The aim of this study is to numerically investigate the cooling performance of the electric vehicle motor depending on the attachment of the heat sink and materials to the cooling channel. The research focused on the numerical comparison of forced convective heat transfer coefficients with case 1 (Heat Sink-None, Cooling Channel-Al), case 2 (Heat Sink-None, Cooling Channel-Metal Hybrid Material), case 3 (Heat Sink-4EA, Cooling Channel-Al), and case 4 (Heat Sink-6EA, Cooling Channel-Al). To compare the cooling performance for novel design of the smart cooling system, selected local positions for various temperature distributions were marked on the coil surface. Normalized local Nusselt number of the cooling area at the normalized width position indicated that cooling performance of case 1 was on an average 8.05, 0.57, and 5.85% lower than that of cases 2, 3, and 4, respectively.

This research is to investigate the cooling performance of the motor in the electric vehicle depending on the cooling channel fin. The research focused on numerical study of the temperature of coil and cooling channel and the heat transfer coefficients to find a optimum design shape with high cooling performance at three different cases. To compare the convective cooling performance of the three cooling channels, local position (R) are displayed on the surface of the coils with a large temperature deviation. This research was performed on forced convection and was numerically analyzed by FLUENT V20.2. Owing to forced convection by the same mass flow, the average cooling channel velocity in Case 3 was 17.4% faster than Case 1 and 8.6% faster than Case 2. Out of the three cases, the highest heat transfer coefficient was found in the cooling channel and coil of Case 3, which had two cooling fins. The coil maximum temperature of Case 3 with 2 cooling fins was 4.7% lower than Case 1 without cooling fins and 1.7% lower than Case 2 with 1 cooling fin. Ultimately, Case 3 with two cooling fins provided the best cooling performance and improved driving motor performance for motor durability.

This research investigated the cooling performance of the motor in electric vehicle depending on the shape of the cooling channel. The research, conducted numerically by FLUENT V20.1, focused on the numerical study of heat transfer coefficients to find an optimum design shape with high cooling performance. To compare the cooling performance, the temperatures in the coil and cooling channel were analyzed. As a result of forced convection, the average cooling channel velocity of Case 2 was 38% faster than Model N and 34% faster than Case 1. The maximum temperature of the cooling channel of Case 2 was 8.7% lower than Model N and 5.6% lower than Case 1. The minimum temperature of the coil of Case 2 was 2.7% lower than Model N and 4.3% lower than Case 1. The maximum temperature of the coil of Case 2 was 4.6% lower than Model N and 2.9% lower than Case 1. Ultimately, cooling channel of Case 2 showed the best cooling performance and improved driving performance for motor durability.

This study is to investigate the cooling performance of the battery in the electric vehicle depending on the attachment of the cooling plates and materials to the battery cells. Research focused on the numerical comparison of forced convective heat transfer coefficients with case 1(cell-Al, cooling plate-None), case 2(cell-Al, cooling plate-Al), case 3(cell-Al, cooling plate-C), and case 4(cell-C, cooling plate-Al). Normalized local Nusselt number of the cooling area at the normalized width position indicated that the heat transfer coefficient of the case 1 was averaging at 7, 14.5, 11.9% lower than that of case 2, case 3, and case 4. Based on case 3, the cooling performance with six different types of mass flow rates (0.05, 0.075, 0.0875, 0.1, 0.125, 0.15 kg/s) were compared. Normalized local Nusselt number at the normalized width position indicated that the heat transfer coefficient of 0.0875 kg/s was averaging at 35.8, 11.9% higher than that of 0.05, 0.075 kg/s and 12.3, 36.4, 60% lower than that of 0.1, 0.125, 0.15 kg/s. Ultimately, the best optimization design for air-cooling performance was case 3 with mass flow rate of 0.125 kg/s.