Grey relational based Taguchi analysis on heat transfer performances of direct oil spray cooling system for electric vehicle driving motor

In the present study, the grey relational based Taguchi analysis is conducted to evaluate the effect of influential factors on heat transfer performances of direct oil spray cooling system for electric vehicle driving motor. The influence of spray flow characteristics considering three nozzle types of spiral, full cone and hollow-cone, coolant temperature (CT), heating capacity (HC), flow rate (FR) and distance between nozzle spray and heating source (D) are evaluated on heat transfer performances of maximum temperature, temperature uniformity, maximum injection pressure, heat transfer coefficient, friction factor and Nusselt number. The Taguchi analysis evaluates the effect of influential factors in terms of mean, range, influence order, F-value and percentage contribution while the grey relational analysis evaluates the combined parameter of two performances in terms of grey relational grade (GRG). The decreasing influence order of factors is evaluated as FR > HC > CT > D with corresponding percentage contributions of 39.16%, 33.99%, 14.16% and 12.68% for GRG of heat transfer coefficient and spray power consumption and FR > HC > D > CT with corresponding percentage contributions of 86.02%, 7.98%, 3.54% and 2.46% for GRG of Nusselt number and friction factor. An efficient direct oil spray cooling system could be fabricated for electric vehicle driving motor based on qualitative and quantitative effects of various influential factors on its heat transfer performances.& COPY; 2022 Elsevier Ltd. All rights reserved.

Automated summary

The grey relational based Taguchi analysis was used to evaluate the effect of influential factors on the heat transfer performances of a direct oil spray cooling system for electric vehicle driving motor. The study considered different nozzle types, coolant temperature, heating capacity, flow rate, and distance between nozzle spray and heating source. The analysis provided insights into the influential factors and their contributions to the system's heat transfer performances.