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The limitations of existing commercial indirect liquid cooling have drawn attention to direct liquid cooling for battery thermal management in next-generation electric vehicles. To commercialize direct liquid cooling for battery thermal management, an extensive database reflecting performance and operating parameters needs to be established. The development of prediction models could generate this reference database to design an effective cooling system with the least experimental effort. In the present work, artificial neural network (ANN) modeling is demonstrated to predict the thermal and electrical performances of batteries with direct oil cooling based on various operating conditions. The experiments are conducted on an 18650 battery module with direct oil cooling to generate the learning data for the development of neural network models. The neural network models are developed considering oil temperature, oil flow rate, and discharge rate as the input operating conditions and maximum temperature, temperature difference, heat transfer coefficient, and voltage as the output thermal and electrical performances. The proposed neural network models comprise two algorithms, the Levenberg–Marquardt (LM) training variant with the Tangential-Sigmoidal (Tan-Sig) transfer function and that with the Logarithmic-Sigmoidal (Log-Sig) transfer function. The ANN_LM-Tan algorithm with a structure of 3-10-10-4 shows accurate prediction of thermal and electrical performances under all operating conditions compared to the ANN_LM-Log algorithm with the same structure. The maximum prediction errors for the ANN_LM-Tan and ANN_LM-Log algorithms are restricted within ±0.97% and ±4.81%, respectively, considering all input and output parameters. The ANN_LM-Tan algorithm is suggested to accurately predict the thermal and electrical performances of batteries with direct oil cooling based on a maximum determination coefficient (R2) and variance coefficient (COV) of 0.99 and 1.65, respectively. [ABSTRACT FROM AUTHOR]

The rapidly growing commercialization of electric vehicles demands higher capacity lithium-ion batteries with higher heat generation which degrades the lifespan and performance of batteries. The currently widely used indirect liquid cooling imposes disadvantages of the higher thermal resistance and coolant leakage which has diverted the attention to the direct liquid cooling for the thermal management of batteries. The present study conducts the experimental investigation on discharge and heat transfer characteristics of lithium-ion battery with direct liquid cooling for the thermal management. The 18,650 lithium-ion cylindrical battery pack is immersed symmetrically in dielectric fluid. The discharge voltage and capacity, maximum temperature, temperature difference, average temperature, heat absorbed, and heat transfer coefficient are investigated under various conditions of discharge rates, inlet temperatures, and volume flow rates of coolant. The operating voltage and discharge capacity are decreasing with increase in the volume flow rate and decrease in the inlet temperature for all discharge rates. At the higher discharge rate of 4C, the lowest battery maximum temperatures of 60.2 °C and 44.6 °C and the highest heat transfer coefficients of 2884.25 W/m2-K and 2290.19 W/m2-K are reported for the highest volume flow rate of 1000 mLPM and the lowest inlet temperature of 15 °C, respectively. [ABSTRACT FROM AUTHOR]

The oil spray cooling is emerging as advanced thermal management technique for next generation electric traction motor. The performance of oil spray cooling needs to be mapped and predicted based on various influential factors to develop efficient thermal management system for electric traction motors. In the present work, the thermal and flow performances are experimentally investigated and predicted using an artificial neural network (ANN) for electric traction motor with oil spray cooling. The thermal and flow performances are extracted in terms of maximum temperature, heat transfer coefficient, Nusselt number, spray pressure and spray power consumption. The effects of nozzle types, spray heights, volume flow rates and chiller temperatures are analyzed as operating factors. The ANNs are modeled considering Levenberg-Marquardt (LM) training variant, 10 hidden neurons and Tangential-sigmoidal (Tan) and Logarithmic-sigmoidal (Log) as transfer functions. The thermal and flow performances are superior for full cone followed by hollow cone and spiral nozzles in decreasing order. The higher spray height, higher volume flow rate and lower chiller temperature show optimum values of maximum temperature and heat transfer coefficient. The spray power consumption is maximum in case of higher volume flow rate, reported less variation with change in spray height and chiller temperature. The ANN model comprising of LM-Tan algorithm is recommended as the best model with prediction error within ± 1% for all thermal and flow performances. The predicted results from the optimum ANN model are validated with experiments with maximum coefficient of determination (R2) and coefficient of variance (COV) as 0.99 and 3.20, respectively. The correlations for thermal and flow performances in terms of all influential factors are proposed using the optimum ANN model. • ANN modeling to develop oil spray cooling system for electric traction motor. • Prediction of thermal and flow performances using LM-Tan-sig and LM-Log-sig algorithms. • ANN with LM-Tan algorithm and 10 hidden neurons is suggested as optimum model. • Experimental validation of optimum model with R2 and COV of 0.99 and 3.20, respectively. • Proposed ANN based correlations to minimize expensive and time-consuming experiments. [ABSTRACT FROM AUTHOR]