As the global automotive industry transitions toward sustainable energy solutions, the development of high-performance electric vehicles has become a central focus. The EV battery pack serves as the core energy storage unit, directly influencing vehicle range, safety, and reliability. In this study, I present a systematic design and optimization process for an EV battery pack, addressing two critical aspects: the thermal management system and the structural integrity of the battery pack enclosure. Through comprehensive theoretical analysis, computational fluid dynamics simulations, and structural finite element analysis, I developed a liquid-cooled thermal management system and validated the mechanical performance of the battery pack housing under various operating conditions.
1. Thermal Characteristics of Lithium-Ion Batteries
Lithium-ion batteries are the most widely adopted power source for electric vehicles due to their high energy density, low self-discharge rate, and extended cycle life. Understanding the thermal behavior of these batteries is essential for designing an effective EV battery pack thermal management system. The internal structure of a lithium-ion battery consists of positive and negative electrodes, electrolyte, current collectors, and a separator membrane. The positive electrode materials commonly include lithium manganese oxide, lithium cobalt oxide, ternary lithium compounds, and lithium iron phosphate. Each material offers distinct advantages in terms of energy density, safety, and cost. The negative electrode typically employs graphite due to its excellent compatibility and stability. The electrolyte, usually containing lithium hexafluorophosphate, facilitates ion transport between electrodes, while the separator prevents direct electrical contact while allowing ionic conduction.
The working principle of lithium-ion batteries relies on the migration of lithium ions between the positive and negative electrodes during charge and discharge cycles. The heat generated during battery operation can be categorized into four components: electrochemical reaction heat, polarization heat, ohmic heat, and secondary reaction heat. The total heat generation rate can be expressed using the Bernadi battery heat generation rate model:
$$Q = I^2 R + I T \frac{\partial U_{ocv}}{\partial T}$$
where \(I\) represents the discharge current, \(R\) corresponds to the internal resistance, \(U_{ocv}\) denotes the open-circuit voltage, and \(T\) stands for the battery temperature. In this equation, the secondary reaction heat is neglected since it only becomes significant during thermal runaway conditions. The internal resistance of the battery is the primary contributor to temperature rise during normal operation.
Heat transfer in the EV battery pack occurs through three fundamental mechanisms: conduction, convection, and radiation. For battery thermal management, conduction and convection are the dominant modes. Fourier’s law describes the conductive heat transfer:
$$\Phi = -\lambda A \frac{dt}{dx}$$
where \(\lambda\) is the thermal conductivity, \(A\) represents the contact area, \(t\) is temperature, and \(x\) is the heat conduction distance. Newton’s law of cooling describes the convective heat transfer process:
$$\Phi = hA(t_w – t_f)$$
where \(h\) represents the convective heat transfer coefficient, \(t_w\) and \(t_f\) are the wall and fluid temperatures respectively. The convective heat transfer coefficient can be determined using the Nusselt number correlation:
$$h = \frac{Nu \cdot \lambda_l}{d}$$
where \(Nu\) represents the Nusselt number and \(d\) represents the characteristic length. The Reynolds number, which determines the flow regime, is expressed as:
$$Re = \frac{\rho u d}{\mu}$$
where \(\rho\) is the fluid density, \(u\) the flow velocity, and \(\mu\) the dynamic viscosity. The flow resistance within the cooling channels can be described by two components: frictional resistance along the channel and local resistance caused by flow disturbances:
$$\Delta P_{\lambda} = \lambda \cdot \frac{l}{d} \cdot \frac{\rho v^2}{2}$$
$$\Delta P_{\zeta} = \zeta \cdot \frac{\rho v^2}{2}$$
The computational fluid dynamics (CFD) approach was employed throughout this research to analyze the thermal and flow characteristics of the EV battery pack. The fundamental governing equations include the continuity equation, the Navier-Stokes momentum equation, and the energy conservation equation:
$$\nabla \cdot \mathbf{v} = 0$$
$$\rho \frac{\partial \mathbf{v}}{\partial t} + \rho(\mathbf{v} \cdot \nabla)\mathbf{v} = -\nabla P + \mu \nabla^2 \mathbf{v} + \rho \mathbf{g}$$
$$\rho c_p \frac{\partial T}{\partial t} + \rho c_p \mathbf{v} \cdot \nabla T = \nabla \cdot (\lambda_l \nabla T)$$
These equations form the basis for all subsequent thermal simulations of the EV battery pack system.
2. Battery Pack Configuration Design
The design of the EV battery pack began with determining the required energy capacity based on the target driving range. Reference was made to the NEDC cycle conditions for range calculation. The target vehicle parameters are summarized in the following table:
| Parameter | Value |
|---|---|
| Test mass (kg) | 1700 |
| Rolling resistance coefficient | 0.008 |
| Air drag coefficient | 0.36 |
| Frontal area (m²) | 2.47 |
| Rotational mass conversion factor | 1.06 |
| Transmission efficiency | 0.9 |
| Discharge depth | 0.9 |
| Discharge efficiency | 0.95 |
| Accessory power consumption rate | 0.1 |
To achieve a target driving range of 200 kilometers, I calculated the minimum battery capacity required. The NEDC cycle was divided into constant-speed and uniform-acceleration segments for energy consumption analysis. The total resistance during constant-speed operation includes rolling resistance and air drag:
$$W_f = \frac{mgfv t}{3.6\eta_1}$$
$$W_r = \frac{C_D A v^3 t}{21.15 \times 3.6 \times \eta_1}$$
For the uniform acceleration segments, additional acceleration resistance work is involved:
$$W_f’ = \frac{mgf(v_0 + 0.5at)t}{\eta_1 \times 3.6}$$
$$W_r’ = \frac{C_D A(v_0^3 + 3v_0^2 \cdot 0.5at + 3v_0(0.5at)^2 + (0.5at)^3)t}{\eta_1 \times 3.6 \times 21.15}$$
$$W_a = \frac{\delta m a (v_0 + 0.5at)t}{\eta_1 \times 3.6}$$
The calculated energy consumption for constant-speed segments was approximately 0.23 kWh, while the acceleration segments required approximately 1.46 kWh. Accounting for the discharge depth, discharge efficiency, and auxiliary power consumption, the total battery capacity requirement was determined as:
$$E_{total} = \frac{W + W’}{(\eta_2 \cdot \eta_3 \cdot (1-\eta_4))} \times \frac{ER}{D}$$
This calculation yielded a required battery capacity of approximately 39.53 kWh. To provide sufficient margin, I selected a battery pack configuration with a total capacity of 40.5 kWh.
For the motor power calculation, three performance criteria were evaluated: maximum speed, maximum gradeability, and maximum acceleration. The power required for maximum speed of 200 km/h was calculated as:
$$P_1 = \frac{1}{\eta_1} \left( \frac{m g f v_1}{3600} + \frac{C_D A v_1^3}{76140} \right) = 112.04 \text{ kW}$$
The power for maximum gradeability of 16.7° at 30 km/h was:
$$P_2 = \frac{1}{\eta_1} \left( \frac{m g f v_2 \cos\alpha}{3600} + \frac{m g v_2 \sin\alpha}{3600} + \frac{C_D A v_2^3}{76140} \right) = 45.86 \text{ kW}$$
The power for 0-100 km/h acceleration in 8 seconds was:
$$P_3 = 113.95 \text{ kW}$$
Therefore, the maximum discharge rate of the EV battery pack was determined to be 3C. I selected a ternary lithium battery as the cell type, with the following specifications:
| Parameter | Value |
|---|---|
| Nominal capacity (Ah) | 114 |
| Internal resistance (mΩ) | 0.4 |
| Density (kg/m³) | 2219 |
| Specific heat capacity (J/kg·K) | 1000 |
| Thermal conductivity x (W/m·K) | 2.5 |
| Thermal conductivity y (W/m·K) | 12 |
| Thermal conductivity z (W/m·K) | 12 |
The EV battery pack consisted of 96 cells connected in series, divided into three modules separated by insulating plates made of phenolic resin. This configuration yielded a battery pack voltage of 355.2V and total energy of 40.5 kWh.
3. Single Cell Thermal Simulation
I conducted thermal simulations of the single cell at various discharge rates to establish baseline temperature behavior without active cooling. Using the Bernadi heat generation model, the heat source powers for 1C, 2C, and 3C discharge rates were calculated as 5.2W, 20.8W, and 46.8W respectively, with corresponding discharge durations of 3600s, 1800s, and 1200s.
The simulation was performed using Fluent with a transient solver and SIMPLE algorithm. A convective heat transfer coefficient of 5 W/(m²·K) was applied to the cell surface to represent natural air convection at an ambient temperature of 25°C. The temperature rise curves showed a clear trend: higher discharge rates resulted in more rapid temperature increases. The simulation results are summarized below:
| Discharge Rate | Maximum Temperature (°C) | Temperature Rise (°C) |
|---|---|---|
| 1C | 33.03 | 8.03 |
| 2C | 43.08 | 18.08 |
| 3C | 53.41 | 28.41 |
At the 1C discharge rate, the battery temperature remained within the normal operating range. However, at 2C discharge, the temperature exceeded the recommended maximum of 40°C, and at 3C discharge, the temperature reached 53.41°C, demonstrating the critical need for an effective thermal management system in the EV battery pack.
The temperature distribution analysis revealed that the corners and edges of the battery exhibited lower temperatures due to natural convection with the surrounding air. The top and side surfaces showed higher temperatures, while the front surface displayed slightly lower temperatures due to the larger contact area with air. The internal temperature distribution confirmed that without active cooling, heat accumulates in the battery core, potentially degrading performance and safety.
4. Liquid Cooling System Design
Based on the thermal analysis results, I designed a liquid cooling plate system for the EV battery pack. The liquid cooling plate heat exchange method was selected due to its structural simplicity, cost-effectiveness, and superior cooling performance compared to air cooling methods.
4.1 Grid Independence Verification
Before conducting the cooling performance simulations, I performed a grid independence study to determine the optimal mesh configuration. Six mesh schemes were evaluated with element counts ranging from 313,557 to 1,827,666. The simulation results showed minimal variation in battery pack maximum temperature, with a difference of only 0.02°C between the coarsest and finest meshes. However, the flow resistance was more sensitive to mesh quality due to the complex structure of the liquid cooling plate. The results are presented below:
| Scheme | Mesh Size Setting | Element Count | Average Orthogonal Quality | Maximum Temperature (°C) | Flow Resistance (Pa) |
|---|---|---|---|---|---|
| 1 | 10:30 | 313557 | 0.75 | 30.26 | 858.08 |
| 2 | 7:30 | 593633 | 0.85 | 30.25 | 794.12 |
| 3 | 5:30 | 1044646 | 0.87 | 30.25 | 816.25 |
| 4 | 4:30 | 1495842 | 0.89 | 30.28 | 833.85 |
| 5 | 3:30 | 1627727 | 0.89 | 30.28 | 807.20 |
| 6 | 2:30 | 1827666 | 0.89 | 30.28 | 811.32 |
I selected scheme 5 with 1,627,727 elements as the optimal mesh configuration, balancing accuracy and computational efficiency.
4.2 Liquid Cooling Plate Structure Comparison
I designed two types of liquid cooling plates for comparison: a parallel channel serpentine flow structure (Scheme A) and a traditional single serpentine channel structure (Scheme B). Both plates measured the same overall dimensions with a channel width of 25mm and thickness of 6mm. The inlet and outlet ports were positioned on the same side with a diameter of 12mm.
The simulation results, obtained under identical boundary conditions with a coolant flow rate of 0.05 kg/s and heat generation rate of 5.2W per cell, are summarized below:
| Parameter | Scheme A (Parallel Channels) | Scheme B (Single Channel) |
|---|---|---|
| Maximum temperature (°C) | 30.28 | 29.92 |
| Temperature difference (°C) | 2.16 | 2.45 |
| Flow resistance (Pa) | 807.2 | 6878.71 |
Scheme B demonstrated slightly lower maximum temperature due to the higher coolant velocity maintained throughout a single continuous channel. However, its flow resistance was over eight times greater, which would significantly impact the energy consumption of the cooling system. Scheme A, with parallel channels that divide the flow, provided more uniform temperature distribution with significantly lower flow resistance. I selected Scheme A for further optimization.
4.3 Coolant Concentration Selection
I evaluated the cooling performance of five coolant media: pure water, 30wt% ethylene glycol solution, 50wt% ethylene glycol solution, and 70wt% ethylene glycol solution. The thermo-physical properties are listed below:
| Coolant | Density (kg/m³) | Specific Heat (J/kg·k) | Dynamic Viscosity (mPa·s) | Thermal Conductivity (W/m·K) | Freezing Point (°C) |
|---|---|---|---|---|---|
| Water | 997.3 | 4190 | 0.80 | 0.607 | 0 |
| 30wt% EG | 1012.4 | 3572.1 | 1.6431 | 0.44238 | -14.1 |
| 50wt% EG | 1034.4 | 3212.2 | 2.8454 | 0.361 | -33.8 |
| 70wt% EG | 1065.3 | 2852.1 | 5.0962 | 0.30109 | <-50 |
The simulation results showed that the maximum temperature of the battery pack increased with increasing ethylene glycol concentration. This occurs because higher viscosity and density affect the flow regime, potentially hindering heat transfer. Considering the freezing point requirements for winter operation, I selected the 50wt% ethylene glycol solution as the most suitable coolant medium for the EV battery pack, balancing cooling performance with low-temperature operability.
I also investigated the potential of Al₂O₃-ethylene glycol nanofluids with nanoparticle mass fractions of 0.3%, 0.5%, 0.8%, and 2%. The addition of nanoparticles showed minimal improvements in cooling performance, with changes in temperature and flow resistance being relatively small. Therefore, I proceeded with the 50wt% ethylene glycol solution without nanoparticles.
4.4 Effect of Channel Number
I investigated the influence of flow channel quantity on the cooling performance of the parallel flow liquid cooling plate. Four configurations were evaluated with channel numbers of 2, 3, 4, and 5, while maintaining constant overall dimensions and channel width.
The simulation results revealed that the number of channels significantly affects cooling performance. The 5-channel configuration delivered the best results, achieving a maximum battery pack temperature of 30.68°C with a flow resistance of 962.88Pa. The difference between the best and worst configurations was 1.75°C. The flow resistance decreased with increasing channel count because the division of flow among multiple channels reduced individual channel velocity.
The temperature distribution pattern showed that the battery pack temperature generally aligned with the coolant flow trajectory when more channels were present. The inlet side consistently maintained lower temperatures, while the highest temperatures appeared near the outlet side. The temperature difference across all configurations remained within the acceptable 5°C range.
An analysis of the coolant Reynolds numbers confirmed that all flow conditions were in the laminar regime (Re < 2300), as shown below:
| Configuration | Channel Number | Velocity (m/s) | Reynolds Number |
|---|---|---|---|
| 2-channel | 1 | 0.1680 | 592.35 |
| 2-channel | 2 | 0.1493 | 526.57 |
| 3-channel | 1 | 0.1157 | 407.87 |
| 3-channel | 2 | 0.1127 | 397.46 |
| 3-channel | 3 | 0.0881 | 310.65 |
| 4-channel | 1 | 0.0890 | 313.73 |
| 4-channel | 2 | 0.0904 | 318.73 |
| 4-channel | 3 | 0.0803 | 283.02 |
| 4-channel | 4 | 0.0586 | 206.47 |
| 5-channel | 1 | 0.0742 | 261.63 |
| 5-channel | 2 | 0.0753 | 265.69 |
| 5-channel | 3 | 0.0764 | 269.34 |
| 5-channel | 4 | 0.0559 | 197.28 |
| 5-channel | 5 | 0.0373 | 131.36 |
4.5 Effect of Channel Width
I further evaluated the influence of channel width on cooling performance using the 5-channel configuration. Four widths were tested: 10mm, 15mm, 20mm, and 25mm. The simulation results showed that the battery pack temperature decreased with increasing channel width, with the 25mm configuration performing optimally. The 10mm channel resulted in a maximum temperature of 31.48°C with a flow resistance of 2054.87Pa, while the 25mm channel achieved 30.68°C with only 962.88Pa flow resistance.
Although narrower channels increased the coolant velocity, the reduced heat transfer area negated this benefit. The laminar flow regime remained unchanged across all configurations. Therefore, I determined that the 25mm channel width combined with 5 channels provided the optimal cooling performance for the EV battery pack.
4.6 Coolant Inlet Conditions
Using the optimized liquid cooling plate configuration, I analyzed the cooling performance under transient conditions at different discharge rates. Given that the 1C discharge rate kept the battery temperature within acceptable limits naturally, I focused on the 2C and 3C discharge scenarios.
For the 2C discharge rate, I evaluated coolant flow rates of 0.06, 0.08, and 0.1 kg/s. The results are presented below:
| Flow Rate (kg/s) | Maximum Temperature (°C) | Temperature Difference (°C) | Flow Resistance (Pa) |
|---|---|---|---|
| 0.06 | 38.48 | 3.8 | 1285.80 |
| 0.08 | 37.90 | 3.3 | 2057.64 |
| 0.1 | 37.49 | 3.0 | 3003.24 |
A coolant flow rate of 0.06 kg/s was sufficient to maintain the battery pack temperature within the acceptable range at 2C discharge, minimizing energy consumption of the cooling system while ensuring thermal safety.
For the 3C discharge rate, I evaluated flow rates of 0.1, 0.2, and 0.3 kg/s. All three configurations failed to maintain the battery temperature below the maximum allowable limit:
| Flow Rate (kg/s) | Maximum Temperature (°C) | Temperature Difference (°C) | Flow Resistance (Pa) |
|---|---|---|---|
| 0.1 | 46.89 | 4.4 | 3003.24 |
| 0.2 | 45.35 | 3.2 | 11258.18 |
| 0.3 | 44.72 | 2.9 | 23718.70 |
The excessive flow rates created recirculation zones in the manifold areas, preventing uniform coolant distribution among channels and causing a dramatic increase in flow resistance without proportional thermal benefits. Reducing the coolant inlet temperature at 3C discharge proved to cause excessive temperature gradients between cells, making that approach unviable. I therefore developed a double-layer liquid cooling plate solution.
4.7 Double-Layer Liquid Cooling Plate
For effective thermal management at 3C discharge, I designed a double-layer liquid cooling plate configuration. The upper layer includes plastic fixtures, current collectors, and insulating ceramic materials, with thermal interface materials added to improve heat transfer. The coolant inlet and outlet for both plates were positioned to optimize flow distribution. Each plate operated at a flow rate of 0.1 kg/s.
The simulation results demonstrated that the double-layer configuration effectively managed the high heat generation at 3C discharge, reducing the maximum battery pack temperature to 37.92°C with a temperature difference of only 2.6°C. The temperature distribution was more uniform throughout the EV battery pack, with the highest temperatures located in the central region. The upper plate showed slightly lower cooling performance due to the additional thermal interface materials, with its maximum temperature approximately 1°C higher than the lower plate.
5. Liquid Cooling Plate Structure Optimization
To further improve the cooling system efficiency of the EV battery pack, I employed parameter optimization techniques to reduce flow resistance while maintaining thermal performance. The design variables were the seven manifold gap dimensions (X₁ through X₇) in the symmetric liquid cooling plate structure shown earlier. The design variable ranges were set from 20mm to 60mm.
5.1 Design of Experiments
I used the optimal Latin Hypercube Design method to generate 39 sample points for simulation. To reduce computational time, I applied a uniform heat flux on the upper surface of the cooling plate instead of modeling the complete battery pack. The objectives were the coolant flow resistance (Y₁) and the average outlet temperature (Y₂).
The simulation results demonstrated that the manifold gap dimensions significantly influenced flow resistance while having minimal effect on outlet temperature. This indicated that optimizing for flow resistance alone would be sufficient without compromising thermal performance.
5.2 Response Surface Model
I constructed a second-order response surface model from the DOE data. The coefficient of determination (R²) values were 0.91222 for Y₁ and 0.90716 for Y₂, both exceeding the 0.9 threshold required for adequate fitting accuracy. The mathematical relationship between design variables and flow resistance was expressed through a quadratic regression equation incorporating linear, quadratic, and interaction terms.
5.3 NSGA-II Optimization
Since the design variables primarily influenced flow resistance, I set the outlet temperature as a constraint (maintained at 26.55°C) and minimized the flow resistance as the single objective. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) was employed for its robustness in handling nonlinear optimization problems and its ability to maintain solution diversity while converging to the Pareto front.
The optimization results identified the optimal design parameters as X₁ = 57mm, X₂ = 37mm, X₃ = 55mm, X₄ = 58mm, X₅ = 72mm, X₆ = 76mm, and X₇ = 67mm, yielding a predicted flow resistance of 607.17Pa. Fluent verification simulation showed an actual flow resistance of 652.5Pa, representing a 7.8% error relative to the prediction, which is acceptable for engineering purposes.
The optimized liquid cooling plate featured larger gaps on the lateral sides compared to the central channels, creating a crescent-shaped flow distribution. The optimization substantially reduced vortex formation in the confluence regions and promoted more balanced flow distribution among channels, resulting in a 32.23% reduction in flow resistance while maintaining excellent cooling performance.
6. Battery Pack Box Structure Design and Optimization
6.1 Structural Model Design
For the EV battery pack enclosure, I designed an aluminum alloy housing composed of an upper cover, lower cover, and intermediate frame. Internal longitudinal beams divided the enclosure into three sections corresponding to the battery modules, while side reinforcement ribs enhanced protection against lateral impacts. Four mounting lugs were positioned around the perimeter for chassis attachment.
| Material Property | Value |
|---|---|
| Density (kg/m³) | 2700 |
| Young’s Modulus (MPa) | 68000 |
| Poisson’s Ratio | 0.33 |
| Yield Strength (MPa) | 370 |
I simplified the simulation model by using mass point elements to represent the battery modules, while the liquid cooling plates were explicitly modeled to assess structural integrity. The upper and lower covers were meshed using shell elements via mid-surface extraction. The final mesh contained 398,912 elements and 702,566 nodes.
6.2 Static Structural Analysis
I evaluated the mechanical performance of the battery pack box under three critical loading conditions: sudden braking (3g in driving direction), sharp turning (3g transverse acceleration), and road bump (3g vertical acceleration). The static equilibrium equation forms the basis of this analysis:
$$[K]\{x\} = \{f\}$$
where \([K]\) is the global stiffness matrix, \(\{x\}\) the displacement vector, and \(\{f\}\) the static load vector. Since aluminum is a ductile material, I applied the von Mises yield criterion for strength evaluation:
$$\sigma_{eq} = \sqrt{\frac{1}{2}[(\sigma_1-\sigma_2)^2 + (\sigma_2-\sigma_3)^2 + (\sigma_3-\sigma_1)^2]} \leq [\sigma]$$
The simulation results for all three conditions are summarized in the following table:
| Condition | Maximum Deformation (mm) | Maximum Stress (MPa) | Yield Strength (MPa) |
|---|---|---|---|
| Sudden braking (3g longitudinal) | 0.09 | 17.18 | 370 |
| Sharp turning (3g lateral) | 0.22 | 17.62 | 370 |
| Road bump (3g vertical) | 4.45 | 53.94 | 370 |
All stress values were well below the yield strength, confirming adequate structural strength of the EV battery pack box. However, the upper cover showed notable deformation of 4.45mm under the road bump condition, indicating a need for stiffness improvement.
6.3 Dynamic Analysis
Modal analysis was performed to determine the natural frequencies of the EV battery pack box. The undamped free vibration equation is expressed as:
$$[M]\{\ddot{x}\} + [K]\{x\} = 0$$
Substituting the harmonic solution \(\{x\} = \{\phi\}\sin(\omega t + \theta)\) yields the characteristic equation:
$$([K] – \omega^2[M])\{\phi\} = 0$$
The first six natural frequencies are presented below:
| Mode Number | Natural Frequency (Hz) |
|---|---|
| 1 | 16.82 |
| 2 | 32.909 |
| 3 | 35.671 |
| 4 | 41.012 |
| 5 | 46.880 |
| 6 | 47.552 |
The first-order natural frequency of 16.82Hz was relatively low, indicating potential resonance risks with road excitation. I therefore conducted random vibration analysis following the GB38031-2020 standard for EV battery pack vibration testing. The PSD profiles for the three axes are shown below:
| Frequency (Hz) | Z-axis PSD (g²/Hz) | Y-axis PSD (g²/Hz) | X-axis PSD (g²/Hz) |
|---|---|---|---|
| 10 | – | 0.005 | – |
| 15 | 0.015 | – | – |
| 20 | – | 0.005 | – |
| 30 | – | – | 0.006 |
| 65 | 0.001 | – | – |
| 100 | 0.001 | – | – |
| 200 | 0.0001 | 0.00015 | 0.0003 |
The random vibration analysis results are summarized below:
| Direction | Maximum Deformation (mm) | Maximum Stress (MPa) | Yield Strength (MPa) |
|---|---|---|---|
| X | Negligible | 0.04 | 370 |
| Y | 0.02 | 15.33 | 370 |
| Z | 13.65 | 62.46 | 370 |
The vertical direction exhibited the most significant deformation of 13.65mm concentrated in the upper cover, which required structural optimization to prevent interference with internal components.
6.4 Shape Optimization of the Upper Cover
I performed shape optimization of the battery pack upper cover to enhance its stiffness without adding significant mass or manufacturing cost. The optimization region was defined as the central panel area of the cover, while the peripheral mounting flange was designated as a non-design region to maintain assembly compatibility.
The optimization results suggested four longitudinal stiffening ribs along the cover edges and two circular recesses in the diagonal corners. The optimized cover design, manufactured through stamping operations, achieved a dramatic reduction in maximum deformation from 13.65mm to 0.01mm under Z-direction random vibration loading. This represents a 99.9% improvement in stiffness performance. The corresponding stress distributions remained well within acceptable limits, ensuring both structural integrity and component reliability of the EV battery pack under severe operating conditions.
7. Conclusions
In this research, I successfully designed and optimized an EV battery pack integrating both thermal management and structural considerations. The key contributions and findings are summarized below:
First, I established a comprehensive understanding of lithium-ion battery thermal behavior through theoretical analysis of heat generation mechanisms. The Bernadi heat generation rate model confirmed that internal resistance is the primary source of temperature rise during discharge. The classical heat transfer equations, including Fourier’s law and Newton’s law of cooling, provided the theoretical foundation for thermal management system design.
Second, through systematic calculations based on the NEDC driving cycle and vehicle performance targets, I determined that the EV battery pack required a total capacity of 40.5 kWh to achieve a 200km driving range, and the maximum discharge rate of 3C was established from the motor power demand of 113.95 kW. I selected 96 ternary lithium-ion cells configured as three modules to provide the required energy and power.
Third, the single-cell thermal simulations at 1C, 2C, and 3C discharge rates demonstrated that battery temperature rises significantly with increasing discharge rate. The maximum temperatures of 33.03°C, 43.08°C, and 53.41°C for the respective discharge rates confirmed the necessity of active cooling, particularly at high discharge rates common in real-world driving.
Fourth, the liquid cooling plate system design identified the 50wt% ethylene glycol solution as the optimal coolant, balancing thermal performance with winter operability. The parallel channel serpentine flow structure was selected for its significantly lower flow resistance compared to traditional serpentine channels. The parametric study determined that 5 channels with 25mm width provided optimal balance between heat transfer performance and flow efficiency.
Fifth, cooling system analysis revealed that for 2C discharge rates, a coolant flow of 0.06 kg/s maintained the battery temperature at an acceptable 38.48°C with minimal energy consumption. However, for 3C discharge rates, a double-layer liquid cooling plate configuration was necessary, achieving a maximum battery temperature of 37.92°C with a temperature difference of only 2.6°C. This configuration effectively managed the heat generation during high-power operation.
Sixth, the NSGA-II optimization algorithm combined with response surface modeling effectively reduced the coolant flow resistance by 32.23%, from 962.88Pa to 652.5Pa at a flow rate of 0.05 kg/s. The optimized manifold design minimized vortex formation and promoted uniform flow distribution, enhancing the overall efficiency of the thermal management system.
Finally, the structural analysis validated the mechanical robustness of the battery pack enclosure under severe static and dynamic loading conditions. Shape optimization of the upper cover reduced deformation from 13.65mm to 0.01mm under random vibration loads, ensuring proper protection of internal components and overall EV battery pack safety.
The comprehensive design methodology presented in this thesis establishes a practical framework for EV battery pack development. Future research directions should focus on validating the simulation results through experimental testing, exploring advanced materials for weight reduction, and extending the thermal management system to accommodate fast-charging scenarios. The integration of phase change materials or refrigerant-based direct cooling systems could provide additional performance enhancements, while advanced manufacturing techniques such as 3D printing may enable more complex and efficient cooling channel geometries. These continuous improvements will contribute to the advancement of electric vehicle technology, supporting the global transition toward sustainable transportation.