Abstract
This article describes an application of Non-linear Model Predictive Control algorithms on energy efficient control of fully electric vehicle cabin temperature and air quality. Since fully electric vehicles can not utilize waste heat from a powertrain (or there is not enough waste heat) as ICE vehicles do, it is necessary to employ advanced control approaches (especially for cabin heating) due to the possible mileage lost by using energy from the batteries for cabin conditioning. The basic idea behind this is to avoid the heat losses caused by excessive air exchange and to ensure a satisfactory air quality in combination with a user defined temperature. The Non-linear Model Predictive control algorithms were successfully implemented into an Infineon AURIX Tricore microcontroller and tested within a Processor in the Loop simulation.
Highlights
F ULLY electric vehicles (FEV) require special approaches for cabin heating, as the classical solution adapted from internal combustion engine (ICE) vehicles is not satisfactory from the perspective of energy consumption
This article introduced Non-linear Model Predictive Control for Fully Electric Vehicle cabin temperature and air quality control
The particular energy saving will depend on many different conditions and there is no common methodology for HVAC consumption evaluation
Summary
F ULLY electric vehicles (FEV) require special approaches for cabin heating, as the classical solution adapted from internal combustion engine (ICE) vehicles is not satisfactory from the perspective of energy consumption. For a mid-size EV (such as a Mercedes B, Nissan Leaf) that means a loss of mileage by 7.6% due to cabin heat build-up and 10% per each hour of operation This model case didn’t take into account the power consumption of the fan and coolant pump, a possible lower ambient temperature, heat losses in the engine compartment, and other influences that might make the range loss even higher. The conclusion from this analysis is that cabin heating can have a strong negative influence on EV range At this point, we should summarize the requirements on cabin environment control: 1) Temperature - keep it at a (user) defined reference 2) Air quality - keep it at a reasonable value As far as we at best know, there is no implementation of NMPC in an automotive grade processor in series production for EV cabin heating with respect to cabin air quality
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