure of reciprocal chiasma channels for the battery module, presents an optimum temperature-controlling performance among various cooling structures during the charge and discharge cycling process. This research is expected to provide significant insights into the designing and optimization of thermal management systems.

1. Introduction

With the growing challenge of pollution, it becomes more and more important to overcome the shortage of fossil energy. EVs and hybrid electric vehicles (HEVs) are both excellent alternatives to traditional vehicles that would significantly reduce the emission of greenhouse gas and other pollutants. Recently, the demands for EVs and HEVs are rapidly growing, and the lithium-ion battery has been applied extensively as the power source [1]. Compared to various kinds of batteries, the lithium-ion battery exhibits promising prospect due to its low self-discharging, long lifespan, high energy density and so on [2].

Nevertheless, the working performance and safety of the lithium-ion battery are rather susceptible to its working temperature, especially under extreme conditions, such as overheating, which makes it a bottleneck for further practical application of the lithium-ion battery [3]. According to Arrhenius law, the reaction rate of the electrochemical reaction increases exponentially with the rise of the temperature [4]. Meanwhile, as the discharge process goes on, the lithium-ion battery generates lots of heat that will lead to the rise of the operating temperature. If the heat fails to be dissipated in time, there will be damage to the batteries, even thermal runaway. For the lithium-ion battery, the optimal temperature range is 20 °C to 50 °C. When the temperature exceeds 50 °C, the capacity and lifespan begin to decrease, and the internal resistance will increase [5]. The decomposition of the battery diaphragm begins to happen under the operating temperature of 90 °C [6,7]. If the temperature keeps rising, the battery diaphragm will fully discompose and lead to internal short circuit, which will eventually lead to thermal runaway [8].

Therefore, an effective BTMS is very necessary to dissipate the heat generated by the battery module and to maintain the operating temperature within a reasonable range. So far, a lot of effort has been spent to explore effective BTMS by a large amount of researchers. Generally, BTMS can be categorized based on cooling methods, such as air-cooling, liquid-cooling, phase change material (PCM), heat pipe-based systems and combinations of multiple cooling strategies [9]. To design an effective BTMS, there are many factors that should be taken into consideration, such as volume constraint, installation and construction cost, cooling performance and so on.

PCM has the characteristic of absorbing a great amount of heat in the phase change process, which has attracted lots of attention. It was Al-Hallay [10] that first proposed the application of PCM into BTMS and found it to be effective. Wang et al. [11] investigated the paraffin/aluminum foam composite PCM; the results indicated that, by using aluminum foam, the melting process could be sped up, and the temperature uniformity would also improve. Aditya et al. [6] performed a numerical study of PCM-based BTMS. The results led to the conclusion that a minimum of a 4-mm thickness is necessary for effective temperature control of the battery cell. Babapoor et al. [12] added carbon fiber to PCM to enhance its heat transfer potentials and reduced the temperature rise by 45%. Peng et al. [13] proposed a novel hybrid battery thermal management system combining air-cooling with phase change material, and the maximum temperature and temperature difference were reduced by 16 and 1.2 °C under the 3C rate, respectively. Although PCM exhibits an outstanding performance in BTMS, there are still many problems that need to be solved, such as leakage, relative high cost, flame retardation and so on, which make it difficult to be applied in BTMS [14].

Air-cooling BTMS as the most traditional BTMS can be classified as passive and active cooling according to whether an external energy is involved. Air-cooling can also reduce the temperature rise of batteries, and the performance can be improved through rational design, such as increasing the volume flow rate, attaching fins, changing the distribution of the batteries and so on. Chen et al. [15,16] developed a method to construct a symmetrical air-cooling system with uneven cell-spacing in order to achieve better cooling performance that reduces maximum temperature difference and energy consumption by at least 43% and 33%, respectively. They also developed a structure optimization for air cooling BTMS and optimized the structure of parallel air-cooled BTMS; the comparison results revealed that the model was in good accordance with the reality. Li et al. [17] proposed a BTMS using a double silica cooling plate, coupled with copper mesh, as an air cooling system that exhibited excellent performance. Saw et al. [18] investigated the air- cooling system for 38,120 battery modules by computational fluid dynamic (CFD) analysis, providing a simple method to estimate performance of the BTMS in a large module in which transient simulation was not viable. As more and more study about air-cooling is being done, it is being realized that, though air-cooling BTMS could maintain the operation temperature within the certain range, the performance failed to satisfied the requirement under high charging/discharging rate due to poor capacity of air [19].

Compared with other BTMS, the liquid-cooling system presents a better cooling performance, although there remain many challenges such as complexity and potential leakage. Nelson [20] compared various cooling methods for lithium-ion batteries; the results suggested that a liquid-cooling system had better cooling performance than other cooling systems. Therefore, a liquid-cooling strategy was still promising for BTMS, especially when applied in large scale battery modules. A lot of studies have been done to optimize the liquid-cooling strategy. The performance can be simply improved by increasing the inlet flow rate, but the improvement will become very limited when the inlet flow rate reaches a certain value [21]. Besides raising inlet flow rate, it was found that the performance can be significantly improved by applying reasonable layouts [22]. Molaeimanesh et al. [23] studied the role of system configuration on the cooling performance through simulations of four designed BTMS. The result indicated that the parallel configuration provides the best performance for long cycling operation. Wang et al. [24] designed a liquid-cooling BTMS coupled with serpentine microchannels and successfully reduced the maximum temperature and promoted temperature uniformity effectively by optimizing the configuration of the serpentine microchannels. Dong et al. [25] proposed a novel double helix cooling structure for cylindrical batteries; it proved to be capable of keeping the operation temperature within optimal range. In order to further improve the performance of liquid-cooling BTMS, various cold plates have been designed. The cold plate can transfer the heat to coolant flowing through the internal channel and then take the heat away. Jarrett et al. [26] proposed a serpentine channel cold plate assessed by CFD to analyze the effect of different characteristics. Wu et al. [27] designed a baffled cold plate based BTMS exploring the influence of structure parameters on the cooling performance. They also managed to improve the comprehensive heat transfer performance and temperature uniformity of the baffled cold plate. Jiang et al. [28] used the mini-V-shaped ribs to optimize the cooling performance of the cold plate. Through studying the flow and heat transfer features of different ribs, such as shape square, semicircular V-shaped and so on, they found that the Nusselt number and friction factor of the V-shaped channel were higher than that of other channels. Liang et al. [29] proposed a novel inclined U-shaped flat micro heat pipe array for liquid cooling BTMS, and the equivalent thermal conductivity is about 4350 W·m⁻¹·K⁻¹.

In this paper, compared with a pure silicon cooling system, a liquid cooling system, including silicon assisted with mini-channel cold plates, has been proposed; the impact of flow directions and volume flow rates are studied by experiment and simulation. What is more, the maximum temperature and temperature difference of the module have been analyzed and discussed. The temperature distribution was analyzed by dividing the module into three parts, which helps in studying the thermal behavior of proposed BTMS. UDF was also applied to obtain a more precise heat generation rate of the LIBs. The object of this research would provide an efficient and feasible thermal management system for battery modules.

2. Experiment

2.1. Preparation of CSG

The SG used was purchased from Guangzhou Platinum Bridge Electronic Materials Co., Ltd. (Guangzhou, China). The aluminum (Al) was purchased from Anshan Iron and Steel Group Co., Ltd. (Anshan, China) with the particle size of 10 μm. The EG exhibited the expanded rate of 300. As shown in Figure 1a, it demonstrated the manufacturing process of CSG for battery modules. It should also be noted that the mass ratio of component A and curing agent B was 1:1. Firstly, the component A and Al powder was put into one beaker while putting curing agent B and EG into another beaker. Then the composites of component A and Al powder were mixed together by the electric mixer (DW-90 W, Shanghai Zhize Biological Technology Development Co., Ltd., Shanghai, China) with 1000 r/min for 15 min. At the same time, the curing agent B and EG were also mixed and stirred with the same speed for 15 min. Then these two mixtures were poured into the same beaker after which they were mixed together and stirred for another 15 min. Finally, the mixtures—which were to be put into the fume hood with the selected batteries and solidified at room temperature after 8 h—were taken out of the container.

2.2. Design of BTMS

As shown in Figure 2, the test system was designed and built. The specific parameters of the batteries were shown in

Table 1. The parameters of the prismatic LIBs in the simulation.

Parameter	Value
Size (mm)	18 × 65
Anode	LiNi1/3Mn1/3Co1/3O2
Cathode	Graphite
Electrolyte	LiPF6/Ethylene acid ester (EC)
Cut-off voltage of charging (V)	4.2
Nominal capacity (Ah)	2.6
Cut-off voltage of discharging (V)	2.7
Weight (g)	43.8

. The CSG liquid-cooling system (CSG–LC) was composed of 25 batteries, which were assembled with 5 in series and 5 in parallel. It should also be mentioned that the thermocouples (TT-T-30-SLE, 260 °C, 0.255 mm, accuracy of ±0.2 °C) were attached on the surface of batteries, and the measured batteries were marked in Figure 1c. What is more, all thermocouples were tested before being applied in the module to ensure the reliability of results. To guarantee the consistency of the module, all batteries were first discharged at 1C until the voltage achieved 2.7 V by constant current (CC) in which the batteries were charged/discharged at constant current until the voltage achieved nominal voltage, then set aside for 60 min. C-rate is usually used to describe the rate of charging/discharging, which can be calculated by dividing current by nominal capacity of cell. Whereafter, the batteries were charged at 1C until the voltage reached 4.2 V. Lastly, the batteries were charged while the voltage was maintained at 4.2 V. As the current fell to 0.2 A by constant-current and constant voltage (CC-CV) in which the batteries were first charged/discharged at constant current until the voltage achieved nominal voltage and then charged/discharged at constant voltage until the current was reduced to the setting value, then measuring the capacity, internal resistance and other parameters of all batteries. After the measurements, the batteries were chosen according to their close capacity, internal resistance and so on, which were to be assembled into battery modules. What is more, all thermocouples were tested before being used in the modules. After assembling the modules, the pipes were connected to the pump by diverter valve and the thermocouples were connected to the data logger.

Compared with traditional liquid-cooling strategies, the CSG liquid-cooling presented much better cooling performance because the heat transfer coefficient of MCP was much higher than common copper tube, which is caused by the larger specific area of MCP. The CSG could enhance the heat transfer from batteries to the MCPs. What is more, the structure of cross network of the battery module could further improve the cooling performance. As is shown in

Table 2. The heat transfer network of the CSG module.

	Center	Side	Corner

, the cross structure changed the heat transfer network of the CSG module. For the cells at center and corner, the heat transfer surface was distributed more uniformly. For the cells at the side, the heat transfer surface was 1.5 times as much as those of parallel structure.

Among the battery modules with the CSG–LC system, the flows of water were driven by pump with power of 24 W. During the operating process, the water pump should keep running continually.

In Figure 2, the schematic diagram of experimental setup was shown. During the operating process, the battery module was placed in a thermostat (ZH-TH-1000D, Dongguan Zhenghang Instrument Equipment Co., Ltd., Dongguan, China), which helped to keep a constant ambient temperature. The temperature variations of CSG–LC module were collected by a data logger (Agilent 34,970 A) through the thermocouples. The computer was utilized to control the data logger and the testing equipment and to record the corresponding data. The parameters of the charging and discharging process were displayed in

Table 3. Process of the charging and discharging.

Process	Current (A)	Cut-Off of Voltage (V)	Time (min)	Cut-Off of Current (V)
Constant current discharging	39	14
Rest			15
Constant current charging	13	21
Constant voltage charging		21		1

.

3. Numerical Model and Methodology

3.1. A Liquid Cooling BTMS Based on Mini-Channel Cold Plate

As shown in Figure 3, the liquid cooling system coupled with CSG has exhibited optimum cooling performance with synergistic temperature-controlling ability. The liquid cooling battery module contained 25 18,650-type ternary batteries, which were assembled with 5 in serial and 5 in parallel. The batteries are distributed uniformly among the module. Towards the CSG module, eight MCPs were inserted into the CSG mold, which were inserted uniformly among the battery module. The specific sizes of the CSG module (110 × 110 × 60 mm) were depicted in Figure 3b, and the corresponding parameters of the operating conditions are shown in

Table 4. The parameters of the operating conditions in the CSG module.

Operating Condition	Parameters
Ambient Temperature (°C)	25/35
Initial Temperature (°C)	25/35
Temperature of Coolant (°C)	25
Volume Flow Rate (mL/s)	6/8/10/12/14
Discharging rate	1/2/3

. The CPU was Intel (R) Xeon (R) Silver 4214R CPU @ 2.40 GHz 2.9 GHz. Besides, the time step size of simulation runtime was 1 s and the maximum iterations were 60 times.

What is more, each battery has the equivalent spacing of 4 mm. Therefore, the corresponding MCPs were contrapuntally inserted into the upper and lower part of the CSG module, respectively. To achieve a more rational arrangement, the space between each MCP was set at 20 mm. The distance between the bottoms of the CSG module and the upper MCPs was 37 mm, while the marginal distance from the bottom to the MCP was set at 8 mm. The geometry of the MCP (150 × 20 × 2 mm) could be machined, as shown in Figure 3c and described from different views. The 10 straight flow channels were homogeneously arranged inside each MCP with a cross-sectional area of 2.625 mm² (1.5 × 1.75 mm). The MCP was made of aluminum, and the coolant was water with an inlet temperature of 25 °C.

Based on the finite element method, the numerical simulation was performed to calculate the thermal behaviors of the battery module, which was adopted by the computational fluid dynamic (CFD) software of Ansys Fluent 2021R1. The thermo-physical properties of these materials utilized in the model are displayed in

Table 5. Thermo-physical properties of the materials in the simulation.

	Battery	CSG	Aluminum	Water
Density (kg∙m−3)	2648	1420	2719	998.2
Specific heat (J∙kg−1∙K−1)	2500	911.4	871	4182
Thermal conductivity (W∙m−1∙K−1)	3	1.11	202.4	0.6
Viscosity coefficient (kg∙m−1∙s−1)	--	--	--	0.001

.

3.2. The Generating Heat Model of Battery and Validating Analysis

According to the previous research results, a portion of the electrical energy was stored and converted into heat, which was exchanged with the surrounding environment during the charge-discharge process. During the discharge process, the temperature distributions of the batteries have to be affected by the actual irreversible and reversible heat generation behaviors. Thus, the total heat generated by battery can be divided into three various aspects such as Q_r, Q_p and Q_j, which were calculated as follows:

$$Q_{gen}=Q_r+Q_p+Q_j$$

(1)

where Q_r, Q_p and Q_j represented the reaction heat, polarization heat and Joule heat, respectively. These three different kinds of heat could be calculated by the following specific Equations (2)–(4).

$$Q_r=-T_c\mathrm{∆}S\frac{I}{nF}$$

(2)

where T_c was the temperature (°C) of the cell; I was the charge and discharge current (A); n respected the charge number pertaining to the reaction (n = 1 for a single LIB); and F was the Faraday constant (C∙mol⁻¹).

$$Q_P=I^2{R}_p=I^2\left(R_t-R_e\right)$$

(3)

where R_p, R_e, R_t respected the polarization resistance, the internal resistance and the total resistance of the cell (Ω), respectively.

$$Q_j=I^2{R}_e$$

(4)

where I was the current (A) of the electrode.

To obtain the heat generation and the temperature distribution of the cell, a lumped battery model—D. Bernardi heat generation was used to calculate the temperature variation and to analyze the thermal performance of the cell as follows:

$$\dot{q}=\frac{I}{V}\left(E_{OC}\right)-U-T\frac{d{E}_{oc}}{dT}$$

(5)

where $\dot{q}$ was the heat generation of the cell; I was the charge and discharge current (A); V was the volume of the cell (m³); E_oc was the balance electromotive force of the cell; U was the operating potentials of the cell (V); and T was the working temperature of the cell (°C).

To verify the simulation accuracy and stability of the battery heating model, the experimental heat generation rate test of one 18650-type ternary LIB was carried out. As shown in Figure 4a, during the discharge process, the battery was placed in the thermostat, which could provide a constant ambient temperature of 30 °C. Then the batteries were discharged by battery testing instrument (CT-40001-60V-100A-NA, Shenzhen Newwell Electronics Co., Ltd., Shenzhen, China) at 1, 2 and 3C, respectively. The current value was set with the corresponding current density with discharge rate until the cut-off voltage. C-rate, which commonly represents the charge/discharge rate, is calculated by dividing the charge/discharge current by nominal capacity at 1–3C (2.6–7.2 A) until the cut-off voltage of 2.7 V. There were three T-type thermocouples that were set on the top, middle and bottom of the cell, respectively. The surface temperature dates were recorded by Agilent (34,970 A) equipment through the thermocouples. According to the calculation of the internal heat source (Q_gen), the heat generation rate of batteries ranged from 0.49 to 5.82 W (as shown in

Table 6. Heat generation rate of the 18650-type battery at different discharge rates.

C-Rate	1C	2C	3C
Heat generation rate (W)	0.49	3.35	5.82

), taking into account the heat transfers by convection. In the meanwhile, the simulation was conducted with the heat source of the cell using a user-defined function. The comparison between the experimental data and simulation results were shown in Figure 4b–d. It can be obviously observed that the simulation effectiveness was in good agreement with the experimental data. During the discharge processes of different rates, the maximum relative errors between the experimental and simulated results was only 3.95%, which indicated that the battery heating model was reliable to be employed in the following numerical simulation.

3.3. Governing Equations

Considering the transient numerical simulation based on the three-dimensional model (the energy conservation equations of the battery and the CGS were given), it could be seen as follows: [30,31]

$${\rho }_c{C}_{p,c}\frac{\partial T}{\partial t}=\mathrm{\nabla }·\left({\lambda }_c\mathrm{\nabla }{T}_c\right)+\dot{q}$$

(6)

$${\rho }_{CSG}{C}_{p,CSG}\frac{\partial T}{\partial t}=\mathrm{\nabla }·\left({\lambda }_{CSG}\mathrm{\nabla }{T}_{CSG}\right)$$

(7)

where ρ_c, C_p,c, λ_c, T_c and $\dot{q}$ were the density, specific heat capacity, thermal conductivity, temperature and heat generation rate of the batteries, respectively; the ρ_CSG, C_p,CSG, λ_CSG and T_CSG were the density, specific heat capacity, thermal conductivity and temperature of the CSG, respectively.

The mass equation, the momentum conversation equation and the energy equation of the cooling water were given as follows [30,31]:

$$\frac{\partial {\rho }_w}{\partial t}+\mathrm{\nabla }·\left({\rho }_w\overrightarrow{v}\right)=0$$

(8)

$$\frac{\partial }{\partial t}\left({\rho }_w\overrightarrow{v}\right)+\mathrm{\nabla }·\left({\rho }_w\overrightarrow{v}·\overrightarrow{v}\right)=-\mathrm{\nabla }{P}_w+\mathrm{\nabla }·\left({\mu }_w·\overrightarrow{v}\right)$$

(9)

$${\rho }_w{C}_{p,w}\frac{\partial T}{\partial t}+\mathrm{\nabla }·\left({\rho }_w{C}_{p,w}\overrightarrow{v}{T}_w\right)=\mathrm{\nabla }·\left({\lambda }_w\mathrm{\nabla }{T}_w\right)$$

(10)

where ρ_w, C_p,w, λ_w and T_w were the density, specific heat capacity, thermal conductivity and temperature of the cooling water; $\overrightarrow{v}$ was the velocity vector; P was the static pressure; and μ meant the aerodynamic viscosity coefficient of the cooling water.

3.4. Boundary Conditions

Since the hydraulic diameter of the mini-channel inside the MCPs was 1.62 mm, the value of the Reynolds number (Re = ul∙v⁻¹) ranged from 44.83 to 89.69 when the flow rate was set from 6 to 12 mL∙s⁻¹. The laminar flow model utilized the inlet flow rate of 12 mL·s⁻¹ (Re = 89.69) in the simulation, while the enhancing wall treatment was selected to five. Thus, the laminar flow model (y⁺ = 5) had been utilized to simulate the flow state of the cooling water in the flow channels. During the discharge processes at different discharge rates, it was assumed that the batteries were discharged by the constant-current mode until the depth of discharge (DOD) was 100%. The heat source term was adopted with the heat generation rate (0.49–5.82 W) for each battery in the numerical simulation.

There are three different types of battery modules that were carried out with various thermal management systems, which were pure CSG, MCP and IMCP (intersect channel plate) cooling system; it was set as fluid-solid coupling with contact heat transfer faces. The interface between the coolant and the flow channels was also employed with the same setup parameters. The boundary of the inlets was set as velocity-inlet, while the outlet was set as pressure-outlet. The temperature of the cooling water was set at 25 °C with constant velocity-inlet depending on the specific conditions. Based on the three-dimensional incompressible Navier-Stokes equations, the iterate calculation was carried out by using the simple method due to high calculation accuracy and good robustness. Meanwhile, the second-order upwind difference scheme was applied to convective kinematics.

Besides, in order to simplify the simulation complexity of the battery module, some assumptions were proposed according to the previous works, for example, the thermal contact resistance and the radiative heat transfer were ignored, and the CSG and the MCP were made of composite silica-gel and aluminum, respectively, which were assumed to be homogenous and isotropic. Besides, all the thermo-physical properties of the composite material were considered constant values.

3.5. Meshing and Grid Independence Test

During the simulation of the heat and mass transfer, the meshes of the battery module were divided and generated Workbench Meshing from Ansys 2021 R1. To improve the grid quality, the hybrid grid was based on different methods (Cells & MCPs: Sweep method, CSG: Multizone method) and was applied to the battery module for the simulation. Considering the mesh accuracy and computation efficiency, the necessary grid independence test was carried out based on five different grid numbers ranging from 3,843,073 to 6,309,837. As shown in Figure 5b, the T_max of different cells and the V_max of flow channels were employed as the evaluation indexes. According to the evaluation indexes proposed in this simulation test, when the grid number of the module increased from 6,169,216 to 6,309,837, the difference in T_max of cell₁ (0.147%), T_max of cell₂ (0.038%), T_max of cell_cen (0.027%) and V_max of flow channels (0%) was able to be neglected. Furthermore, as the battery module adopted the grid number of 6,169,216 (Figure 5a), the average values of Skewness, Element quality and Orthogonal quality in the meshing were 0.08, 0.71 and 0.89, respectively. It indicated that the number of grids had not substantially affected the simulation effectiveness with such high-quality mesh. Thus, the corresponding mesh level with 6,169,216 elements was applied to the subsequent numerical simulation.

4. Results and Discussion

In this research, to simplify analyzing the data and discussing the thermal performances of battery modules, T_out (the maximum temperature of T_1A, T_1B, T_1C and T_1D); T_middle (the maximum temperature of T_2A, T_2B, T_2C and T_2D); and T_center have been recognized as the research objects, which were analyzed in detail. The selected batteries in module were shown in Figure 1c. Besides, the maximum temperature and temperature difference among the battery modules were also essential parameters to evaluating the performance, so the MCPs with various conditions such as flowing rates and flowing orientation were further analyzed.

4.1. Characterization of the CSG

In the proposed liquid-cooling module, the silica gel (SG) was utilized to enhance the heat transfer from LIB to MCP. However, the thermal conductivity of traditional SG was too low to be applied in the thermal management system. Thus, it is very essential to improve the heat conductivity capacity of SG.

In this experiment, expanded graphite (EG) with high thermal conductivity was utilized to improve the thermal conductivity of SG. However, accompanied with the increasing proportion of EG in CSG exceeded 2%; the viscosity of CSG was also greatly increased. The photographs of different CSG were displayed in Figure 6. Since the space between cells and MCPs was very limited, such features made CSG with too much EG hard to be used in modules. What is more, when the proportion of EG exceeded 2%, the increase of thermal conductivity became very limited. In order to decrease the viscosity of CSG while maintaining its thermal conductivity, aluminum powder was induced so that the thermal conductivity could be improved while the viscosity could be maintained within a suitable range. The thermal conductivity of CSG was displayed in Figure 6. It could be seen that when the proportions of EG and aluminum powder reached 1.5% and 1%, the thermal conductivity was 1.15 W∙K⁻¹∙m⁻¹ and the viscosity was the same as that of CSG with 1.5% EG. Thus, the CSG presented the best comprehensive performance when the proportions of EG and aluminum powder reached 1.5 and 1% whose thermal conductivity was nearly 3.7 times the pure SG.

4.2. The Performance of Battery Module with CSG–LC

Due to high thermal transfer coefficient of cold plates, CSG–LC exhibited a great performance in thermal management. In the CSG–LC module, CSG could be benefited to enhance the heat transferring rate between batteries and MCPs; then the water inside the cold plates could absorb the heat and take it away. In order to optimize the designation of the CSG–LC module, the effect of the volume flow rate and flow channel were investigated.

4.2.1. The Effect of the Inlet Volume Flow Rate

As for the thermal management performance of the CSG–LC module, the inlet volume flow rate has a great impact on the cooling effect, which would significantly influence the heat transfer coefficient of the MCP. However, the increase of the heat transfer coefficient becomes very limited when the flowing rate exceeds a certain degree. What is more, the pressure drop would also keep an increasing tendency as the inlet volume flow rate increased, leading to a higher external energy consumption.

Thus, it is very necessary to figure out the optimal inlet volume flow rate of MCP. In this section, there were four different inlet volume flow rates of 6, 8, 10 and 12 mL∙s⁻¹ selected to investigate the impact of inlet volume flow rate. What is more, the thermal behaviors of the CSG–LC module were investigated by numerical method at the T_A of 25 and 35 °C, respectively. Figure 7 displays the temperature variation of the CSG–LC at 3C discharge rate, and the corresponding temperature distribution was displayed at Figure 8. The maximum temperature of the CSG–LC module decreased as the inlet volume flow rate increased. However, the temperature variations of simulation results of 10 and 12 mL∙s⁻¹ were very closed at T_A of 25 and 35 °C, respectively. Considering the cooling effect and energy consumption, it indicated that the CSG–LC module presented the optimum performance with the inlet volume of 10 mL∙s⁻¹. As shown in Figure 7 and Figure 8, the temperature of the cell at its center was much lower than that of cells at their corners. It is because the heat transfer between batteries and environment was much weaker than that between batteries and MCPs. What is more, T₂ and T_center presented a decreasing trend during the discharging process close to its end, especially at the T_A of 35 °C. The main reason was that, as the temperature went up, the heat transfer grew stronger, but the heat generation rate of cells became weak as the discharging process got close to the end.

4.2.2. The Effect of the Flow Channel

The flow channel also has significant impact on the cooling performance of the CSG–LC module. On the basis of the temperature distribution of volume flow rate, the inlet volume flow rate of 10 mL∙s⁻¹ and the T_A of 25 °C were selected to investigate the impact of flowing channel configuration. In the parallel flowing channel (PFC), although the temperature of batteries could be controlled below 45 °C, the temperature difference was not very satisfying. By altering the flowing channel configuration, the temperature uniformity could be further improved to satisfy the requirements of thermal management of the battery module.

In order to improve the temperature uniformity, the CSG–LC module with sides to middle channels (SMC) and reciprocal chiasma channels (RCC) were proposed and studied. As for the SMC, the water flowed from outer to inner MCPs. As for RCC, the water in the channels could evenly transverse the interior of the battery module. Figure 9 displayed the temperature variations, the schematic of the corresponding flow direction and temperature distribution of the CSG–LC module with different flowing channel configuration. It can be seen that the performance was significantly improved when optimized by the flowing channel. The maximum temperature of the battery module with SMC and RCC channel was controlled 39.34 and 41.64 °C and the temperature difference was 2.93 and 1.43 °C, respectively. Compared with parallel, the working temperature and temperature difference were both decreased. Nevertheless, the temperature of the RCC-battery module presented a better thermal management than the SMC-battery module cooling system, as shown in Figure 9b. In the SMC-battery module, the heat transfer of outer cells was enhanced by the entrance effect. Thereby, the center cells no longer presented the minimum temperature which led to better temperature uniformity. As for the RCC-battery module, each center cell and the middle cells were both cooled by inlet and outlet MCPs. Though the center cells still presented the minimum temperature, the maximum temperature was greatly reduced. However, the temperature of outside cells cooled by the outlet MCP was higher. Since the maximum temperature of the RCC-battery module was maintained lower than 45 °C, it can be concluded that the RCC-battery module presented better comprehensive performance.

In order to further investigate the performance of these battery modules, the process of cycling test was displayed in

Table 7. Process of cycling test.

Process	Current (A)	Voltage (V)	Time (min)
Constant current discharging	39	Cut-off of 14
Rest			10
Constant current charging	13	Cut-off of 21
Constant voltage charging	Cut-off of 1	21
Cycling	For 6 times

and the temperature variations were displayed in Figure 9.

As can be seen in Figure 9, there was heat accumulation in both SMC- and RCC-battery modules, especially at the front three cycling processes. But such phenomenon was very weak at the latter two cycling processes. The maximum temperature of the RCC-battery module was higher than that of the SMC-battery module during the whole cycling. It indicated that the temperature difference of SMC and RCC can be maintained within 3.78 and 1.61 °C, and the maximum temperature can be controlled below 41.32 and 44.65 °C, which proved the RCC-battery module can provide an excellent flowing channel configuration. The main reason is that the heat can control the temperature rising trend through the water absorbing the heat in the MCPs. This makes the heat exchange in the latter part of the channel weaker. In the SMC channel, although the inner batteries are cooled by hotter coolant, the heat exchange of outside batteries and MCPs is much weaker, which improves the balance and leads to lower working temperature and temperature difference. What is more, since the heat dissipation of outside batteries that have the highest temperature during discharging process is greatly improved, the reduction of the maximum temperature is significant. In the RCC channel, the reduction of the maximum temperature is less significant. Due to the better uniform temperature distribution of coolant, however, the improvement of temperature uniformity is outstanding. At the same time, the operating temperature can still be controlled below 50 °C. Therefore, the battery module with the RCC presents the optimum comprehensive performance.

5. Conclusions

Aiming at the thermal management system for battery module, the composite silica gel (CSG), coupled with cross-structure mini-channel cold plate (MCP), had been proposed and utilized for the cylindrical battery module. The CSG and MCPs with different flow rates and various flow channels were analyzed through experiments and simulations. The maximum temperature and temperature difference among battery modules with various cooling structures are compared in detail. The main conclusions are summarized as follows:

(1)

The CSG–LC module with 10 mL∙s⁻¹ flow rate can provide an optimum thermal management effect, which can maintain the temperature of batteries within desirable operating range. The maximum temperatures were controlled below 42 and 49 °C under the ambient temperature of 25 and 35 °C, respectively.

(2)

The temperature uniformity can be obviously affected with the flowing orientation in the liquid system. The CSG–LC module with RCC exhibited better cooling effect than that with SMC. Especially at 3C discharge rate, the results indicated that the temperature difference can be maintained within 1.5 °C, and the maximum temperature was below 42 °C after six cycling processes.

(3)

After six cycling processes, the CSG–LC module with the RCC structure can maintain the maximum temperature and the temperature difference within suitable temperature range. The experimental and simulating results revealed that the CSG–LC module with the RCC structure exhibits an optimum thermal management effect, especially at high discharge rate.