In the rapidly evolving landscape of electric cars, the demand for high-performance power modules is paramount. As a key component in China EV systems, insulated gate bipolar transistor (IGBT) power modules face significant thermal challenges due to increasing power densities. Efficient thermal management is critical to ensure reliability and longevity in electric car applications. This study focuses on numerical simulation of IGBT power module thermal management, addressing factors like substrate materials, coolant flow, and pin-fin structures to optimize performance for China EV markets.
The IGBT power module, integral to electric car powertrains, experiences high heat generation during operation. In China EV designs, managing this heat is essential to prevent failures. We employ a fluid-thermal-solid coupling approach to analyze the thermal behavior. The model simplifies the complex geometry while capturing essential heat transfer pathways. Key materials include silicon chips, ceramic substrates, and copper layers, with a pin-fin散热器 for direct liquid cooling. The coolant is a 50% ethylene glycol solution, typical in electric car cooling systems.
Numerical simulations were conducted using ANSYS Fluent, with governing equations for fluid flow and heat transfer. The Reynolds-averaged Navier-Stokes (RANS) equations model turbulent flow, while energy equations handle heat conduction in solids. For the coolant, the mass, momentum, and energy equations are:
$$ \frac{\partial}{\partial x_i} (\rho u_i) = 0 $$
$$ \frac{\partial}{\partial x_j} (\rho u_i u_j) = -\frac{\partial p}{\partial x_i} + \frac{\partial}{\partial x_j} \left( -\rho u’_i u’_j \right) + \frac{\partial}{\partial x_j} \left[ \mu \left( \frac{\partial u_i}{\partial x_j} + \frac{\partial u_j}{\partial x_i} – \frac{2}{3} \delta_{ij} \frac{\partial u_l}{\partial x_l} \right) \right] $$
$$ \frac{\partial}{\partial x_i} (\rho u_i C_p t_f) = \frac{\partial}{\partial x_i} \left( \lambda_{\text{eff}} \frac{\partial t_f}{\partial x_i} \right) $$
Here, $u_i$ represents velocity components, $p$ is pressure, $t_f$ is fluid temperature, $\rho$ is density, $\mu$ is dynamic viscosity, $C_p$ is specific heat, and $\lambda_{\text{eff}}$ is effective thermal conductivity. The standard $k$-$\epsilon$ turbulence model closes the equations with:
$$ \frac{\partial}{\partial x_i} (\rho k u_i) = \frac{\partial}{\partial x_i} \left[ \left( \mu + \frac{\mu_t}{\sigma_k} \right) \frac{\partial k}{\partial x_j} \right] + G_k – \rho \epsilon $$
$$ \frac{\partial (\rho \epsilon u_i)}{\partial x_i} = \frac{\partial}{\partial x_i} \left[ \left( \mu + \frac{\mu_t}{\sigma_\epsilon} \right) \frac{\partial \epsilon}{\partial x_j} \right] + C_{1\epsilon} G_k \frac{\epsilon}{k} – C_{2\epsilon} \rho \frac{\epsilon^2}{k} $$
For solid regions, heat conduction follows Fourier’s law with a heat source for chip power loss:
$$ \frac{\partial}{\partial x_i} \left( \lambda_s \frac{\partial t_s}{\partial x_i} \right) + S_h = 0 $$
where $t_s$ is solid temperature, $\lambda_s$ is thermal conductivity, and $S_h$ is heat source density. Boundary conditions include a flow rate of 10 L/min at 65°C for the coolant inlet, pressure outlet, and adiabatic walls. Chip power losses are set to 500 W for IGBT and 300 W for diode chips, based on typical electric car operating conditions. Mesh independence was verified, ensuring results are grid-insensitive.
Validation against experimental data showed a relative error of 3.7%, confirming model accuracy for China EV applications. The chip thermal resistance $R_{jc}$ is calculated as:
$$ R_{jc} = \frac{t_j – t_c}{P_L} $$
where $t_j$ is junction temperature, $t_c$ is coolant temperature, and $P_L$ is total power loss. This metric is crucial for evaluating thermal performance in electric cars.
We analyzed the impact of substrate ceramic materials on thermal resistance. Common ceramics like Al2O3, Si3N4, AlN, and BeO were compared for their thermal conductivity effects. Results are summarized in Table 1, showing that higher conductivity materials reduce thermal resistance, benefiting electric car power modules by lowering junction temperatures.
| Material | Thermal Conductivity (W/m·°C) | IGBT $R_{jc}$ (°C/W) | Diode $R_{jc}$ (°C/W) |
|---|---|---|---|
| Al2O3 | 27 | 0.109 | 0.157 |
| Si3N4 | 90 | 0.087 | 0.143 |
| AlN | 180 | 0.081 | 0.135 |
| BeO | 240 | 0.079 | 0.132 |
Coolant flow rate effects were studied for volumes from 2 to 14 L/min. Increasing flow reduces thermal resistance initially, but diminishing returns occur above 8 L/min. Pressure drop $\Delta p$ rises significantly at higher flows, impacting pumping power in electric cars. The relationship is captured by:
$$ R_{jc} \propto q_V^{-0.5} \quad \text{for low flows}, \quad \Delta p \propto q_V^2 $$
where $q_V$ is volume flow rate. This highlights the trade-off between cooling and energy consumption in China EV systems.
Pin-fin geometry optimization is vital for electric car thermal management. We varied circular pin-fin diameter $D_1$ and lateral spacing $S_1$, and introduced elliptical pin-fins with major axis $D_2$. Results show that increasing $D_1$ reduces thermal resistance but increases pressure drop. Elliptical pin-fins offer better performance by enhancing heat transfer area and flow characteristics. For instance, with $D_1 = 1.2$ mm and $D_2 = 2.4$ mm, thermal resistance decreases by 13% compared to baseline. Table 2 summarizes key findings.
| Parameter | Range | IGBT $R_{jc}$ Change | $\Delta p$ Change |
|---|---|---|---|
| $D_1$ (mm) | 1.2–3.2 | -13% | +150% |
| $S_1$ (mm) | 3.2–5.4 | +20% | -40% |
| $D_2$ (mm) | 2.0–4.5 | -10% | +50% |
A three-stage design optimization method was developed for China EV power modules: contribution quantification, surrogate modeling, and multi-objective optimization. Contribution analysis revealed that convective thermal resistance and ceramic layer resistance dominate total thermal resistance. For a 750 V/820 A H-Boost IGBT module, surrogate models based on regression analysis predict performance. The optimization function minimizes thermal resistance, pressure drop, and mass $m$:
$$ f_{\text{ce}} = \min \left( \frac{1}{2} \cdot \frac{R_{jc}}{R_{jc0}} + \frac{1}{2} \cdot \frac{m_{\text{ce}}}{m_{\text{ce0}}} \right) $$
$$ f_{\text{hs}} = \min \left( \frac{1}{3} \cdot \frac{R_{jc}}{R_{jc0}} + \frac{1}{3} \cdot \frac{\Delta p}{\Delta p_0} + \frac{1}{3} \cdot \frac{m_{\text{hs}}}{m_{\text{hs0}}} \right) $$
where subscript 0 denotes original values. Optimized parameters include Si3N4 ceramic and elliptical pin-fins with $D_1 = 1.2$ mm, $D_2 = 2.4$ mm, and $S_1 = 3.2$ mm. This reduces IGBT thermal resistance by 21.1%, pressure drop by 39.3%, and mass by 6.1%, enhancing electric car efficiency.
In conclusion, numerical simulation effectively optimizes IGBT power module thermal management for electric cars. Key factors like ceramic materials and pin-fin structures significantly impact performance, with the proposed method offering a balanced approach for China EV applications. Future work could integrate cost and reliability aspects to further advance electric car technologies.