Design and Optimization of the Heatsink of a Level 1 Electric Vehicle Charger

Abstract

The onboard circuits of EV chargers comprise heat-producing electronic devices such as MOSFETs and diodes for switching and power conversion operations. A heatsink must dissipate this generated heat to extend the devices’ life and prevent component thermal stress or failure. This study primarily investigates the optimal heatsink geometry and pin configuration, which offers the most efficient temperature versus cost performance. MATLAB/Simulink (R2024a) was used to model a Level 1 charger using eight MOSFETs and four diodes. Various heatsink geometries were modeled using the ANSYS (2024 R1) Workbench and Fluent software to optimize the sink’s thermal performance. The analyses were performed under transient conditions using natural and forced cooling scenarios. The 2 mm wide plate fin heatsink with 44 fins yielded the best result. Further enhancement of the best-performing naturally cooled model improved the switches and diodes temperatures by 14% and 4%, respectively. The performance of the heatsink was further improved by applying a cooling fan to achieve an up to 25% diode and 40% MOSFET thermal dissipation efficiency. The results of this study show that the most efficient cooling performance and cost are realized when the optimum combination of fin spacing, proximity from the cooling fan, and fin geometry is selected.

1. Introduction

Globally, the adoption of electric vehicles (EVs) has spiked over the past few years due to rising rates of greenhouse gas emissions, depletion of fossil fuel reserves, increasing investments in renewable energy deployment, and so on [1]. Therefore, developing an efficient charging infrastructure is essential for a seamless transition from internal combustion engine (ICE) vehicles that consume fossil fuel derivatives to EVs, such as on-road passenger vehicles, two-wheelers, and off-road vehicles for transportation [1]. The electrification of mobility is undoubtedly one of the most effective strategies for addressing climate change [2].

EVs are up to four times more efficient than internal combustion engines [3], and their energy can be generated locally from renewable sources [4]. Interest in electric two-wheelers (e-bikes and scooters) as a viable and cheap alternative road transportation means (when compared to conventional four-wheeled electric road vehicles) has become widespread in Asia and has been gaining traction in North America in recent years [5]. They offer benefits such as reduced traffic blockades, cheaper transport means (compared to conventional four-wheeled EVs), mitigated transport emissions, and a better quality of life in terms of improved physical fitness and wellness [5].

Managing the thermal performance of power electronic devices in EV chargers is crucial for their reliability and efficient operation. Like electricity, heat flows from a region of higher to lower (thermal) potential until a thermal equilibrium is reached [6]. Heatsinks are essential in dissipating the heat generated during charging, ensuring safe operating temperatures, and preventing electronic component failures. As shown in Figure 1, heat flow through the thermal resistances of the electronic components depends on the temperature delta between the junction (T_j) and ambient (T_a) temperatures. T_C and T_s represent the components’ case and heatsink temperatures. Power (PD) is dissipated as heat from the component’s junction to the operating environment. Therefore, the aggregate thermal resistance of a heatsink is a measure of its heat transfer efficiency, and it is dependent on the thermal resistances of the junction-case (R_jc), case–sink (R_cs), and sink-ambient (R_sa) values specified on the component datasheets.

The switching and conduction losses of MOSFETs and diodes in EV chargers can quickly heat them beyond their specified maximum allowable junction temperatures (T_j-max), causing component failure and even fires. Therefore, since most of the heat is typically generated by these switching devices, it is imperative to properly design the heatsink to maintain the junction temperature of the components below safe thresholds [7,8]. The use of heatsinks has become one of the viable alternatives for the thermal management of power electronic devices. The heatsink model shown in Figure 1 also delineates a passive heat exchanger, which transfers the heat generated by the heat source using air as the cooling medium to the ambient environment. The generated heat dissipates from the device, preventing device or component failure. Because the performance of heatsinks is influenced by air velocity [9], the type of material [10], material properties (high thermal conductivity, specific heat, density, etc.) [11], design geometry [12], and surface finishing [13], in this study, the best-performing heatsink design was selected by optimizing the design parameters under naturally cooled and forced convection conditions using the ANSYS simulation tool.

Over the years, microchips have continued to evolve, allowing more circuits to be packaged per chip [14]. This increases the power density per unit area, which leads to reduced costs and improved functionalities. Consequently, power electronic devices such as transistors today are minuscule as well as faster, lighter, and more powerful [15]. However, heat dissipation is challenging because multiple power transistors are combined on a circuit board. It was found that microchips only consume milliwatts of power. Still, due to their minuscule dimensions, the power density is high enough to generate enough heat energy to damage the components if not managed. Several studies have been conducted previously to optimize the design parameters of heatsinks to enhance their cooling efficiency and improve their thermal dissipation performance [16,17,18].

Zhang et al. [19] improved the heat removal capacity of naturally cooled heatsinks of the battery chargers of small EVs by quantitatively experimenting on multiple fin geometries with identical footprints. An overall efficiency of up to 27% was achieved after surface anodization of the fins, depending on geometry and component orientation.

Han et al. [20] optimized the design of a liquid-cooled heatsink for a 30 kW motor inverter by assessing the cooling performances of three configurations of heatsinks. The heatsink with a serpentine channel geometry was preferred, owing to its strong temperature consistency and cooling characteristics. The effects of geometry (fin thickness) and fluid flow rate on cooling performance were analyzed. The selected geometry achieved a high-power density of 9.677 kW/kg of cooling fluid.

Palumbo et al. [21] utilized a novel additive manufacturing technique involving wire-arc thermal spray to develop a topologically optimized heatsink for the thermal management of an EV fast charger. It was shown that the optimized heatsink achieved a 27% reduction in the mean thermal resistance from the cooling medium to the heatsink surface for each component and a 25% reduction in the maximum heatsink surface temperature delta.

Rasool et al. [22] proposed a co-design optimization procedure for a 175 kW SiC EV DC fast charger with a high-power density to achieve a high output power efficiency greater than 96%. The design optimization was performed in MATLAB Simulink, and a three-phase active front-end rectifier topology was used in the design and optimization process. A current total harmonic distortion of less than 3% and a power factor near unity under rated power conditions were achieved.

Ghorbani et al. [8] studied EV chargers’ passive thermal management designs using phase change materials (PCMs) to transfer 1 MW of electrical power to heavy-duty vehicles within 35 min. They qualitatively investigated various heatsink configurations and numerically studied hybrid PCMs for effective heat dissipation during charging. The research revealed that integrating a three-type hybrid PCM with a heatsink results in a 7.3% decline in final junction-ambient temperature delta compared to a single PCM integrated with a heatsink.

Mozafari et al. [23] numerically studied the effect of adding thermal conductivity enhancers by using upward triangular aluminum fins within PCM in heatsinks. The impact of using hybrid PCMs on the thermal management of electronic devices was also assessed. It was revealed that the combination of n-Eicosane and RT44 PCMs improved the average and maximum temperatures during 2 h of charging time and 3 h of discharge time for heat fluxes of 1.5 to 3.5 kW/m².

Moradikazerouni et al. [24] investigated the thermal performance and structural stability against thermal stresses of a flat plate heatsink under laminar forced air convection. The parameters studied under various airflow velocities to determine the optimal conditions for effective heat transfer included the Reynolds and Nusselt numbers, fin height, fin number, and heat transfer coefficient. It was found that pure convection achieved 1.66% less heat transfer than convection-radiation and that increasing the fin count and enhancing the fin height improved the fin maximum temperature by 25% and 20%, respectively.

Despite extensive research on EV charger thermal management, little attention has been given to how the arrangement of switches and diodes on a heatsink affects the thermal performance of electronic components. This study addresses a critical gap in the literature by using ANSYS simulations with thermal imaging to optimize the heatsink design for effective thermal management through a comparative analysis of the different layouts of the heatsink switches and diodes.

2. Materials and Methods

In this study, a 1 kW EV charger was designed using MATLAB/Simulink software (R2024a). The aggregate power dissipated by the MOSFET switches and power diodes was calculated based on measurements of the current flow through them. The component manufacturer’s specification sheets define the key parameters that influence the charger’s thermal performance and heat dissipation. The block diagram in Figure 2 shows the power flow topology of the designed bi-directional EV charger for a two-wheeler with a power output of 1042 W.

Figure 3 shows the charger’s MATLAB/Simulink model. It consists of the grid supply (120 V, 60 Hz AC), an input filter for harmonic reduction or noise attenuation to and from the mains, and a totem-pole power factor correction (PFC) AC/DC rectifier circuit, which converts the AC mains power into a sinusoidal half-wave pulsating DC voltage of 400 V and offers over 90% PF. The totem pole topology was selected because of its low current ripple, high efficiency, low component count, and ability to operate in bi-directional mode if required. The rectified phase-shifted full bridge (PSFB) DC-DC converter has an output of 43.5 VDC at a switching frequency (Fsw) of 10 kHz. The PSFB converter is designed to achieve a 20% current ripple and a DC ripple voltage of 1% and to operate at a 50% complementary duty cycle. It steps down the 400 Vdc output from the PFC converter stage to 42 Vdc for the two-wheeler. The DC–DC converter can realize electrical isolation between the power supply stage and the output stage of the charger. The transformer is used for galvanic input and output isolation to protect the battery load from fault currents [25]. It also steps down the output voltage from 400 VDC delivered by the rectifier stage to 42 VDC.

Overall, 8 MOSFET switches (S1 to S8) were used in the model. The parameters of the PFC rectifier were set using the datasheet of Infineon IPW60R040C7 [26] 600 V N-channel Si MOSFET and Wolfspeed C3M0065100K [27] 1000 V N-channel Enhancement SiC MOSFETs. These MOSFETs were selected because their low drain-source resistance (RDS_on) value makes them suitable for high-power-density and -efficiency applications such as EV charging. The SiC MOSFET is used for S1 and S2 (high-frequency legs of the PFC converter), while S3 and S4 use the Si MOSFET for the low-frequency leg. S5 to S8 in the PSFB converter also use the Si MOSFET. Furthermore, four (4) MR751 [28] 600 V rectification diodes (D1 to D4) were used because of their low thermal resistance, fast switching operation, reduced switching losses, and low reverse recovery current. The MATLAB model features a 36 V, 48 Ah lithium-ion (Li-ion) battery pack to simulate charging operation.

2.1. Summary of Charger Design Measurements

Table 1. Design calculations measurements’ summary.

Measurement	Value
Grid Voltage (85–265 V from datasheet)	120 V @ 60 Hz
Grid Current Drawn	9.544 A
Input Power	1145 W
AC/DC Rectifier Circuit Stage
AC/DC Rectifier Circuit Output DC Voltage	399.8 V
Inductor Current, IL	12.38 A (rms), 17.51 A (peak)
Inductor	2.86 mH
Capacitor	828.9 μF
Switch S1 Power Dissipated (@ RDS = 65 mΩ from datasheet)	2.96 W
Switch S2 Power Dissipated (@ RDS = 65 mΩ from datasheet)	2.96 W
Switch S3 Power Dissipated (@ RDS = 40 mΩ from datasheet)	1.84 W
Switch S4 Power Dissipated (@ RDS = 40 mΩ from datasheet)	1.84 W
PSFB DC-DC Converter
Input Voltage, Vin	399.8 V
Output Voltage, Vout	42 V
Switching Frequency, Fsw	10 kHz
Ripple Voltage, Vrip	0.42 V
Current Ripple, Irip	4.8 A
Duty Cycle, D	0.5
Output Current	23.96 A
PFSB Inductor	70 μH
PFSB Capacitor	480 μF
Switch S5 Power Dissipated (@ RDS = 40 mΩ from datasheet)	0.16 W
Switch S6 Power Dissipated (@ RDS = 40 mΩ from datasheet)	0.16 W
Switch S7 Power Dissipated (@ RDS = 40 mΩ from datasheet)	0.16 W
Switch S8 Power Dissipated (@ RDS = 40 mΩ from datasheet)	0.16 W
Isolation Transformer Secondary Voltage	49.93 V
DC-DC Converter Output Voltage	43.5 V
Diode, D1 Power Dissipated (@VF = 0.9 V from datasheet)	21.6 W
Diode, D2 Power Dissipated (@VF = 0.9 V from datasheet)	21.6 W
Diode, D3 Power Dissipated (@VF = 0.9 V from datasheet)	21.55 W
Diode, D4 Power Dissipated (@VF = 0.9 V from datasheet)	21.6 W
Charger Output Power	1042 W
Battery Nominal Voltage	36 V
Battery Rated Capacity	48 Ah
Total Power Dissipated by S1–S8 and D1–D4	98.6 W

summarizes all the charger design calculations, measurements, and key component specifications based on the manufacturers’ datasheets.

2.2. Charger Simulation Waveforms

Figure 4 illustrates the input electrical characteristics of the EV charger’s connection to the power grid. The waveforms represent the voltage and current drawn from the grid, indicating a 120 V, 9.54 A, 60 Hz AC supply. Figure 4 also shows that around 1145 W of power is drawn from the grid, which is the charger’s input power. The smooth sinusoidal nature of the waveform reflects the effectiveness of the input filter in minimizing harmonics.

Figure 5 shows the charger’s output electrical characteristics as delivered to the EV charger’s battery pack. The waveform highlights the DC output voltage, showcasing the conversion efficiency and stability of the power rectification and DC–DC conversion stages. Features like a low ripple voltage of 0.42 V and stable DC output voltage of 42 V emphasize the charger’s capacity to deliver consistent power to the battery, reducing wear and improving charging efficiency. As shown in Table 1, the charger’s output power of 1042 W implies that up to a 91% charging efficiency was achieved. This high efficiency shows that this charger design utilizes most of the energy from the grid effectively to charge the battery with minimal losses. Furthermore, the SoC waveform indicates a gradual and linear increase in the battery’s charge state over time, suggesting a stable charging process with minimal stress on the battery to promote extended battery life. This result validates the fact that the electronic components that make up the charger were carefully selected to achieve a robust charger design. This also demonstrates the functional integrity of the simulated charger model.

2.3. ANSYS Workbench Heatsink Model Design Parameters

The maximum allowable junction temperature (T_jmax) limits a device’s power dissipation capability. The manufacturer defines T_jmax, which usually depends on the reliability of the die used in the manufacturing process [7]. Equation (1) is used to estimate the junction temperature of the heatsink. Assuming a T0-220 package is used for the heatsink material and the components are mounted using a heatsink compound with an R_cs value of 0.8 °C/W:

$$T_{jmax}=P_D\left(R_{jc}+R_{cs}+R_{sa}\right)+T_a$$

(1)

where:

• T_a = ambient temperature (25 °C);
• T_jmax = MOSFET maximum junction temperature defined on the manufacturer’s datasheet (150 °C);
• R_jc = junction-to-case thermal resistance (1.1 °C/W for S1/S2; 0.55 °C/W for S3–S8; 4 °C/W for D1–D4);
• R_cs = case-to-sink thermal resistance (0.8 °C/W) defined by the case–sink interface material and mounting compound assumed;
• R_sa = sink-to-ambient thermal resistance (°C/W);
• P_D = power dissipation (W).

The overall component power dissipated was calculated from Table 1 as follows:

Total P_DMos1 = 5.92 W, Total P_DMos2 = 6.4 W, and Total P_Ddiode = 86.4 W,

R_sa was therefore computed using Equation (2) as follows [7]:

$$T_{jmax}=P_{DDiode}{\left(R_{jc}+R_{cs}\right)}_{Diode}+P_{DMos1}{\left(R_{jc}+R_{cs}\right)}_{Mos1}+P_{DMos2}{\left(R_{jc}+R_{cs}\right)}_{Mos2}+(P_{DDiode}+P_{DMos}){R_{sa}+T}_a$$

(2)

The heatsink-ambient minimum thermal resistance was calculated by rearranging Equation (2) to obtain 3.11 °C/W at a maximum junction temperature of 150 °C. This implies that a heatsink with a thermal resistance value less than this designed value is suitable to ensure a proper dissipation of the heat generated by the charger. We selected 0.82 °C/W as the preferred sink-ambient thermal resistance to achieve a more robust heatsink with an excellent thermal dissipation capacity. This offers an additional safety factor of 3.8 to protect the transistors against transient heat loads or bulk temperature during charger operation.

2.4. Transient Thermal Analysis Under Natural Convection Scenario

Using SolidWorks CAD design software (2023 version), 3D models of four different heatsink fin geometries were designed. Furthermore, thermal analysis was performed using ANSYS Workbench R2024 R1 software to determine the thermal behavior of the best-performing model containing switches S1–S8 and diodes D1 to D4 components of the EV charger. The junction–sink interface heat fluxes for the switches and diodes were 34,133.33 W/m² and 827,727.27 W/m², respectively.

Transient thermal analysis was performed on the designed heatsink to determine the temperature distribution and heat flux distribution over the models. Transient analysis differs from steady-state analysis in that it achieves the following:

• Determines when the selected heatsink model cycles between the heating and cooling phases;
• Assesses the behavior of the model in response to a time-dependent heat load or bulk temperature;
• Assesses the components’ behavior under thermal stresses arising from temperature changes.

Table 2. Heatsink system transient boundary conditions.

Boundary Condition	Value
Average Fin Surface Area	778.74 mm2 (C1); 456.38 mm2 (C2); 2938.8 mm2 (P1); 2349.7 mm2 (P2)
Constant Fin Dimensions	180 × 90 mm
Airflow Type	Natural Convection
Optimized Heat Flux Magnitudes	34,133 W/m2 and 827,727.27 W/m2 (step applied)
Ambient Temperature	25 °C (step applied)
Number of Iterations	3600
Fin Material: Aluminum	Young’s Modulus (71 GPa); Poisson’s Ratio (0.33); Density (2800 kg/m3); Thermal Conductivity (175 W/m °C); Specific Heat (875 J/kg °C)
Optimized Thermal Interface Resistance, Rsa	0.82 °C/W

summarizes the input parameter boundary conditions applied in the ANSYS Workbench physics setup. Pure aluminum, aluminum alloys, and copper and silver airfoil fins are the most commonly employed materials for heatsinks [29]. In this study, pure aluminum was the fin material of choice owing to its relatively strong thermal conductivity, heat transfer coefficient, and cost-effectiveness [30,31,32].

Four different geometries were analyzed, including heatsinks with circular fins (Model C1 with 10 mm diameter and Model C2 with 5 mm diameter) and plate fins (Model P1 with 4 mm width and Model P2 with 2 mm width). Models C1, C2, P1, and P2, shown in Figure 6, Figure 7, Figure 8 and Figure 9, illustrate the transient fin thermal distribution and the maximum, average, and minimum temperature distribution plots for the step-applied heat fluxes over 3600 iterations.

3. Results and Discussion of the Natural Cooling Scenario

Table 3. Transient fin thermal analysis result summary.

Fin Geometry	Circular Fin Model C1 (10 mm)	Circular Fin Model C2 (5 mm)	Plate Fin Model P2 (2 mm)	Plate Fin Model P1 (4 mm)
Max. Temperature (°C)	119.70	88.71	73.68	85.48
Avg. Temperature (°C)	116.48	85.13	66.14	83.16
Min. Temperature (°C)	114.48	83.33	56.71	81.36
Max. Heat Flux (W/m2)	38,926	41,284	40,344	39,782
Min. Heat Flux (W/m2)	17,579	21,964	12,337	21,796

summarizes the preliminary transient thermal performance of the fin geometries as the charging process progressed under natural cooling conditions.

Model P2, with a fin count of 44 and fin thickness of 2 mm, exhibited the best thermal dissipation behavior owing to its lowest temperature distribution.

Table 4. Model P2 device temperatures under transient thermal stress.

Device Tag	S1	S2	S3	S4	S5	S6	S7	S8	D1	D2	D3	D4
Max. Temperature (°C)	66.14	66.08	65.82	65.89	66.04	65.94	65.82	66.11	73.67	73.68	73.679	73.68

presents the measured temperatures of switches S1–S8 and diodes D1–D4 for Model P2 under a brief period of transient thermal stress.

To further enhance the thermal dissipating performance of this best-performing geometry, the physical positions of the switches and diodes were modified using four different device arrangements: P2-A, P2-B, P2-C, and P2-D. The device configurations and the associated measured temperatures of each device are depicted in Figure 10, Figure 11, Figure 12 and Figure 13 and

Table 5. Transient temperature results for Model P2-A.

Device Tag	S1	S2	S3	S4	S5	S6	S7	S8	D1	D2	D3	D4
Max. Temperature (°C)	59.56	59.61	59.61	59.55	59.83	59.91	59.90	59.83	73.25	73.42	73.461	73.25

,

Table 6. Transient temperature results for Model P2-B.

Device Tag	S1	S2	S3	S4	S5	S6	S7	S8	D1	D2	D3	D4
Max. Temperature (°C)	52.637	53.84	55.709	58.31	52.628	53.823	55.7	58.3	80.441	82.856	80.203	82.765

,

Table 7. Transient temperature results for Model P2-C.

Device Tag	S1	S2	S3	S4	S5	S6	S7	S8	D1	D2	D3	D4
Max. Temperature (°C)	58.942	58.981	59.007	59.02	59.019	59.007	58.978	58.943	73.848	73.842	73.854	73.708

and

Table 8. Transient temperature results for Model P2-D.

Device Tag	S1	S2	S3	S4	S5	S6	S7	S8	D1	D2	D3	D4
Max. Temperature (°C)	56.873	56.828	56.513	56.514	56.819	56.777	56.41	56.255	70.606	70.44	70.054	70.838

. This methodology was used to determine the switch and diode arrangement onboard that offered the optimal, efficient heat-dissipating capability under transient conditions.

In model P2-A, shown in Figure 10, the transistor devices were rearranged, with D1–D4 placed across the center of the fin base between the upper and lower rows of S1–S4 and S5–S8, respectively. S6 recorded the maximum switch temperature of 59.91 °C, a 9.1% decrease from the original model (65.94 °C), whereas D3 recorded the maximum diode temperature (73.46 °C), a 0.3% improvement in temperature.

In model P2-B, shown in Figure 11, the transistor devices were rearranged, with D1–D4 grouped on the left side of the fin base, while S1–S8 were grouped on the right side. In this case, S4 yielded the maximum switch temperature of 58.3 °C, an 11.5% decrease from the original model’s performance. However, D4 recorded the maximum diode temperature of 82.76 °C, a 12.3% decline in performance. The likely cause of this decline in performance is that the heat flux was now disproportionately concentrated at one end of the sink base.

In model P2-C, shown in Figure 12, the devices were rearranged, with D1–D4 grouped in series along the long underside of the fin base. S1–S8 were similarly grouped on the opposite edge of the sink base. S4 showed a 10.4% improvement in maximum temperature, while the D2 performance declined by 0.2% (73.84 °C) from the original 73.68 °C.

In model P2-D shown in Figure 13, S1–S8 were arranged in series along the long edge (occupying one-third) of the sink base area, while the positions of D1–D4 were evenly staggered over the remaining two-thirds of the base area. The maximum temperature of S1 improved by 14%, holding steady at 56.87 °C. The D4 temperature was also enhanced by almost 4%, from 73.68 °C, and reached a steady state at 70.84 °C.

Figure 14 compares the maximum temperature distributions for model P2’s four configurations over the entire simulation iteration.

We can surmise that the switch-diode arrangement of Model P2-D offers the most efficient and optimal thermal dissipating capability. This arrangement of the transistors leverages the increased vertical and horizontal spacing between devices to yield an average heat flux evenly distributed over the entire area of the heatsink base. The enhanced heat dissipation performance of Model P2 in the natural convection analysis is likely due to the 2 mm fin thickness. These thinner fins create optimized fluid flow channels, maximizing heat transfer efficiency through convection and radiation. Another reason may be that plate fins provide a larger heat transfer surface area than circular fins, leading to more effective heat dissipation. The superior heat dissipation performance of Model P2 in the natural convection scenario is primarily due to its optimal fin geometry, spacing, material choice, and component arrangement. This combination enables efficient heat transfer, a uniform temperature distribution, and lower maximum operating temperatures for critical components.

4. Results of Transient Thermal Analysis Under the Forced Convection Scenario

The thermal performance of Model P2-D was further examined under forced air conditions, to compare the thermal performances of Model P2-D under passive convection versus active cooling scenarios and to analyze the fan-cooled geometry’s improved thermal performance in terms of decreased fin count and the associated temperature–cost tradeoff.

The forced cooling transient analysis was performed using the ANSYS Fluent simulation tool. Model P2-D, with dimensions 180 × 90 × 100 mm, was placed in a computational domain three times the width of the sink geometry (540 mm), as shown in Figure 15.

The domain represents a cuboidal space that allows sufficient air movement to cool the geometry of interest (heatsink) using forced ambient air from the inlet (fan location) to the three-space opening outlet, to allow air egress. The CFD simulation’s fluid flow solution is evaluated in this space. In the initial setup, the heatsink is arbitrarily positioned at the center of the domain width and 100 mm away from the cooling fan at the inlet. The input parameters (fan speed and air pressure) used in the setup for the axial fan were obtained from the Noctua NF-A4x20 FLX specifications datasheet [33] sourced online. The fan speed applied was 3350 RPM. The airflow rate applied was 9.4 m³/h (or 0.00261 m³/s). With a fan diameter of 40 mm, the cross-sectional area of the fan was 0.001257 m². Therefore, the fluid (air) velocity was estimated as 2.08 m/s.

4.1. ANSYS Fluent Setup for Forced Cooling Analysis

The steps taken to set up the radiator model in the ANSYS Fluent environment are detailed as follows:

• Geometry Modification: The model was further modified to separate the fluid and solid domains and to specify the inlet and outlet surfaces in the ANSYS software. The ANSYS tool called “DesignModeler” was used to edit the model.
• Meshing independence study: The computational domain was discretized into 562,095 mesh elements (and 179,314 node elements with size set as 51.041 mm) with increasing levels of refinement to solve the discretized fluid flow equations. The refinement display style selected was the orthogonal quality, with a maximum and minimum value of 1.0 and 0.066216, respectively. The relevant discretized equations of fluid flow for the mesh setup, which describe the motion of the fluid (air), are governed by the Navier–Stokes equations (principles of conservation of mass, momentum, and energy); they include (a) the continuity equation, which is given in Equations (3) and (4) for incompressible flows as follows:

$${\displaystyle \frac{\mathsf{\partial }\rho }{\mathsf{\partial }t}}+\nabla ·\left(\rho u\right)=0$$

(3)

It is denoted in its discretized form as

$${\displaystyle \frac{{\rho }^{n+1}-{\rho }^n}{∆t}}+{\displaystyle \frac{{\rho }^{n+1}·{u_f}^{n+1}}{∆x}}=0$$

(4)

where

• $\rho =\mathrm{f}\mathrm{l}\mathrm{u}\mathrm{i}\mathrm{d} \left(\mathrm{a}\mathrm{i}\mathrm{r}\right) \mathrm{o}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{d}\mathrm{e}\mathrm{n}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{y}, \mathrm{c}\mathrm{h}\mathrm{a}\mathrm{n}\mathrm{g}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{a}\mathrm{t} \mathrm{t}\mathrm{h}\mathrm{e} \mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}{\displaystyle \frac{\mathsf{\partial }\rho }{\mathsf{\partial }t}}\mathrm{w}\mathrm{i}\mathrm{t}\mathrm{h} \mathrm{t}\mathrm{i}\mathrm{m}\mathrm{e}$;
• $u=\mathrm{v}\mathrm{e}\mathrm{l}\mathrm{o}\mathrm{c}\mathrm{i}\mathrm{t}\mathrm{y} \mathrm{v}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{o}\mathrm{r}$;
• $\nabla =\mathrm{d}\mathrm{i}\mathrm{v}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{e}\mathrm{n}\mathrm{c}\mathrm{e} \mathrm{o}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{o}\mathrm{r}$;
• $∆t,x=\mathrm{t}\mathrm{i}\mathrm{m}\mathrm{e} \mathrm{a}\mathrm{n}\mathrm{d} \mathrm{s}\mathrm{p}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{a}\mathrm{l} \mathrm{d}\mathrm{i}\mathrm{s}\mathrm{c}\mathrm{r}\mathrm{e}\mathrm{t}\mathrm{i}\mathrm{z}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{s}\mathrm{t}\mathrm{e}\mathrm{p}\mathrm{s}$;
• $n=\mathrm{t}\mathrm{i}\mathrm{m}\mathrm{e} \mathrm{s}\mathrm{t}\mathrm{e}\mathrm{p} \mathrm{i}\mathrm{n}\mathrm{d}\mathrm{e}\mathrm{x}$;
• $f=\mathrm{f}\mathrm{a}\mathrm{c}\mathrm{e}-\mathrm{c}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{e}\mathrm{d} \mathrm{v}\mathrm{a}\mathrm{l}\mathrm{u}\mathrm{e}\mathrm{s} \mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{p}\mathrm{o}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d} \mathrm{f}\mathrm{r}\mathrm{o}\mathrm{m} \mathrm{c}\mathrm{e}\mathrm{l}\mathrm{l}-\mathrm{c}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{e}\mathrm{d} \mathrm{q}\mathrm{u}\mathrm{a}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{e}\mathrm{s}$.

(a)

The momentum equation (Newton’s Second Law), which denotes the balance of forces on the fluid for incompressible flow, is given in Equation (5) as

$${\displaystyle \frac{\mathsf{\partial }\left(\rho u\right)}{\mathsf{\partial }t}}+\nabla ·\left(\rho uu\right)=-\nabla \mathrm{P}+\nabla ·\left(\mathrm{\mu }\nabla \mathrm{u}\right)+\mathrm{F}$$

(5)

where

• F = external forces (acceleration due to gravity);
• P = air pressure;
• $\mu$ = constant dynamic viscosity, representing its resistance to deformation;
• u = velocity vector.

(b)

The energy equation (conservation of energy in the fluid) for active cooling with heat transfer is given in Equation (6) as

$${\displaystyle \frac{\mathsf{\partial }\left(\rho h\right)}{\mathsf{\partial }t}}+\nabla ·\left(\rho hu\right)=\nabla ·\left(\mathrm{k}\nabla \mathrm{T}\right)+S_h$$

(6)

where

• h = enthalpy;
• T = temperature;
• k = thermal conductivity of the sink material;
• S_h = source term of the heat generated by electronic components or fans.

The Navier–Stokes equation solver setting was pressure-based in the general setup interface, while the velocity formulation was absolute. This setting also included gravity values applied to the domain. Figure 16 shows the “Create/Edit Materials” setup parameters in ANSYS Fluent, where the properties of air are imported from the material database.

Density is set to “incompressible-ideal-gas” because density is calculated based on the ideal gas law.

4.2. Cell Zone, Power Density, Reynolds Number, and Nusselt Number Specification

The cell zone is the region where the materials for the domains and the solids are specified along with other parameters like fan speed, energy source, and volumetric heat density of the solid components.

The cell zone was used to specify the domain material, solids, and other parameters like fan speed, energy source, and volumetric heat density of the solid components. The fan enclosure was renamed a rotating domain with a speed of 3350 RPM, as shown in Figure 17.

Furthermore, the power density of the energy sources was computed as follows:

The transistor material was copper, as specified in ANSYS. Each diode was specified as $4.95\times {10}^{-8}$ m³ in volume from the component datasheet.

Therefore, the total volume was $4\times 4.95\times {10}^{-8}=1.98\times {10}^{-7} {\mathrm{m}}^3$. With a diode total power of 86.35 W, the power density was computed as ${\displaystyle \frac{86.35}{1.98\times {10}^{-7}}}=4.36\times {10}^8$ W/m³.

The total MOSFET volume was specified as $8\times 1800\times {10}^{-9}=1.44\times {10}^{-5}$ m³. Therefore, the total volume was $4\times 4.95\times {10}^{-8}=1.98\times {10}^{-7} {\mathrm{m}}^3$.

With a MOSFET total power of 10.25 W, the switch power density was computed as ${\displaystyle \frac{10.25}{1.44\times {10}^{-5}}}=7.118\times {10}^8$ W/m³.

The power densities denote the transistor’s spatial energy distribution, which was applied to the sink’s base as its primary heat energy source in the analysis. The diode and MOSFET power density setup dialogs in ANSYS are shown in Figure 18 and Figure 19, respectively.

The Reynolds number (R_e) for the fluid flow regime is defined by Equation (7):

$$R_e={\displaystyle \frac{\rho ul}{\mu }}$$

(7)

where

• $\rho =\mathrm{a}\mathrm{i}\mathrm{r} \mathrm{d}\mathrm{e}\mathrm{n}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{y} (1.184 \mathrm{k}\mathrm{g}/{\mathrm{m}}^3)$;
• $u=\mathrm{i}\mathrm{n}\mathrm{l}\mathrm{e}\mathrm{t} \mathrm{a}\mathrm{i}\mathrm{r} \mathrm{v}\mathrm{e}\mathrm{l}\mathrm{o}\mathrm{c}\mathrm{i}\mathrm{t}\mathrm{y} (2.08 \mathrm{m}/\mathrm{s})$;
• $l=\mathrm{l}\mathrm{e}\mathrm{n}\mathrm{g}\mathrm{t}\mathrm{h} \mathrm{o}\mathrm{f} \mathrm{m}\mathrm{o}\mathrm{d}\mathrm{e}\mathrm{l} \mathrm{P}2-\mathrm{D} \mathrm{s}\mathrm{i}\mathrm{n}\mathrm{k}$ (90 mm from Table 2);
• $\mu =\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{t} \mathrm{d}\mathrm{y}\mathrm{n}\mathrm{a}\mathrm{m}\mathrm{i}\mathrm{c} \mathrm{v}\mathrm{i}\mathrm{s}\mathrm{c}\mathrm{o}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{y} \mathrm{o}\mathrm{f} \mathrm{a}\mathrm{i}\mathrm{r} \mathrm{a}\mathrm{t} 25 °\mathrm{C} (1.1849\times {10}^{-5} {\mathrm{k}\mathrm{g}/\mathrm{m}}^{-\mathrm{s}})$.

R_e was determined to be 11,987. Therefore, flow was turbulent. The Nusselt Number over the flat plate fins was estimated using the R_e number from Equation (8):

$$N_u=0.037{R_e}^{0.8}{P}_r^{1/3}$$

(8)

where

P_r is the Prandtl constant for air in standard conditions (approximately 0.73 assumed).

Therefore, N_u was estimated to be 61.04.

4.3. Transient Thermal Analysis Under Forced Convection Scenario

Figure 20, Figure 21 and Figure 22 depict the temperature distribution results of the thermal analysis of model P2-D placed at proximities of 100 mm, 200 mm, and 300 mm away from the cooling fan, while

Table 9. Forced air cooling temperature distribution at variant proximities.

Proximity	100 mm	200 mm	300 mm
Maximum Temperatures (°C)
S1	33.735	33.735	33.733
S2	33.909	33.91	33.909
S3	34.355	34.356	34.355
S4	34.379	34.38	34.38
S5	34.015	34.015	34.015
S6	34.021	34.019	34.018
S7	34.414	34.413	34.412
S8	34.469	34.469	34.469
D1	52.505	53.172	52.488
D2	53.177	53.176	53.175
D3	52.476	52.459	52.467
D4	53.187	53.180	53.171
Heatsink	40.463	40.462	40.456
Domain Wall	42.425	49.989	41.71
Outlet	27.294	27.266	27.241

highlights the corresponding maximum temperatures of each transistor device, heatsink, domain wall, and air outlet recorded at those proximities.

The results in Table 9 show that the 300 mm proximity between the fan and the heatsink exhibited the best thermal performance, and it was, therefore, selected for further improvement and comparison with the naturally cooled heatsink.

Furthermore, an attempt was made to optimize the 44-fin heatsink geometry (as recorded in Table 2) by decreasing the fin counts to 33, 22, 11, and 6 while still maintaining the 300 mm proximity from the fan. These variants are labeled P2-D_i, P2-D2_j, P2-D_k, and P2-D_l. Figure 23, Figure 24, Figure 25 and Figure 26 depict the temperature distribution results of the thermal analysis of the variants, respectively, and

Table 10. Temperature distribution of variants at 300 mm proximity from cooling fan.

Variants	P2-D	P2-Di	P2-Dj	P2-Dk	P2-Dl
Fin Spacing (mm)	2.14	3.65	6.48	15.8	33.6
Maximum Temperatures (°C)
S1	33.733	72.741	38.946	44.715	106.046
S2	33.909	73.075	39.213	45.094	106.676
S3	34.355	73.617	39.756	45.76	107.66
S4	34.38	73.663	39.797	45.85	107.777
S5	34.015	73.265	39.408	45.432	107.453
S6	34.018	73.173	39.394	45.406	107.344
S7	34.412	73.452	39.812	45.839	107.464
S8	34.469	73.382	39.867	45.816	107.281
D1	52.488	91.445	60.785	60.739	125.538
D2	53.175	91.462	58.738	57.962	126.379
D3	52.467	92.411	57.987	59.408	127.314
D4	53.171	92.341	59.306	59.898	126.555
Heatsink	40.456	79.814	46.174	54.644	114.38
Domain Wall	41.71	75.894	46.666	50.77	112.263
Outlet	27.241	42.751	29.451	31.45	41.158

highlights the corresponding maximum temperatures of each transistor device, heatsink, domain wall, and air outlet recorded at those proximities.

Model P2-D with 44 fins, each spaced 2.14 mm apart and placed 300 mm from the cooling fan, resulted in a maximum temperature of 52.171, outperforming the other four variants. Table 10 compares the performance results of all five configurations.

Table 10 shows the positive correlation between the heatsink’s surface area and thermal performance. This supports the fact that various geometrical parameters, such as the heatsink fin shape, size, spacing, thermal conductivity, and fluid flow conditions, sufficiently influence heatsinks’ thermal performance and efficiency [34]. Moreover, it can be observed that the maximum temperatures recorded for models P2-D_i and P2-D_j are similar. This may be attributed to “diminishing returns on heat dissipation efficiency” (whereby even though the surface area for heat transfer increases when adding more fins, the gains in heat dissipation capability can diminish) and “boundary layer effects” (from adjacent fins, which may have merged, reducing the effective area available for heat transfer). These phenomena may have led to the similar thermal performances of the 11- and 22-fin scenarios.

The bar chart in Figure 27 depicts the thermal performance comparison between the passive (natural) cooling and the active (forced) scenarios based on the results in Table 8 and Table 10.

Overall, the maximum temperature of switch S1 improved significantly, by 40.7% from 56.87 °C under the naturally cooled scenario to 33.73 °C under the fan-cooled scenario. Diode D4’s temperature declined by 24.9%, from 70.84 °C to 53.171 °C.

4.4. Comparative Analysis of Variants’ Thermal Performance Versus Fin Material Cost

The overall impact of the decreased fin count on the sink’s thermal performance is analyzed in this section. As highlighted in

Table 11. Temperature versus cost analysis of fan-cooled heatsink variants.

Variant	Fin Count	Mass (kg)	Aluminum Material Cost ($C)	Temperature °C	°C/$C
P2-D	44	2.379	6.1854	53.171	8.596
P2-Di	33	1.894	4.9244	79.814	16.208
P2-Dj	22	1.410	3.666	46.174	12.595
P2-Dk	11	0.925	2.405	54.644	22.721
P2-Dl	6	0.705	1.833	114.380	62.40

, each variant model’s mass was obtained using SolidWorks’ mass properties command.

According to Markets Insider [35], aluminum traded at $C 2600 per metric ton (or $C 2.6 per kg) as of October 2024. The cost-effectiveness of each variant was evaluated by applying the variable η_T, as shown in Equation (9), which denotes the temperature per unit cost of material.

$${\eta }_T={\displaystyle \frac{Temperature (℃)}{Mass \left(\mathrm{k}\mathrm{g}\right)\times Material Cost (\mathrm{\$}\mathrm{C}/\mathrm{k}\mathrm{g})}}$$

(9)

This metric allows for as direct comparison of sink cooling performance relative to material cost.

Again, P2-D, with 44 fin counts, emerged as the best-performing model, costing a meagre $C 1 for a sink temperature rise of 8.6 °C. This result shows that the design for the most efficient cooling performance and cost was obtained when the optimum combination of fin spacing, proximity from the cooling fan, and fin geometry was selected.

5. Conclusions

This study comprehensively investigated the effects of fin geometry, fin spacing, and the integration of a cooling fan on the thermal performance of the heatsink for a Level 1 EV charger. The transient thermal performance under natural and forced convection scenarios was analyzed using the ANSYS simulation tool. The findings can be summarized as follows:

• Fin Geometry and Thickness: Plate-fin geometries (models P1 and P2) demonstrated superior thermal dissipation compared to circular-fin designs (models C1 and C2), due to their larger surface area and linear configurations, which enhanced heat transfer through convection and radiation. Thinner fins (P2 with 2 mm thickness) improved heat transfer to the ambient by optimizing the fluid flow channels and increasing the effective heat transfer surface area.
• Switch and Diode Arrangement: The arrangement of heat-generating components on the heatsink significantly influenced thermal performance. Model P2-D, with an optimized layout of switches and diodes, reduced the maximum temperatures of switches by 14% and diodes by 4%, compared to unoptimized layouts. This highlights the importance of proper spacing to avoid localized hotspots and ensure a uniform heat distribution.
• Cooling Methods: Forced convection with a fan drastically improved thermal performance compared to natural convection. The maximum temperature of switch S1 decreased by 40.7% (from 56.87 °C under natural cooling to 33.73 °C under forced cooling), and diode D4 saw a 24.9% reduction in temperature. The optimal fan proximity (300 mm) ensured the best thermal dissipation without compromising airflow dynamics.
• Optimization of Fin Count: A 44-fin heatsink (model P2-D) exhibited the best thermal dissipation, with a balanced cost–performance tradeoff. Variants with reduced fin counts (e.g., 33 fins or 22 fins) showed diminishing returns on heat dissipation efficiency, further emphasizing the importance of balancing geometry with thermal requirements.
• Economic Feasibility: This study evaluated the cost-effectiveness of heatsink designs by analyzing the relationship between aluminum mass and cooling efficiency. Model P2-D was the most efficient design, achieving a superior thermal performance at 8.596 °C/$C of aluminum sink material with 44 fins compared to the 6-fin model with a thermal performance of 62.40 °C/$C.

In conclusion, the results demonstrate that a well-optimized heatsink design, incorporating the appropriate combination of fin geometry, spacing, and cooling fan placement, can significantly enhance the thermal management of power electronic devices in Level 1 EV chargers. This research contributes valuable insights into designing and optimizing cost-effective and thermally efficient heatsinks for EV applications.

6. Results’ Validation

Prior studies and the referenced literature were used as benchmarks to validate the key aspects of the findings in this study, such as heatsink geometry optimization, thermal management strategies, and comparison with natural and forced convection scenarios:

• Zhang et al. [19] investigated natural convection heatsinks for battery chargers using various fin geometries and found that surface anodization improved the cooling efficiency by 27%. That aligns with this study’s findings that increasing the fin surface area and optimizing the geometry (thinner plate fins) enhance heat dissipation.
• Palumbo et al. [21] found that topologically optimized heatsinks achieved a 25% reduction in surface temperature when using additive manufacturing. While the authors did not employ advanced manufacturing techniques, the observed performance improvement reached 40% under forced convection, suggesting that simpler designs, combined with fan cooling, can achieve comparable results.
• Moradikazerouni et al. [24] found that increasing the fin height and count improved the cooling performance in laminar forced convection, and the present study confirmed this, demonstrating better cooling with 44 fins compared to reduced fin counts under forced convection conditions.

7. Novelty of This Study

The novelty of this study lies in its approach to optimizing heatsink design for Level 1 EV chargers through a combination of innovative strategies, including the following:

• Focus on Switch and Diode Layout Impact: Unlike many previous studies, this research examines explicitly how the arrangement of MOSFETs and diodes on a heatsink affects thermal performance. This is a relatively unexplored factor in heatsink optimization for EV chargers. Some of the reviewed literature focuses on material-based thermal enhancements like phase change materials (PCMs) and thermal conductivity enhancers to address the heat dissipation capacity of the heatsink material. Unlike this study, it does not investigate the spatial arrangement of heat-generating electronic components. Furthermore, naturally cooled heatsinks for EV battery chargers focusing solely on fin geometry and surface modifications (e.g., anodization) have previously been studied. While the sink geometry in these studies was optimized, there was no attention to how the placement of diodes or MOSFETs might influence localized heat dissipation. This manuscript addresses that significant gap by demonstrating that the spatial layout of switches and diodes can significantly influence heat distribution. The results show that Model P2-D with an optimized layout reduces temperatures by 14% for switches and 4% for diodes, a clear improvement over unoptimized layouts.
• Integrating Natural and Forced Cooling Scenarios: This study evaluated natural convection and forced cooling scenarios to identify the best-performing heatsink design. It was unique in its thoroughness in combining these cooling methods with detailed simulation results.
• Comparative Analysis of Multiple Configurations: We performed an exhaustive comparative analysis by testing various heatsink geometries (circular and plate fins) and adjusting parameters like fin count, spacing, and proximity to a cooling fan. The findings reveal a specific optimal configuration (P2-D) that outperforms others.
• Comprehensive Thermal Simulation: Advanced simulation tools like ANSYS Workbench for transient thermal analysis, coupled with MATLAB/Simulink modeling of the EV charger, ensure high accuracy and applicability of the results.
• Thermal Efficiency vs. Cost Tradeoff: This study uniquely evaluated the cost-effectiveness of heatsink materials (aluminum) and geometries, ensuring their practical feasibility for manufacturers. This adds an economic dimension to the engineering problem.