Prediction of experimental thermal performance of new designed cold plate for electric vehicles' Li-ion pouch-type battery with artificial neural network

Since liquid-based thermal management systems are usually preferred methods for battery electric vehicles and cold plates are generally preferred to circulate the coolant, studies on their design are becoming increasingly essential. Besides, it seems useful to work artificial intelligence approaches to evaluate different battery thermal management systems, as it is known that the use of artificial intelligence is increasing in many applications today. The aim of this paper is to build up an artificial neural network model due to predict average battery temperature and maximum temperature difference on the battery surface which are also the artificial neural network outputs. The model inputs are depth of discharge, coolant flow rate (0.1, 0.6 and 1.1 l/min), discharge rate (1C- 5C), coolant inlet temperature (15, 25 and 35 degrees C). It is developed for a serpentine tubed cold plate, and mini channeled one which has novel design. To shorten the training time, after the optimization of the data set, a total of 270 data sets were utilized for training, validation, and test phases. In addition, the developed model predicts successfully average battery temperature and maximum temperature difference on the battery surface in the 10% error band range. Finally, the maximum margin of deviation and R values are 7.3% and 0.997%, respectively.

Automated summary

This paper investigates the application and design of liquid-based thermal management systems in battery electric vehicles. It also explores the feasibility of using artificial intelligence to evaluate different battery thermal management systems. By utilizing an artificial neural network model, the average battery temperature and maximum temperature difference on the battery surface can be accurately predicted. The model parameters were optimized, and the predicted results were within a 10% error range compared to the actual values.