Abstract
The heating, ventilation and air conditioning (HVAC) system negatively affects the electric vehicle (EV) driving range, especially under cold ambient conditions. Modern HVAC systems based on the vapour-compression cycle can be rearranged to operate in the heat pump mode to improve the overall system efficiency compared to conventional electrical/resistive heaters. Since such an HVAC system is typically equipped with multiple actuators (compressor, pumps, fans, valves), with the majority of them being controlled in open loop, an optimisation-based control input allocation is necessary to achieve the highest efficiency. This paper presents a genetic algorithm optimisation-based HVAC control input allocation method, which utilises a multi-physical HVAC system model implemented in Dymola/Modelica. The considered control inputs include the cabin inlet air temperature reference, blower and radiator fan air mass flows and secondary coolant loop pumps’ speeds. The optimal allocation is subject to specified, target cabin air temperatures and heating power. Additional constraints include actuator hardware limits and safety functions, such as maintaining the superheat temperature at its reference level. The optimisation objective is to maximise the system efficiency defined by the coefficient of performance (COP). The optimised allocation maps are fitted by proper mathematical functions to facilitate the control strategy implementation and calibration. The overall control strategy consists of superimposed cabin air temperature controller that commands heating power, control input allocation functions, and low-level controllers that ensure cabin inlet air and superheat temperature regulation. The control system performance is verified through Dymola simulations for the heat pump mode in a heat-up scenario. Control input allocation map optimisation results are presented for air-conditioning (A/C) mode, as well.
Highlights
Electric vehicles are steadily gaining a foothold in market and this trend is bound to continue with stronger consumer acceptance and favorable emissions and carbon tax legislation, e.g., those in European Union [1] and China [2]
In order to investigate the impact of different control strategy settings on the HVAC system performance, eight control configurations (Table 3) have been analysed and compared
In Configuration 8 the radiator fan is turned on to full power for longer time compared to Configuration 1. This all improves the heat exchange during the transient process and results in the fast target temperature achievement for Configuration 8, which is in turn paid for by higher energy consumption (Table 3)
Summary
Electric vehicles are steadily gaining a foothold in market and this trend is bound to continue with stronger consumer acceptance and favorable emissions and carbon tax legislation, e.g., those in European Union [1] and China [2]. The increase in mass market-share of battery electric vehicles (BEVs) is hindered by end users’ perspective of BEVs having limited driving range when compared to conventional vehicles. Passenger cabin heating presents an additional challenge, because the abundant waste heat of the internal combustion engine is no longer available, while the e-drive and battery pack waste heat is scarce due to their high efficiency. The coefficient of performance of PTC heaters is 1 at maximum [3]. This leads to high energy consumption of the heating, Energies 2020, 13, 5131; doi:10.3390/en13195131 www.mdpi.com/journal/energies
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
