Experimental Study on Heating Performances of Integrated Battery and HVAC System with Serial and Parallel Circuits for Electric Vehicle

Abstract

The objective of the present study is to conduct experiments for investigating heating performances of integrated system with serial and parallel circuits for battery and heating ventilation and air conditioning system (HVAC) of electric vehicles under various operating conditions. In addition, the artificial neural network (ANN) model is proposed to accurately predict the heating performances of integrated system with serial and parallel circuits for battery and HVAC. A test bench of integrated system with serial and parallel circuits has been developed for establishing the trade-off between battery heating and HVAC heating. The heating performances namely, battery out temperature, battery temperature rise rate, battery heating capacity, HVAC heating capacity and total heating capacity are evaluated experimentally for the integrated system with serial and parallel circuits. The behavior of various heating performances is evaluated under influence of flow rate and heater power. Battery out temperature reaches 40 °C within 10 min with rise rate of 2.17 °C/min for the integrated system with serial circuit and that within 20 min with rise rate of 1.22 °C/min for the integrated system with parallel circuit. Integrated system with serial circuit shows higher HVAC heating capacity than integrated system with parallel circuit which are 5726.33 W and 3869.15 W, respectively. ANN model with back-propagation algorithm, Levenberg-Marquardt training variant, Tan-sigmoidal transfer function and 20 hidden neurons presents the accurate prediction of heating performances of the integrated system with serial and parallel circuits for battery and HVAC.

Fuzzy Logic Controlled Simulation in Regulating Thermal Comfort and Indoor Air Quality Using a Vehicle Heating, Ventilation, and Air-Conditioning System

“…When designing intelligent HVAC system controllers, previous studies [ 18 , 19 , 20 , 21 ] have only considered cabin temperature as the primary factor to measure thermal comfort and cabin IAQ. In this paper, cabin temperature, relative humidity, and air velocity were considered as variable input factors to predict the PMV index based on the simulated outdoor environmental parameters.…”