Numerical Analysis of Cooling Plates with Different Structures for Electric Vehicle Battery Thermal Management Systems

Li, Ming; Wang, Jianchao; Guo, Qin; Li, Yue; Xue, Qingfeng; Qin, Guihe

doi:10.1061/(ASCE)EY.1943-7897.0000648

Open access

Technical Papers

Jun 10, 2020

Numerical Analysis of Cooling Plates with Different Structures for Electric Vehicle Battery Thermal Management Systems

Authors: Ming Li, Jianchao Wang, Qin Guo https://orcid.org/0000-0001-8754-3734 [email protected], Yue Li, Qingfeng Xue, and Guihe QinAuthor Affiliations

Publication: Journal of Energy Engineering

Volume 146, Issue 4

https://doi.org/10.1061/(ASCE)EY.1943-7897.0000648

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Abstract

The performance of lithium-ion batteries used in electric vehicles (EVs) is greatly affected by temperature. Hence, an efficient battery thermal management system (BTMS) is needed to ensure the safety of batteries and prolong the cycle life. In order to find a more efficient type of cooling plate for the rectangular batteries, the three-dimensional models of four common cooling plates with different internal structures are established. After a series of computational fluid dynamic simulations and comparisons, the most optimum structure of the cooling plate is obtained. Subsequently, the effect of different mass flow rates is investigated among the different cooling plates. It indicates that the cooling plate with convex structure has a better cooling performance than the other three, and the heat transfer performance of various cooling plates changes a lot with the increasing of mass flow rate. The convex structured cooling plate could be applied for optimizing the performance for electric vehicles.

Introduction

With the rapid development of electric vehicles (EVs), the technical requirements of lithium-ion batteries are constantly increasing (Ren 2018). How to ensure the long-term and efficient operation of EVs has become a hot spot for many researchers. In order to meet the needs of users for the endurance of EVs, battery technology is constantly developing, and the energy density of batteries is also increasing (Yuksel et al. 2016). Accordingly, the requirements for a battery thermal management system (BTMS) should also increase (Wang et al. 2016). At very high or low temperatures, the capacity of a lithium-ion battery decreases greatly with increasing cycle times. Ramadass et al. (2002) studied the capacity of batteries under different charging and discharging cycle times. The research showed that with the increase of batteries’ surface temperature, the number of charge or discharge cycles of batteries decreased a lot, and the batteries would aging more with the wide range of temperature fluctuations. A lithium-ion battery often exceeds its optimal operating temperature range because of the complex road conditions, ambient environment, and driving habits of automobiles (Guo and Chen 2015). The C-rate is the charge or discharge current divided by battery capacity. The rate 1C means that fully charging or discharging a battery requires 1 h, while 2C means only 0.5 h is needed to complete it. It not only has a negative impact on battery endurance, but a long time at a high temperature is more likely to threaten our personal safety. For example, Flight JA829 was reported to be on fire and smoking in 2013 because of the battery thermal management out of control. In recent years, reports of electric vehicle fire due to excessive battery temperature have also been common occurrences. This has also aroused widespread concern about the safety of electric vehicles (Situ et al. 2017).

According to the different cooling mediums, the cooling modes of an EV lithium-ion battery are mainly divided into air-cooling system, liquid-cooling system, and phase change material (PCM) cooling system (Yuanwang et al. 2018; Wang et al. 2016). The traditional air-cooling system is simple in structure, easy to arrange, and has good cooling characteristics for the battery module. It can be divided into active heat dissipation and passive heat dissipation according to different air supply modes, that is, forced convection heat transfer and natural convection heat transfer (Wang et al. 2015). The effect of active air cooling is better than that of passive air cooling. Park (2013) optimized the design of hybrid vehicles using forced air cooling, and found that the improved structure can meet the cooling needs of lithium-ion batteries. Mahamud and Park (2011) designed a variety of structures for air-cooled systems to enhance heat transfer capacity. As far as pure EVs are concerned, all the lithium-ion components on the vehicle need batteries to provide lithium ion, which requires a more powerful BTMS. Xu and He (2013) found that compared with the air speed of

60 km / h

, the average values of the battery maximum temperature rise and internal maximum temperature difference decrease by 10.4% and 6.3% at the speed of

70 km / h

. The speed of the fan cannot unlimitedly increase because the increase of wind speed also means increasing the load of the fan, which has a great impact on the mileage of the EV. Sabbah et al. (2008) found that with the increase of heat production, the air-cooling system could not quickly control the temperature of the battery within the optimal range, and the temperature uniformity of the battery pack was also poor.

PCMs could be another method to cool batteries. They could absorb a lot of heat through the change from solid to liquid. Because of the high performance of the heat dissipation of batteries, many researchers showed great interest in optimizing them (Samimi et al. 2016; Rao et al. 2015). Khateeb et al. (2004) simulated and optimized the heat pipe, and found that the air-cooled battery could meet the requirements of heat dissipation at low discharge rates, while at high discharge rates, using PCMs is more efficient than using the air-cooled method. The results show that the optimal mass flow rate should be

0.0008 kg s^{- 1}

and the transition point should be 308.15 K. But it is very difficult to keep the temperature in the phase transition point. Before phase change, the thermal physics of the materials are not so good. Besides, the cost of the materials also limits the application of this technology in the field of battery thermal management systems (Ianniciello et al. 2018).

Because the thermal conductivity of liquids is much higher than that of air, more and more experts and scholars focus on the study of liquid cooling. According to the different types of refrigerants used for heat transfer with batteries, the methods of cooling liquids can also be divided into direct refrigerant cooling and indirect cooling. Direct cooling is to absorb heat by evaporation of refrigerant, which takes away the heat generated by the battery. The most common form of indirect cooling mode is the heat pipe (Jiaqiang et al. 2016). But this type is very difficult to install with batteries in a such narrow space of EVs. As for the indirect cooling mode, most of the batteries are cooled by water and ethylene glycol solution (Liu et al. 2016; Jiaqiang et al. 2016).

Liquid cooling plate is an important direction in the research of indirect contact BTMS. The design dimensions of the liquid cooling plates are often related to the structure material and layout of the battery, while the internal flow channel of the liquid-cooled plate has a variety of forms. The most common form of cooling plate is the serpentine coil with minichannel (Rao et al. 2015; Huo et al. 2015). Jarrett and Kim (2011) calculated the size of a rectangular channel by numerical simulation and obtained the corresponding optimal width form. Huo et al. (2015) optimized the performance of the cooling plate by changing the number of channels. Zhao et al. (2015) studied the influence of the length of the cooling plate channels and the velocity of coolant. The results showed that the increase of the length and velocity of the channel would lead to the increase of the maximum temperature difference of the coolant. At present, many researchers have optimized the length, width, and corner radius of the liquid cooling plate in a rectangular channel. Panchal et al. (2017) presented a study of different inlet temperatures and discharge rates to compare the temperature and velocity. They found that the velocity remains roughly the same. But the temperature distribution increased a lot as the charge rate changed from 1C to 2C. Patil et al. (2018) simulated another type of cold plate that contains multiple channels in one plate, and concluded that with the number of channels increased, the pressure drop decreased a lot. Additionally, inlet temperatures of 298 and 288 K were suggested to be applied for this type of cold plate when the charge rate is 1C and 2C, respectively.

However, analysis and comparison of different structures of the cooling plate are still rare. At present, the structure of the cooling plate is relatively simple, and the channel structure of the cold plate is relatively single. The heat transfer and fluid resistance of fluids would be affected by different channel structures. Other types of BTMSs like metal foams cool batteries by increasing the heat transfer area and then cooling the metal foam by air. However, the mechanical properties of this method need to be improved (Saw et al. 2017; Hussain et al. 2016; Mohammadian et al. 2015).

In this paper, four multichannel cooling plates with honeycomb structure, U-shaped structure, airfoil structure, and convex structure are designed according to the certain heating condition of a rectangular battery pack. After a series of computational fluid dynamics (CFD) simulations, the optimal cooling plate structure is selected to meet the requirements of optimal heat transfer performance and minimum pressure drop.

Design and Analysis of the Cooling Plate

Simulation Model

The structure of the liquid cooling plate is mainly designed according to the size of the battery pack of an EV. The size of the cooling plate is designed to be

620 \times 340 \times 4.5 mm

(excluding the height of the pipe at the inlet and outlet). The method of cooling the bottom was chosen for this simulation, which is convenient for setting, saving space, and easy repair. The structure of the cooling plate is shown in Fig. 1. The area of the internal fluid is determined by the structure of the battery and the fabrication of the cooling plate. The total size of the cell is

600 \times 310 \times 2.5 mm

.

Fig. 1. Battery cooling plate with convex structure.

The fluid regions of four different types of cooling plates are shown in Fig. 2. The cooling plates were made of aluminum alloy, which is widely used in various heat exchangers because of its smaller density, better thermal conductivity, ductility, and easier processing. Ethylene glycol was chosen for the coolant because of its high boiling point and low freezing point. The thermal physical properties of the materials are shown in Table 1. According to the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE 2004) lists, with the increase of mass concentration, the freezing point of ethylene glycol decreases and the boiling point increases. From Table 2 it can be seen that different concentrations of ethylene glycol are always in the liquid state at 243–373 K, so the density can be assumed constant in a limited temperature range.

Fig. 2. Different structures of cooling plate: (a) convex structure; (b) honeycomb structure; (c) airfoil structure; and (d) U-shaped structure.

Table 1. Thermal physical properties of cooling plate and coolant at 298.15 K

Substance	Density ( $kg m^{- 3}$ )	Dynamic viscosity ( $Pa \cdot s$ )	Specific heat ( $J {kg}^{- 1} K^{- 1}$ )	Thermal conductivity ( $W m^{- 1} K^{- 1}$ )	Thickness (mm)	Length (mm)	Width (mm)
Fluid
50% ethylene glycol aqueous solution	1,071.11	0.00339	3,300	0.384	2.5	600	320
Solid
Aluminum alloy	2,719	—	871	202	4.5	620	340

Table 2. Freezing point and boiling point at different concentrations

Concentration	Freezing point (K)	Boiling point (K)
0.0	273.2	373.2
10	270	374.3
20	265.4	375.4
30	259.1	377.6
40	250.9	378.8
50	239.4	380.4
60	224.9	383.2

Simulation Conditions

The commercial CFD simulation software STAR-CCM+ was used for the simulation of cooling plates. STAR-CCM+ has the advantages of simplicity, high integration, and high stability, so all the grid generation and simulation processes were all completed in it. The boundary conditions were set by the mass flow inlet, pressure outlet, and wall heat flow in the simulation. During this simulation, the flow rate was set to

0.0223 kg s^{- 1}

first, which was a real value of the pump. The diameter of the inlet was set to 20 mm, and the dynamic viscosity of the medium was

0.00339 Pa \cdot s

. Therefore, the Reynolds number can be calculated by Eq. (1), which is 432. Thus, the inlet condition can be seen as the laminar flow. and the inlet temperature of the glycol solution was set to be 298.15 K. The capacity of the rectangular Li-ion battery was 100 Ah and the mean voltage was 3.66 V. The battery is shown in Fig. 3(a). The heat generation inside the battery is a complex process, so it is good to directly measure the heat flux of a battery in a certain charge or discharge rate. When the battery pack discharged at 1C, the heat flux on the bottom of the battery was tested by two heat flow densitometers, and the location can be seen in Fig. 3(b). Then the data were recorded every 10 min, and the average results of five trials show the mean heat flux was

1,257.86 W m^{- 2}

. Because the pack was sealed by the plastic, it can be assumed that the heat could only be taken away by the cooling plate. The basic information of the battery is given in Table 3

Re = \frac{ρ u d}{μ}

(1)

Fig. 3. Battery experiment: (a) battery setup; and (b) sensor locations.

Table 3. Battery properties

Property	Value
Capacity (Ah)	100
Size (mm)	$340 \times 185 \times 130$
Energy density ( $Wh / kg$ )	210
Discharge	$1 C / 100 A$
Cycle	2,000
Test standard	100% DOD, $1 C / 1 C$
Heat flux ( $W / m^{2}$ )	1,257.86

Note: DOD = depth of discharge.

The cooling plates were coupled with the bottom of battery modules. The other sides of the plates were exposed to air inside the battery box. Therefore, the heat resistance may include the radiant heat resistance inside the box, the heat conduction resistance of the battery box walls, and the heat convection resistance with ambient air. Because of the complex heat transfer condition, it can be assumed that the heat could only be taken away by the coolant. The boundary condition can be seen in Fig. 4.

Fig. 4. Boundaries of the cooling plate: (a) the whole plate conditions; and (b) front view of the plate.

As shown in Fig. 4, the model was the conjugate heat transfer model: only one direction of the plate can absorb the heat from the battery, and the other outside surfaces of cooling plate are adiabatic of surroundings. Then the heat is removed by the coolant. Flow-related parameters (such as velocity) change with time, and the flow of the fluid at this time is called unsteady flow. The convergence variables are

X

-velocity,

Y

-velocity,

Z

-velocity, continuity equation, and energy. The convergent criteria were defined that all these residuals were less than 0.00001.

In this simulation, three factors were considered to evaluate the performance of cooling plate: pressure drop (

Δ P

), maximum temperature of cooling plate (

T_{\max}

), and standard deviation of temperature (

T_{σ}

). All three factors can be monitored by STAR-CCM+.

The pressure drop between the inlet and outlet reflects the pump power consumption. The battery power will reduce with the decreasing of the flow resistance of the coolant. This paper did not just simulate a single U-shaped channel, it also simulated many different vortex generators inside the cooling plate. Fig. 5 shows the drag force and resistance of fluid around a three-dimensional object. Therefore, the formula for calculating the pressure drop of an interior channel as follows:

F_{D} = F_{f} + F_{p}

(2)

where

F_{D}

= total resistance;

F_{f}

= frictional resistance, which is caused by the shearing stress from the vortex generator; and

F_{p}

= pressure resistance, which is caused by the normal stress from the vortex generator

F_{D} = C_{D} \frac{ρ u_{0}^{2}}{2} A_{D}, F_{f} = C_{f} \frac{ρ u_{0}^{2}}{2} A_{f}, F_{p} = C_{z} \frac{ρ u_{0}^{2}}{2} A_{D}

(3)

where

C_{D}

= total resistance coefficient;

C_{f}

= frictional resistance coefficient;

C_{z}

= pressure resistance coefficient;

ρ

= density of coolant;

u_{0}

= velocity of fluid;

A_{D}

= projected area perpendicular to the direction of flow; and

A_{f}

= superficial area of the vortex generator. And the pressure drop of the channel without a vortex generator is as follows:

Δ p = λ \frac{L}{D} \frac{ρ u_{0}^{2}}{2}

(4)

where

λ = 4 C_{f}

;

L

= length of channel;

D

= hydraulic diameter of the channel; and

u_{0}

= velocity of fluid.

Fig. 5. Drag force and resistance of fluid around a three-dimensional object.

The maximum temperature represents the most severe environment of the battery. Once the maximum temperature exceeds the warning value, it will pose great danger to human life. The standard deviation of temperature implies the uniformity of a battery’s temperature distribution. It is also an important factor to reflect the performance of a battery

T_{σ} = \sqrt{\frac{\sum_{N} {(T_{N} - T_{ave})}^{2} A_{N}}{\sum_{N} A_{N}}}

(5)

where

N

= number of surface grids;

A_{N}

= area corresponding to the different grids;

T_{N}

= surface temperature (K) of a single grid; and

T_{ave}

= average temperature (K) of the section.

This research was mainly carried out under unsteady-state conditions. The solution results are mainly iterated according to the continuity equation, momentum equation, and energy equation of the fluid. The corresponding solution formulas are as follows:

•

Mass conservation equation

\frac{\partial (ρ u)}{\partial x} + \frac{\partial (ρ v)}{\partial y} + \frac{\partial (ρ w)}{\partial z} = \frac{\partial ρ}{\partial t}

(6)

where

ρ

= density of the coolant (

kg / m^{3}

); and

u

,

v

, and

w

= velocity vectors of the refrigerant in three coordinates (

m / s

).

•

Momentum conservation equations

d i v (ρ u \vec{u}) + \frac{\partial u}{\partial t} = ρ f_{x} + d i v (μ g r a d u) - \frac{\partial p}{\partial x}

(7)

d i v (ρ v \vec{u}) + \frac{\partial v}{\partial t} = ρ f_{y} + d i v (μ g r a d v) - \frac{\partial p}{\partial y}

(8)

d i v (ρ w \vec{u}) + \frac{\partial w}{\partial t} = ρ f_{z} + d i v (μ g r a d w) - \frac{\partial p}{\partial z}

(9)

where

\vec{u}

= velocity vector of the coolant (

m / s

); and

f_{x}

,

f_{y}

, and

f_{z}

= body force of the refrigerant in three coordinates.

•

Energy conservation equation

ρ \cdot C_{p} \cdot (\frac{\partial T}{\partial t} + u \cdot \frac{\partial T}{\partial x} + v \cdot \frac{\partial T}{\partial y} + w \cdot \frac{\partial T}{\partial z}) = α \cdot (\frac{\partial^{2} T}{\partial x^{2}} + \frac{\partial^{2} T}{\partial y^{2}} + \frac{\partial^{2} T}{\partial z^{2}})

(10)

where

C_{p}

= specific heat capacity [

J / (kg \cdot ° C)

]; and

α

= thermal conductivity [

W / (m ° C)

].

The accuracy of simulation is not only related to the choice of model, it is also affected by the size of the grid and calculation step. Because this research considered both the solid region and the fluid region, the interface of these two regions needed to use more prism layers to improve the convergence of the simulation. In order to eliminate the influence of grid size on experimental results and ensure unnecessary waste of computing resources. Seven different grids from 570,000 to 7,950,000 were set up for one type of cooling plate, the minimum relative size was 15% for the fillet area, and the calculation step was 5,000 steps. Several grids are discussed, and the total resistance of the monitoring system is shown in Fig. 6. The resistance values of grids smaller than 4 mm are all around 714.2 Pa. Based on there being 7,950,000 grids, the pressure drop and outlet temperature errors of grids over 1,000,000 are within 1%. In these simulations, it was found that all residual values can be kept below

3 \times 10^{- 5}

after 10,000 steps. Under the premise of ensuring accuracy and computational cost, the minimum relative size was 15% and the mesh size was 2 mm. Fig. 7 shows the meshing domain. The prism meshing parameters were set to initial height = 0.1, height ratio = 1.5, and number of layers = 3.

Fig. 6. Effects of different mesh sizes on simulation results: (a) pressure loss; and (b) outlet temperature.

Fig. 7. Mesh of cooling plate: (a) full domain; and (b) section.

Results and Discussions

Effect of Different Structures

The coolant flowed from the left side of these cooling plates and out of the right side. The inlet and outlet were both set in the top of the cooling plates, which are convenient for manufacturing. As shown in Fig. 8, the pressure loss (

Δ P

), the maximum temperature of cooling plate (

T_{\max}

), and standard deviation of the temperature (

T_{σ}

) change with the discharge of battery under the mass flow rate of

0.0223 kg / s

.

Fig. 8. Effect of different structures: (a) pressure loss; (b) maximum temperature; and (c) standard deviation of temperature.

It can be seen in Fig. 8(a) that the cooling plate with the convex structure had the least resistance because of its more reasonable structure, followed by the U-shaped structure, the airfoil structure, and the honeycomb structure, which has the greatest resistance. At the highest flow rate, the pressure loss of the honeycomb arrangement exceeded 72.1% of the convex structure and 51.5% of the airfoil structure under the same flow rate, which undoubtedly greatly increases the power consumption, and it is unreasonable to adopt the cooling plate with honeycomb structure and airfoil structure.

Fig. 8(b) shows the maximum temperature of the cooling plate. It can be seen that all these structures displayed a similar maximum temperature. The largest temperature was given by the convex structure, which reached 305.1 K. The smallest temperature was 304.5 K of the U-shaped structure. Therefore, the main influence factors of the maximum temperature are not the structure of the internal fluid. As shown in Fig. 9, we can clearly see that the area on the right of the outlet is hotter. This is because as the fluid flows inside the channel, it absorbs the heat from outside constantly, so the temperature will increase along with the flow direction. Additionally, the edge of the cooling plate has the no fluid through it. The area, as a result, would be the most dangerous part of the battery.

Fig. 9. Temperature nephograms of cooling plates: (a) convex structure; (b) honeycomb structure; (c) airfoil structure; and (d) U-shaped structure.

The internal channel has little effect on the maximum temperature of the cooling plate, but changes the standard deviation of the temperature greatly. We can find that honeycomb structure not only has the maximal resistance; it also has the worst temperature uniformity. From Fig. 8(c) we can find the standard deviation is 0.98% smaller than the honeycomb structure. Except the honeycomb structure, the other three structures behave with similar temperature uniformity.

According to the preceding analysis, the cooling plate with the convex structure can meet the requirements of minimum resistance as well as the good heat transfer effect.

Effect of Different Flow Rates

As shown in Fig. 10, in order to explore the performance of cooling plates with different mass flow rates, the inlet mass flow rates were set as 0.00558, 0.1115, 0.0223, and

0.0446 kg / s

; the simulation range is wide enough to compare the four structures. The pressure loss with different flow rates are shown in Fig. 10(a). The increase of

Δ P

corresponds to Eq. (3), which is the quadratic function of the mass flow rate. In the figure, we can see that the four kinds of cooling plates have similar

Δ P

at low mass flow rate. Additionally, the airfoil structure and honeycomb structure both increased more quickly than the other two. In other words, in the case of power consumption, neither the airfoil structure nor the honeycomb structure is the best choice.

Fig. 10. Comparisons of the different structures with different mass flow rates: (a) pressure loss; (b) maximum temperature; and (c) standard deviation of temperature.

In Fig. 10(b), the maximum temperature of the cooling plate always appeared at the edge of the outlet. And the maximum temperature of structures was less than 1 K at different mass flow rates, so it can be inferred that most of those types of cooling plates would have the same problem at a certain temperature. At the same time, the different between the minimum and maximum temperatures should not surpass 10 K; otherwise, the batteries’ efficiency would be affected and human lives could be threatened. Therefore the mass flow rate of this situation should be no less than

0.0223 kg / s

.

As shown in Fig. 10(c), the honeycomb had the highest standard deviation temperature of the structures. But with the mass flow rates increase, the differences became smaller. This is because

Δ T

between the inlet and outlet decreased as the velocity of fluid increased. As for the other three structures, no matter how the mass flow rates change, they all had the same value of

T_{σ}

.

Conclusions

By comparing and analyzing four liquid-cooled plates, it was found that the cooling plate with the convex structure has the lowest pressure loss at different flow rates, and the cooling plate resistances of the honeycomb structure and the airfoil structure were the largest.

The heat exchange capacities of several cooling plates were basically the same, but the temperature uniformity of the honeycomb structure was the worst and the cooling capacity of the cooling plate was greatly affected by the flow rate. The maximum temperature of these types of cooling plate always exists at a certain mass flow rate, which can hardly be eliminated. In order to ensure the temperature difference of the cooling plate inlet and outlet (

\leq 10 K

), it is necessary that the mass flow rate of the cooling plate is no less than

0.0223 kg / s

. Increasing the mass flow rate is an effective way to cool batteries in a short time.

This paper only simulated the heating of one of the batteries, using the average heat flux, and did not change the battery heating with time under different working conditions. Future work will start from these two aspects and be more closely related to the real situation of EVs.

Notation

The following symbols are used in this paper:

$A_{D}$: projected area perpendicular to the direction of flow ( $m^{2}$ );
$A_{f}$: superficial area of the vortex generator ( $m^{2}$ );
$A_{N}$: area corresponding to the different grids ( $m^{2}$ );
$C_{D}$: total resistance coefficient = 1;
$C_{p}$: specific heat capacity J/(kg·°C)
$C_{z}$: pressure resistance coefficient = 1;
$D$: hydraulic diameter of the channel (m);
$F_{D}$: total resistance (N);
$F_{f}$: frictional resistance (N);
$F_{p}$: pressure resistance (N);
$L$: length of channel (m);
$N$: number of surface grids = 1;
$P$: pressure (Pa);
$t$: time (s);
$T_{ave}$: average temperature of the section (K);
$T_{\max}$: maximum temperature of the cooling plate (K);
$T_{N}$: surface temperature of a single grid (K);
$T_{σ}$: standard deviation of the temperature distribution (K);
$\vec{u}$: velocity vector of the coolant ( $m / s$ );
$u_{0}$: velocity of fluid ( $m / s$ );
$x$: $x$ -axis direction;
$y$: $y$ -axis direction;
$z$: $z$ -axis direction;
$Δ P$: pressure drop (Pa);
$λ$: dimensionless friction factor = 1;
$μ$: dynamic viscosity ( $Pa \cdot s$ ); and
$ρ$: density ( $kg / m^{3}$ ).

Data Availability Statement

Some or all data, models, or code generated or used during the study are available in a repository online in accordance with funder data retention policies.

Acknowledgments

The authors wish to acknowledge the financial support from the Jilin Province Technology Breakthrough Project (Project No. 20190101121gg) for the work reported in this paper.

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Information & Authors

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Published In

Journal of Energy Engineering

Volume 146 • Issue 4 • August 2020

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This work is made available under the terms of the Creative Commons Attribution 4.0 International license, https://creativecommons.org/licenses/by/4.0/.

History

Received: Jan 27, 2019

Accepted: Sep 20, 2019

Published online: Jun 10, 2020

Published in print: Aug 1, 2020

Discussion open until: Nov 10, 2020

Authors

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Ming Li

Professor, State Key Laboratory of Automotive Simulation and Control, Jilin Univ., Changchun 130025, China.

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Jianchao Wang

Graduate Student, College of Automotive Engineering, Jilin Univ., Changchun 130025, China.

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Qin Guo https://orcid.org/0000-0001-8754-3734 [email protected]

Ph.D. Student, College of Computer Science and Technology, Jilin Univ., Changchun, Jilin Province 130025, China (corresponding author). ORCID: https://orcid.org/0000-0001-8754-3734. Email: [email protected]

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Yue Li

Senior Engineer, Vehicle Development Dept., Product and Research Center of First Automobile Work Shop, 1063 Chuangye St., Lvyuan District, Changchun 130011, China.

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Qingfeng Xue

Senior Engineer, Vehicle Development Dept., Product and Research Center of First Automobile Work Shop, 1063 Chuangye St., Lvyuan District, Changchun 130011, China.

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Guihe Qin

Professor, College of Computer Science and Technology, Jilin Univ., Changchun, Jilin Province 130025, China.

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Numerical Analysis of Cooling Plates with Different Structures for Electric Vehicle Battery Thermal Management Systems

Abstract

Introduction

Design and Analysis of the Cooling Plate

Simulation Model

Simulation Conditions

Results and Discussions

Effect of Different Structures

Effect of Different Flow Rates

Conclusions

Notation

Data Availability Statement

Acknowledgments

References

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Abstract

Introduction

Design and Analysis of the Cooling Plate

Simulation Model

Simulation Conditions

Results and Discussions

Effect of Different Structures

Effect of Different Flow Rates

Conclusions

Notation

Data Availability Statement

Acknowledgments

References

Information

Published In

Copyright

History

Authors

Affiliations

Metrics

Citations

Download citation

Cited by

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