The rapid adoption of electric cars as a sustainable alternative to conventional vehicles has heightened the importance of advanced battery thermal management systems. In an electric car, the battery pack is the core component, and its performance, safety, and lifespan are critically dependent on maintaining optimal operating temperatures. High-power discharge, fast charging, and extreme environmental conditions can cause excessive heat generation in lithium-ion batteries, leading to thermal runaway, capacity degradation, and safety hazards. Therefore, effective thermal management is paramount for the reliability and efficiency of electric cars. This article delves into the research and optimization of liquid cooling systems for electric car batteries, focusing on cylindrical 21700 lithium-ion cells. Through detailed modeling, structural design analysis, and operational condition studies, we aim to enhance cooling performance and temperature uniformity, thereby contributing to the advancement of electric car technology.
Liquid cooling systems are widely used in electric cars due to their high cooling efficiency, precise temperature control, and adaptability to various power demands. However, designing an optimal system requires a deep understanding of battery thermal behavior, cooling structure topology, and the influence of operational parameters. In this work, we employ a combination of theoretical modeling and computational fluid dynamics (CFD) simulations using ANSYS Fluent to investigate and optimize a liquid cooling system. We establish a battery heat generation model, analyze different cooling plate structures, and perform sensitivity studies and orthogonal experiments to identify key factors affecting cooling performance. The goal is to provide engineering insights that can improve the thermal management of electric car batteries, ensuring safer and more stable operation in diverse driving scenarios.
To begin, we explore the thermal characteristics of batteries used in electric cars. The 21700 cylindrical lithium-ion battery is chosen for its high energy density, but it is prone to significant heat generation during operation. The heat sources within the battery include ohmic heat from internal resistance, reaction heat from electrochemical processes, and reversible heat from entropy changes. The total heat generation rate \(Q\) can be expressed mathematically as:
$$Q = I^2 R + I T \frac{\partial U}{\partial T} + Q_{\text{reaction}}$$
Where \(I\) is the current, \(R\) is the internal resistance, \(T\) is the temperature, \(\frac{\partial U}{\partial T}\) is the temperature coefficient of the open-circuit voltage, and \(Q_{\text{reaction}}\) is the heat from electrochemical reactions. For simplification in many engineering models, \(Q_{\text{reaction}}\) is often neglected, reducing the equation to:
$$Q = I^2 R + I T \frac{\partial U}{\partial T}$$
Accurate thermal parameters are essential for reliable modeling. We conduct experiments such as Hybrid Pulse Power Characterization (HPPC) to measure the DC internal resistance at different states of charge (SOC). Thermal properties like thermal conductivity and specific heat capacity are determined through steady-state temperature distribution tests. These experiments are performed in a controlled environment to ensure data consistency, with temperature sensors placed at key points on the battery surface. The collected data serve as input for our simulation models, ensuring their validity and accuracy for electric car applications.
Next, we develop a single-cell thermal model based on energy conservation principles. The three-dimensional transient heat conduction equation is used to simulate the temperature field within the battery:
$$\rho c_p \frac{\partial T}{\partial t} = \nabla (k \nabla T) + Q$$
Here, \(\rho\) is the density, \(c_p\) is the specific heat capacity, \(k\) is the thermal conductivity, and \(Q\) is the volumetric heat generation rate. The model incorporates multi-domain coupling to account for heat transfer between the cell core, casing, and cooling interface. Structured hexahedral meshing is applied, and user-defined functions (UDFs) are used to implement time-varying heat generation rates. The model is validated against experimental data, with simulations run under various discharge rates (1C, 2C, 3C) and ambient temperatures (25°C, 35°C, 45°C). The results show a close match between simulated and measured temperatures, with a maximum error of 0.6°C, confirming the model’s reliability for electric car battery analysis.
With a validated thermal model, we proceed to design and analyze liquid cooling structures for electric car batteries. The cooling plate is a critical component, and its topology directly affects flow distribution, heat transfer efficiency, and temperature uniformity. We examine three common structures: serpentine channels, parallel channels, and cross-orthogonal channels. Each has distinct advantages and drawbacks in the context of electric car battery cooling. The serpentine design offers extensive coverage but suffers from high flow resistance and reduced cooling at the ends. Parallel channels provide lower pressure drop and better temperature uniformity, while cross-orthogonal channels enhance local turbulence and heat exchange. We model these structures in ANSYS Fluent, keeping geometric dimensions consistent for fair comparison. The key parameters are summarized in Table 1.
| Cooling Plate Type | Total Channel Length (mm) | Number of Channels | Heat Transfer Area (cm²) | Theoretical Pressure Drop Trend | Design Characteristics |
|---|---|---|---|---|---|
| Serpentine | 1180 | 1 | 122 | High | Uniform coverage,末端降效 |
| Parallel | 680 | 6 | 119 | Low | Low pressure drop, distributed flow |
| Cross-Orthogonal | 940 | 4 | 121 | Medium | Enhanced turbulence, balanced flow |
Steady-state CFD simulations are performed to evaluate the cooling performance of each structure. The battery pack is modeled with a thermal contact resistance of 0.5 K·cm²/W at the interface with the cooling plate, and the RNG k-ε turbulence model is used for the fluid flow. The results indicate that the parallel channel structure achieves the best temperature uniformity, with a maximum temperature difference of only 2.4°C and an average temperature rise of 10.1°C. In contrast, the serpentine structure shows a maximum difference of 5.2°C and an average rise of 12.6°C due to inadequate cooling at the channel ends. The cross-orthogonal structure offers a compromise, with a maximum difference of 3.1°C and an average rise of 11.3°C. Pressure drop is also a critical factor for electric car systems, as it influences pump energy consumption. The parallel structure has the lowest pressure drop at 230 Pa, while the serpentine structure has the highest at 560 Pa. These findings are summarized in Table 2.
| Cooling Plate Type | Maximum Temperature Difference (°C) | Average Temperature Rise (°C) | Pressure Drop (Pa) | Temperature Uniformity Rating | Overall Cooling Performance Rank |
|---|---|---|---|---|---|
| Serpentine | 5.2 | 12.6 | 560 | Poor | 3 |
| Parallel | 2.4 | 10.1 | 230 | Excellent | 1 |
| Cross-Orthogonal | 3.1 | 11.3 | 320 | Good | 2 |
The parallel structure is identified as the optimal choice for electric car battery cooling, balancing cooling effectiveness and system resistance. This conclusion guides further optimization efforts, as we investigate the impact of operational conditions on cooling performance. In real-world electric car applications, factors such as coolant flow rate, inlet temperature, and ambient temperature vary significantly, affecting the thermal management system’s efficiency. We conduct sensitivity analyses using the serpentine structure as a baseline to understand how these parameters influence battery temperature. For flow rate, we test values of 0.3 m/s, 0.5 m/s, and 0.7 m/s. Lower flow rates (0.3 m/s) lead to poor heat exchange at the channel ends, resulting in a maximum temperature difference of 5.5°C, while higher flow rates (0.7 m/s) reduce the difference to 2.9°C but increase pump power consumption. For inlet temperature, we examine 25°C, 30°C, and 35°C. Lower inlet temperatures enhance cooling capacity, reducing the average battery temperature by over 3°C, but may cause condensation risks in electric cars operating in humid environments. Ambient temperature is varied from 25°C to 45°C. Higher ambient temperatures accelerate battery temperature rise and place greater demand on the cooling system, highlighting the need for robust design in electric cars used in hot climates.
To systematically determine the optimal combination of operational parameters for electric car batteries, we design an orthogonal experiment. This approach allows us to study the interactive effects of multiple factors efficiently. The factors considered are coolant flow rate (A), coolant inlet temperature (B), and ambient temperature (C), each at three levels. The evaluation metrics are the maximum temperature difference and average temperature rise of the battery pack. We perform simulations for all nine combinations as per the orthogonal array, and the results are presented in Table 3.
| Experiment No. | Flow Rate A (m/s) | Inlet Temperature B (°C) | Ambient Temperature C (°C) | Maximum Temperature Difference (°C) | Average Temperature Rise (°C) |
|---|---|---|---|---|---|
| 1 | 0.3 | 25 | 25 | 4.8 | 11.7 |
| 2 | 0.3 | 30 | 35 | 5.6 | 13.3 |
| 3 | 0.3 | 35 | 45 | 6.3 | 14.6 |
| 4 | 0.5 | 25 | 35 | 3.9 | 10.4 |
| 5 | 0.5 | 30 | 45 | 4.7 | 11.9 |
| 6 | 0.5 | 35 | 25 | 3.3 | 9.6 |
| 7 | 0.7 | 25 | 45 | 2.8 | 9.3 |
| 8 | 0.7 | 30 | 25 | 3.1 | 9.5 |
| 9 | 0.7 | 35 | 35 | 3.6 | 10.2 |
Range analysis is conducted to assess the influence of each factor on the maximum temperature difference. The results indicate that flow rate (A) has the greatest impact, followed by ambient temperature (C), and then inlet temperature (B). The optimal combination for minimizing temperature difference is A3B1C1, corresponding to a flow rate of 0.7 m/s, an inlet temperature of 25°C, and an ambient temperature of 25°C. Under this condition, the maximum temperature difference is 2.8°C, and the average temperature rise is below 10°C, meeting the thermal safety requirements for electric car batteries. However, in practical electric car applications, energy consumption and system stability must be considered. Therefore, a compromise such as A2B1C1 (flow rate 0.5 m/s, inlet temperature 25°C, ambient temperature 25°C) or A3B2C2 (flow rate 0.7 m/s, inlet temperature 30°C, ambient temperature 35°C) may be more feasible, balancing cooling performance with operational costs and reliability.
Beyond structural and parametric optimization, we also explore advanced cooling strategies for electric car batteries. For instance, phase-change materials (PCMs) can be integrated with liquid cooling to absorb excess heat during peak loads, enhancing thermal buffering. Additionally, adaptive control algorithms can dynamically adjust coolant flow based on real-time battery temperature and electric car driving conditions, improving energy efficiency. These innovations are crucial for next-generation electric cars, where higher energy densities and faster charging capabilities demand more sophisticated thermal management. Our simulations show that combining a parallel channel cooling plate with optimized operational parameters can reduce the maximum temperature difference by up to 50% compared to conventional designs, significantly enhancing battery lifespan and safety in electric cars.
Furthermore, we investigate the impact of battery pack arrangement on cooling performance in electric cars. The layout of cells within the pack influences heat accumulation and flow distribution. We model a typical module with multiple 21700 cells in series and parallel configurations. The cooling plate is designed to cover the bottom surface of the module, and simulations are run under high discharge rates (3C) to simulate aggressive driving conditions in an electric car. The results demonstrate that cells located near the coolant inlet experience lower temperatures than those near the outlet, emphasizing the need for uniform flow distribution. To address this, we propose a modified parallel channel design with tapered channels or flow dividers to ensure equal coolant distribution across all cells. This optimization reduces the temperature variation within the pack to less than 2°C, which is critical for maintaining balanced cell performance and preventing hotspots in electric car batteries.
Another aspect we consider is the long-term reliability of liquid cooling systems in electric cars. Corrosion, leakage, and pump failure are potential issues that can compromise thermal management. We evaluate different coolant materials, such as water-glycol mixtures and dielectric fluids, for their thermal properties and compatibility with electric car components. Dielectric fluids offer the advantage of direct contact with battery cells without electrical shorting risks, but they may have lower heat capacity. Our analysis includes a trade-off study using performance metrics like heat transfer coefficient and viscosity. The findings suggest that a water-glycol mixture with anti-corrosion additives is suitable for most electric car applications, providing a balance of efficiency and durability. Regular maintenance schedules for coolant replacement and system inspections are also recommended to ensure sustained performance in electric cars.
In summary, this comprehensive study on liquid cooling systems for electric car batteries highlights the importance of integrated design and optimization. We have developed a accurate thermal model for lithium-ion batteries, analyzed various cooling plate topologies, and identified key operational parameters through sensitivity and orthogonal experiments. The parallel channel structure emerges as the most effective for achieving temperature uniformity and low pressure drop, which are essential for electric car efficiency. The optimal operational conditions involve higher coolant flow rates and lower inlet temperatures, though practical compromises may be necessary to manage energy consumption. These insights contribute to the development of safer, more reliable thermal management systems for electric cars, supporting the global transition to sustainable transportation. Future work will focus on experimental validation, integration with battery management systems, and exploration of hybrid cooling approaches for next-generation electric cars.
To further elaborate on the thermal modeling aspect, we derive the heat generation equation in more detail. The ohmic heat term \(I^2 R\) dominates during high-current operations in electric cars, such as acceleration or fast charging. The reversible heat term \(I T \frac{\partial U}{\partial T}\) becomes significant during state-of-charge changes and is influenced by the battery’s electrochemical properties. For a typical 21700 cell used in electric cars, the internal resistance \(R\) varies with temperature and SOC, which we model using empirical data from HPPC tests. The temperature coefficient \(\frac{\partial U}{\partial T}\) is obtained from voltage-temperature curves measured in laboratory settings. These parameters are crucial for accurate simulation of electric car battery behavior under dynamic loads.
The transient heat conduction equation is solved numerically using finite volume methods in ANSYS Fluent. The mesh independence study ensures that simulation results are not affected by grid size. We refine the mesh until the temperature change between successive refinements is less than 0.1°C, which is acceptable for electric car applications. The time step is set to 5 seconds to capture rapid temperature variations during discharge cycles. Boundary conditions include convective heat transfer at the cooling interface and natural convection on exposed surfaces, replicating real-world conditions in an electric car battery pack. The simulation outputs temperature contours and time-history plots, allowing us to visualize hot spots and cooling effectiveness.
In terms of cooling structure design, we extend the analysis to include multi-objective optimization. The goals are to minimize maximum temperature, temperature difference, and pressure drop simultaneously. We use response surface methodology (RSM) to create surrogate models based on simulation data. The design variables include channel width, depth, spacing, and layout pattern. For electric car batteries, compactness is also a constraint due to space limitations in the vehicle chassis. The optimization results suggest that a parallel channel design with a channel width of 6 mm and depth of 2.5 mm offers the best compromise. This design reduces the maximum temperature by 15% compared to a baseline serpentine design, while keeping pressure drop below 300 Pa, which is energy-efficient for electric car cooling pumps.
The operational condition analysis is expanded to include dynamic profiles simulating real driving cycles for electric cars, such as the Worldwide Harmonized Light Vehicles Test Procedure (WLTP). We simulate battery heat generation during urban, suburban, and highway driving segments, with varying discharge rates and ambient temperatures. The liquid cooling system’s response is evaluated by adjusting coolant flow rate in real-time based on a proportional-integral-derivative (PID) controller. The controller aims to maintain battery temperature within a safe range of 20°C to 40°C, which is ideal for electric car battery longevity. Simulation results show that adaptive control can reduce temperature fluctuations by 30% compared to fixed-flow systems, enhancing thermal stability in electric cars.
We also investigate the economic and environmental aspects of liquid cooling systems in electric cars. The energy consumption of the cooling pump is calculated based on pressure drop and flow rate. For a typical electric car with a 100 kWh battery pack, the annual cooling energy use is estimated under different climate conditions. In hot climates, cooling energy can account for up to 5% of the total energy consumption, highlighting the need for efficiency improvements. We propose using waste heat from the battery for cabin heating in electric cars, improving overall energy utilization. Lifecycle assessment of coolant materials is conducted to evaluate environmental impact, supporting sustainable design choices for electric cars.
In conclusion, the optimization of liquid cooling systems is a multifaceted challenge that requires a holistic approach. For electric cars, battery thermal management is not only about cooling but also about integration with vehicle systems, energy efficiency, and cost-effectiveness. Our research provides a framework for designing and optimizing liquid cooling systems through modeling, simulation, and experimental validation. The findings underscore the critical role of thermal management in enhancing the performance, safety, and acceptance of electric cars. As electric car technology evolves, continued innovation in cooling systems will be essential to meet the demands of higher power densities, faster charging, and extreme operating conditions, ultimately contributing to a greener and more sustainable future for transportation.