Yuhang Zhao1,*, Wenxu Yang2, Manyao Xie3
1College of Automotive Engineering, Jilin University, Changchun, China, 130025
2Beijing Jiaotong University, Beijing, China, 100044
3Xi’an Jiaotong University, Xi’an, China, 710049
*Corresponding author: Zhaoyh1522@mails.jlu.edu.cn
Keywords: Lithium-ion battery, Liquid cooling, Auxiliary cooling plates, Battery thermal management system (BTMS), Electric vehicle
Abstract: Liquid cooling is widely employed in battery thermal management system (BTMS) due to its superior thermal performance. However, conventional liquid cooling systems typically adopted a one-dimensional “plate-cell-plate” configuration, resulting in temperature non-uniformity and elevated core temperatures within the battery cells. To overcome these challenges, in this study, an innovative liquid cooling system incorporates auxiliary cooling plates was proposed. Compared to traditional cooling plates, it could enhance the heat transfer through its strategic orientation perpendicular to the primary plates. The results demonstrated that the system with auxiliary cooling plates exhibited superior cooling performance, which significantly reduced both the maximum temperature () and the temperature differential () of the battery pack. Compared to the system without auxiliary cooling plates, the high-temperature region ( > 10 K) decreased from 47.6 % to 32.1 % of the total pack volume, and the healthy region ( < 5 K) increased from 3 % to 29.4 %. Furthermore, investigation of auxiliary plate configuration revealed that a 7 mm-thick auxiliary plate could effectively control the temperature for four adjacent 30 mm-thick LiFePO₄ cells. Additionally, in a 12-module battery system, sharing primary cooling plates between adjacent modules could reduce the system volume, concurrently maintain the system thermal performance.
1. Introduction
| Nomenclature | |
| q | Heat Transfer Rate per Unit Area |
| t | Time |
| A | Area |
| C | Specific Heat Capacity |
| Pressure Drop | |
| Ambient Temperature | |
| Maximum Temperature | |
| Maximum Temperature Differential | |
| Temperature Rise | |
| BTMS | Battery Thermal Management Systems |
| CFD | Computational Fluid Dynamics |
| EVs | Electric Vehicles |
| PCM | Phase Change Material |
| SOC | State of Charge |
| UDF | User Defined Function |
| Greek Letters | |
| Heat Transfer Rate | |
| Thermal Conductivity | |
| Density | |
In recent years, as environmental issues have become one of the global focal points, electric vehicles (EVs) have entered the public spotlight due to their eco-friendly features[1]. As one of the most critical components, the power battery is closely tied to the performance and reliability of the EVs. Among the batteries, lithium-ion batteries have been widely adopted in the field of power batteries due to their high energy density and low maintenance requirements[2,3]. However, research indicated that the performance and reliability of lithium-ion batteries are highly sensitive to temperature variations[4]. Temperature fluctuations can lead to a decline in battery performance. High maximum temperature () and temperature differential () could trigger the thermal runaway[5,6]. To mitigate these risks, it is essential to implement an efficient battery thermal management system (BTMS) to control the heat generation within battery packs.
Currently, four main cooling strategies are widely used in lithium-ion battery pack thermal management: air cooling, liquid cooling, phase change material (PCM) cooling and heat pipe cooling[7]. Air cooling was characterized by a compact structure and low cost[8]. However, its heat dissipation capability was limited, Wiriyasart et al.[9] investigated the thermal performance of air-cooling lithium-ion battery packs under natural convection conditions. Their results demonstrated that during 0.6 C rate discharging, the reached about 42 ℃, which represented an approximate temperature increase of 12 K compared to the 0.2 C rate condition. Akbarzadeh et al.[10] conducted a comparative analysis of air-cooling and liquid-cooling BTMS for high-energy lithium-ion battery modules. Their study demonstrated that under 2 C discharge rate with the power consumption of 0.5 W, air-cooling BTMS exhibited approximately 3 K higher in than liquid-cooling BTMS. Comparing to liquid-cooling BTMS, battery pack temperatures were often not effectively reduced in air-cooling BTMS under conditions of high discharge rate.
PCM cooling BTMS was widely regarded as a promising thermal-management solution, owing to its high latent-heat capacity and compact, noise-free design[11,12]. However, PCM cooling BTMS was currently constrained by a significant increase in system weight and size[13]. Moreover, hybrid cooling strategies combining PCM with liquid cooling have emerged as a prominent research focus. Liu et al.[14] innovatively encapsulated PCM within a liquid-cooled system and implemented a hierarchical fuzzy PID control strategy to optimize BTMS dynamically. This approach enhanced cooling efficacy while reducing energy consumption by over 70 %, maximizing PCM latent heat utilization. Rao et al.[15] developed a novel hybrid BTMS for power batteries by coupling PCM with mini-channels, and numerically investigated the effects of coolant flow rate, PCM phase-change temperature, and thermal conductivity () on its performance. When optimized for peak efficiency, the hybrid BTMS reduced the peak temperature rise () by 23.7 % compared with a PCM-only system. Although hybrid PCM solutions have notably enhanced BTMS performance, the intrinsic drawback of PCM-based cooling systems—the flammability of organic PCM and the increased weight and size of the battery module—remained unresolved[16].
Other researchers have focused on heat pipe technologies[17–20]. For instance, Lu et al.[21] integrated oscillating heat pipe (OHP) technology with liquid cooling to establish dual heat transfer pathways, achieving further optimization of the BTMS. Their study demonstrated that when employing graphene-based nanofluid as the coolant, the battery pack exhibited a 20.1 % reduction in equivalent thermal resistance and a 32.7 % decrease in , highlighting the promising potential of such hybrid BTMS. Jin et al.[22] proposed an innovative BTMS combining the flat heat pipe with liquid cooling, investigating the combined effects of ambient temperature () and coolant flow velocity at 2 C and 3 C discharge rates. The results demonstrated that this novel BTMS could effectively maintain the surface below 310 K. However, heat pipe cooling has not yet been widely adopted in EVs due to factors such as cost and other limitations[23,24].
Liquid cooling demonstrated exceptionally high cooling efficiency and capacity, enabling effective suppression of temperature rise in battery packs[25]. In liquid cooling BTMS, there are two primary approaches: (1) direct liquid cooling, where batteries are fully immersed in dielectric coolant, and (2) indirect liquid cooling, which employs strategically positioned cold plates within the battery pack assembly[26]. Currently, indirect liquid cooling represents the prevailing market solution for BTMS. Research efforts are increasingly focused on optimizing the structural design of cooling plates to enhance system performance.
A novel bio-inspired liquid cooling plate was developed by Yang et al.[27], incorporating vascular network architecture. The influence of three critical parameters, channel cross-sectional width, coolant initial temperature, and inlet flow velocity, was systematically investigated and evaluated for their effects on thermal regulation performance in BTMS. Subsequently, a reliability analysis was performed using radial basis function (RBF) neural networks combined with the Monte Carlo simulation. The study showed that the biomimetic cooling-plate configuration delivered higher thermal dissipation efficiency than conventional serpentine channels, the was reduced by 3.14 K. Furthermore, parametric analysis revealed that system reliability was predominantly governed by three key factors: coolant initial temperature, flow velocity, and thermophysical properties—density (), specific heat capacity (C).
Fu et al.[28] proposed a liquid cooling plate with a fully filled porous medium and systematically investigated the effects of porosity and material properties on battery thermal performance. The results indicated that battery packs incorporating filled porous media demonstrate superior thermal performance. When specific inlet flow velocities were maintained, both material selection and porosity were optimized. The system achieved up to a 7 K reduction in compared with nonporous configurations. Furthermore, by combining high porosity levels and porous media exhibiting superior thermal conductivities, the thermal management efficacy of battery packs was markedly enhanced. Fu et al.[29] investigated a novel cooling plate design featuring streamlined channels with honeycomb-inspired fins, and identified the optimal configuration for comprehensive battery performance through orthogonal experimental methods. The study revealed that this innovative cooling plate demonstrates exceptional thermal performance, successfully lowering the by 2 K. Yao et al.[30] explored the effects of state of charge (SOC) and temperature on battery internal resistance, while developing a novel cooling-channel design. By applying response surface methodology for optimization, the was lowered by 10.3 %, and the by 14.5 %.
Building upon prior discussions, extensive research on BTMS has focused on optimizing operating temperature and enhancing thermal uniformity. Among all the solutions, liquid cooling has become the predominant approach in industrial applications[31]. However, most liquid-cooling studies adopt a one-dimensional “cooling plate-battery cells-cooling plate” configuration. This approach resulted in linear temperature distributions across the battery pack, leading to high at the center of the battery pack and poor thermal uniformity. To achieve optimal cooling efficiency while minimizing production size and cost, a liquid cooling configuration with auxiliary cooling plates for BTMS was proposed. The effects of plate layout, coolant-flow direction, and piping configuration on key thermal performance metrics (peak temperature, temperature differential, coolant pressure drop) were systematically examined.
2. Model Development
2.1Physical Model
CFD simulations of prismatic LiFePO₄ battery thermal-management systems were carried out in STAR-CCM+ (v2402) using the model introduced by Yao et al[30]. The detailed specifications of the lithium-ion batteries are presented in Table 1. The schematic diagram of the investigated system is illustrated in Figure 1.
Table 1. Performance parameters of lithium-ion battery
| Parameter | Value |
| Nominal capacity | 50 Ah |
| Nominal voltage | 3.2 V |
| Weight | 1.78 kg |
| Dimensions (L×W×H) | 135 × 30 × 215 mm |
| Charge cut-off voltage | 3.6 V (100 % SOC) |
| Discharge cut-off voltage | 2.5 V (0 % SOC) |
| Max continuous discharge rate | 2 C |
| Peak pulse discharge rate | 5 C |
Figure 1 battery model and conditions
The coolant‐channel geometry and battery pack’s conditions were illustrated in Figure 2. The serpentine coolant channel increases the heat‐transfer surface area without sacrificing flow velocity, thereby improving temperature uniformity relative to conventional serial layouts and earning widespread adoption in practical systems[32]. Aluminum was selected as the material for the liquid cooling plates. The material properties of the battery cells and cooling plates are summarized in Table 2. A 50% ethylene glycol/water solution was employed as the coolant, with its thermophysical properties likewise presented in Table 2[33,34].
Figure 2 Schematic of cooling system (a) coolant channel, (b) battery pack
Table 2 Thermophysical properties of liquid cooling plate, battery and coolant.
| Component | (kg/m³) | (J/(kg‧K)) | (W/(m·K)) |
| Al | 2702.0 | 903.0 | 237.0 |
| Positive pole | 2840.0 | 1260.4 | 1.48 |
| Negative pole | 1671.0 | 1064.0 | 1.04 |
| Battery body | 2044.2 | 1235.0 | 0.9 (x) / 9.7 (y) / 9.7 (z) |
| Coolant | 1071.1 | 3300 | 0.384 |
2.2Mathematical Model
2.2.1 Heat Transfer Mechanisms
The battery module’s heat transfer occurs through three primary pathways:
- Heat conduction
Heat conduction relies on the heat energy transfer generated by the thermal motion of microscopic particles when there is no relative displacement between macroscopic objects. This is the main way for heat transfer between the battery and the liquid cold plate and between the cells in contact with each other. Heat conduction can be fundamentally described by Fourier’s Law of Heat Conduction, expressed mathematically as[35]:
| (1) |
- Heat convection
Heat convection arises from the macroscopic motion of fluids, where the intermixing of hotter and cooler fluid regions enables thermal energy transfer. The heat transfer between a fluid flowing over a solid surface, termed convective heat transfer in engineering practice, is quantified using Newton’s Law of Cooling[35]:
| (2) |
- Radiative heat transfer
Radiative heat transfer, which occurs through electromagnetic wave emission from material surfaces, is calculated using a modified form of the Stefan-Boltzmann Law[35]:
| (3) |
Indeed, radiative heat transfer has a negligible impact on the overall thermal management system. To reduce computational complexity, this study deliberately neglects its contribution in all modeling and analyses.
2.2.2 Heat Generation
The heat generation in lithium-ion batteries primarily comprises three components: the battery reaction heat (), polarization heat (), Joule heat (). The total expressed as[36]:
| (4) | ||
In this study, the Bernardi Heat Generation Rate Model[37] was employed to calculate the heat production rate of lithium-ion batteries. The model assumes uniform internal heat generation and is widely adopted in lithium-ion battery thermal analyses. The rate of heat generation could be expressed as follows[38]:
| (5) |
By neglecting the temperature-dependent voltage term (typically a minor constant determined by electrochemical reactions[39]) and expressing the voltage difference as the product of battery current I and internal resistance [40], the Bernardi model simplifies to:
| (6) | ||
The battery’s internal resistance varies dynamically with operating conditions, including temperature, discharge rate, and state of charge (SOC). Therefore, based on the experimental data proposed by Yao et al.[30], the relationship between internal resistance (R), temperature (T), and state-of-charge (SOC) across various discharge rates was fitted, with the fitting results presented in Figure 3.
The state of charge (SOC) can be calculated using the constant-current discharge formula proposed by Cheng K W E et al.[40]:
| (7) |
- 5 C
(b) 1 C
(c) 2 C
Figure 3. The fitting results of the battery’s internal resistance (a) 0.5 C, (b) 1 C, (c) 2 C
The lithium-ion battery thermal model was developed based on three key assumptions[40,41]:
- Constant thermophysical properties (specific heat capacity, thermal conductivity).
- Homogeneous current density distribution.
- Spatially uniform heat generation rates.
The thermal model for lithium-ion batteries was derived:
| (8) |
2.3 Numerical Model
2.3.1 Fundamental Governing Equations of Computational Fluid Dynamics (CFD)
2.3.1.1 Continuity Equation
Continuity Equation, also known as the conservation of mass equation, means the rate of mass change within the control volume equals the net mass inflow[42].
| (9) |
2.3.1.2 Momentum Equations
The Navier-Stokes equations state that the sum of forces acting on a fluid element equals its momentum change rate. In the Navier-Stokes equations, the S term represents the source term accounting for momentum additions/external forces per unit volume[43].
| (10) |
2.3.1.3 Energy Conservation Equation
The energy conservation equation (First Law of Thermodynamics) in CFD states that the rate of energy change in a control volume equals the sum of net heat flux, work done by body forces, and surface forces. Here, the term specifically represents viscous dissipation[35].
| (11) |
2.3.2 Grid Independence Test and Model Validation
Grid independence analysis was assessed to verify the validity and accuracy of the numerical solutions. Eight mesh configurations for a single lithium‐ion battery cell and four mesh configurations for the battery pack cooling by an orthogonally arranged liquid‐cooling plate were generated for the study. The result was shown in Figure 4 (a) and (b), and the detailed quantitative data was reported in Table 3.
For the single‐cell model, once the element count exceeds 66340 (i.e., a base size of 2.4mm), further refinement produces only marginal changes (0.001%) in both and , with the changing by under 1%. In the battery pack, variations in mesh density predominantly originated from the discretization of coolant flow channels. Consequently, the maximum cell-across base sizes remained negligible (≤ 0.1 K). Once the grid number exceeded 1.58 million, the coolant pressure drop () increased at a rate of approximately 3 % for an additional 300,000 elements, indicating numerical convergence.
Figure 4 (a) The dependence of temperature parameters on the grid for a single cell.
Figure 4 (b) The variation of thermal parameters for the battery pack.
Table 3 Effect of grid size on a single cell and liquid cooling battery pack.
| No. (cell) | Grid number | Base size (mm) | (K) | e % = |
(K) | e % = |
| 1 | 8136 | 5.00 | 311.806 | 0.0158 | 0.703 | 9.29 |
| 2 | 20155 | 4.00 | 311.856 | 0.0053 | 0.768 | 3.69 |
| 3 | 37315 | 3.00 | 311.873 | 0.0016 | 0.796 | 1.17 |
| 4 | 66340 | 2.40 | 311.878 | 0.0010 | 0.806 | 0.73 |
| 5 | 84167 | 2.20 | 311.881 | 0.0004 | 0.812 | 0.71 |
| 6 | 109774 | 2.00 | 311.882 | -0.0005 | 0.818 | 0.46 |
| 7 | 239815 | 1.00 | 311.880 | 0.0011 | 0.821 | 0.89 |
| 8 | 517839 | 0.75 | 311.883 | ~ | 0.829 | ~ |
| No. (pack) | Grid number | Base size (mm) | (K) | e % = |
(Pa) | e % = |
| 1 | 1297022 | 2.00 | 320.119 | 0.0014 | 264.0 | 6.44 |
| 2 | 1582490 | 1.50 | 320.124 | -0.033 | 281.1 | 2.34 |
| 3 | 1861046 | 1.25 | 320.111 | 0.0295 | 287.6 | 3.05 |
| 4 | 2149583 | 1.00 | 320.016 | ~ | 296.4 | ~ |
The correctness of the numerical model for this model was validated by comparing with the experimental results of existing literature[30]. Initial validation of the battery model was performed by comparing its numerical result with the experimental data reported by Yao et al.[30]. LiFePO₄ cell was tested experimentally at an of 298 K and discharge rates of 0.5 C, 1 C, and 2 C. Numerical simulations employing identical cell parameters and boundary conditions were carried out, with the results plotted in Figure 5. As evidenced in Figure 5, due to non-ideal experimental conditions and discrepancies between actual and simulated boundary conditions, certain errors were inevitably introduced. Despite these deviations, excellent agreement was observed between the numerical and experimental data, with the deviation limited to 1.54 K and variations within 1 K.
- 5 C
- 1 C
- 2 C
Figure 5 Temperature rise curve of the battery surface (a) 0.5 C (b) 1 C (c) 2 C
To validate the liquid cooling plate model, numerical results were compared against experimental data from Yao et al.[30], using identical battery properties, coolant parameters, and boundary conditions. The results presented in Table 4 demonstrated a deviation of 0.54 K and a variation of approximately 0.9 K. Instabilities in the experimental airflow induced time‐dependent fluctuations in the convective heat‐transfer coefficient at the module surface; these measurement errors remained within acceptable limits. Figure 6(a) depicted the coolant velocity contour at the middle cross-section of the cooling channel, while Figure 6(b) reproduced the corresponding simulation results of Yao et al.[30]. Under identical boundary conditions (1 L/min inlet flow rate, 1 atm outlet pressure), both configurations exhibited nearly identical velocity profiles, and the across the cooling plates differed by only 4.8 % between experiment and simulation.
Table 4 Thermal parameters of the battery pack under 2C current discharging.
| /K | /K | /Pa | |
| experimental data | 311.65 | 8.7 | 279.76 |
| numerical results | 311.22 | 7.6 | 266.09 |
Figure 6 The flow velocity distribution across coolant channel cross-sections. (a) numerical results, (b) The results from Yao et al.[30]
3. Results and discussion
3.1 The Effect of Auxiliary Cooling Plates
In this section, a comprehensive analysis of thermal behavior with and without auxiliary cooling plates on the battery pack was carried out. Employing a 50 % ethylene glycol–water solution as the coolant, simulations were performed at 0.5 C, 1 C, and 2 C discharge rates with a constant inlet flow rate of 1 L/min. Auxiliary cooling plates situated between cells effectively extract heat from adjacent modules. It markedly enhanced temperature uniformity, especially during high‐current discharge operations, and maintained cell temperatures within safe operational limits under demanding conditions.
Figure 7 compared the temperature distributions of battery cross-sections with and without auxiliary cooling plates during 2 C rate discharge, clearly demonstrating that the use of auxiliary plates markedly reduced the high-temperature region. Specifically, the area exceeding 309 K (corresponding to a 10 K ) decreased substantially in the central cross-section. The high-temperature region ( > 10 K) decreased from 47.6 % to 32.1 % of total pack volume, while increasing the healthy region ( < 5 K) from 3 % to 29.4 %.
- without auxiliary cooling plates.
- with auxiliary cooling plates.
Figure 7 The temperature contours of battery cross-sections during 2 C-rate discharge (a) without auxiliary cooling plates, (b) with auxiliary cooling plates
Figure 8 presented the profiles of battery packs with and without auxiliary cooling plates at 0.5 C, 1 C, and 2 C discharge rates. The results demonstrated consistent thermal performance improvements across all tested rates. The auxiliary plates reduced by 15.4 % at 0.5 C, 13.7 % at 1 C, and 11.5 % at 2 C discharge conditions. These measurements validated the auxiliary cooling plates’ effectiveness throughout the operational range.
Figure 8 Temperature rise curve of the battery pack at (a) 0.5 C, (b) 1 C, (c) 2 C discharge rates
- The Effect of Auxiliary Cooling Plates Configuration
In this section, the cooling performance was evaluated concerning the arrangement of auxiliary cooling plates, aiming to identify an optimal geometric configuration that balances thermal management efficacy with cost-effectiveness.
Figure 9 (a) Configuration A with four auxiliary cooling plates.
Figure 9 (b) Configuration B with five auxiliary cooling plates.
Figure 9 (c) Configuration C with six auxiliary cooling plates.
Figure 9(a) depicted the arrangement of four auxiliary cooling plates within the battery pack and the corresponding central temperature distribution at a 2 C discharge rate. The analyzed cross-section was selected perpendicular to the primary cooling plate’s heat transfer direction. Cells directly contacting auxiliary cooling plates reached near 310 K. However, cells distal to the plates showed poorer cooling performance. The central rose to 321 K. The battery pack exhibited a of 21.39 K, while individual cells demonstrated an average of 10.21 K. At a configuration of 5 auxiliary cooling plates, as shown in Figure 9(b), the maximum distance of cells and auxiliary cooling plates was reduced significantly. As a result, the cooling efficiency improved substantially, lowering the pack’s by approximately 2.7 K. And the battery pack achieved a of 18.83 K. Individual cells exhibited an average of 13.01 K. The system exhibited improved overall temperature uniformity while showing a slight increase in average single-cell . This phenomenon could be explained by more high- cells—especially those between cooling plates and high-temperature cells. Figure 9(c) displayed the thermal performance with six auxiliary cooling plates, each cell in direct contact with a cooling surface. The battery pack’s was limited to 307.3 K. Table 5 then summarized the key metrics—, pack’s , and average cell‐level . Configuration C demonstrated exceptional thermal uniformity, with all cells maintaining nearly identical temperature differentials of 7.5 ± 0.2 K.
Table 5 Thermal performance of three configurations.
| Configuration | (K) | (K) (total) | (K) (avg) |
| a | 321.587 | 21.395 | 10.21 |
| b | 318.839 | 18.834 | 13.00 |
| c | 307.321 | 7.7486 | 7.563 |
Figure 10 presented the temperature rise curves for all three configurations. In configuration C, the plateaued at around 600 s, reflecting thermal equilibrium. This state arises from two coupled effects: larger thermal gradients enhance heat transfer at higher temperatures, while reduced internal resistance lowers heat generation. Configuration C thus provided a benchmark for high-capacity batteries under extended high-rate discharge.
Figure 10 Temperature rise curves for all three configurations.
- The effect of coolant flow path design
In this section, a systematic evaluation of the thermal performance implications of various coolant flow path designs was presented, offering fundamental guidelines for battery module configuration optimization.
|
|||
Figure 11 Schematic illustration of the battery module.
Coolant flow rates in the primary and auxiliary cooling plates were allocated in proportion to their cross-sectional areas. In configuration A, the inlet and outlet ports lay on opposite sides, as illustrated in Figure 11. In configuration B, one primary cooling plate was shared by two adjacent modules. Configuration C employed a single battery module. All cooling plates were linked through piping. This design achieved the cooling circuit with one inlet and one outlet.
Figure 12 displayed the temperature rise curves for various coolant flow path designs. The three configurations exhibited similar curve profiles. Configuration A showed a 0.33 K higher than Configuration B. However, it demonstrated better thermal uniformity with a 0.52 K smaller . This minimal variation occurred because all designs maintained identical cooling plate arrangements within individual battery modules. Consequently, their influence on thermal characteristics remained relatively minor.
Figure 12(a) The of various coolant flow path designs.
Figure 12(b) The of various coolant flow path designs.
Table 6 The total and of three configurations.
| Configuration | (K) | |
| A | 1002.149 | 309.551 |
| B | 2760.753 | 309.220 |
| C | 3728.854 | 308.614 |
Table 6 summarized the coolant data. Under a flow rate of 1 L/min, Configuration A exhibited superior hydraulic performance, with a total of only 1002.149 Pa. This efficiency arose from the alignment of flow paths between the modules and the primary cooling plates, which minimizes localized losses at junctions. In contrast, Configuration B displayed a substantially higher total (2760.753 Pa), attributable to increased localized losses. These losses were primarily generated at the interconnection pipes between adjacent battery modules, where flow redirection induces additional resistance. However, Configuration B demonstrated equivalent thermal performance despite its higher pressure drop. And this design could reduce cooling plate quantity in a 12-module system by 46 %, decreasing both system size and fluid volume. Where spatial constraints permit, increasing the proportion of Configuration B modules achieved corresponding cost savings. Configuration C exhibited a 25.9 % greater pressure drop than Configuration B, which meant isolated single-module installations should be avoided where possible.
4 Conclusions
In this study, a novel liquid cooling configuration incorporating auxiliary cooling plates was proposed. The design aimed to enhance the battery pack thermal performance while maintaining existing cooling‐plate specifications and ensuring straightforward installation. Important findings of this study were as follows:
- Auxiliary cooling plates significantly enhanced battery-pack thermal performance across varied charge rates under constant total coolant flow. The findings indicated that auxiliary cooling plates reduce maximum by 14 %, decreased the high-temperature volume fraction ( > 10 K) by 15.5 %, and expanded the low-temperature region ( < 5 K) by 26.4 %.
- In designs incorporating auxiliary cooling plates, strategic placement between battery cells critically enhanced thermal performance. Research demonstrated that a 7 mm-thick auxiliary plate effectively reduces temperatures for four adjacent 30 mm-thick LiFePO₄ cells. Experimental validation showed that integrating six such plates within a 12-cell module successfully constrained the to 307.2 K.
- In a 12-battery module system, the shared primary cooling plate configuration for adjacent battery modules achieved a 46 % reduction in cooling plate count while maintaining equivalent thermal performance.
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