Optimization of Control Parameters of Vehicle Air-Conditioning System for Maximum Efficiency

Cvok, Ivan; Ratković, Igor; Deur, Joško

doi:10.4271/2020-01-1242

Cited by 8 publications

(6 citation statements)

References 8 publications

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“…The script transforms the control inputs into simulation model inputs, sets the simulation parameters (e.g., ambient and cabin conditions) and simulates the Dymola HVAC model (exported from Dymola in the executable form). The model simulation is controlled as described in [24], where the model is gradually initialized in accordance with the set control inputs, and where enough time is given to simulation to settle to steady-state value (1200 s, herein). After the simulation ends, the MATLAB script feeds the steady simulation outputs back to modeFrontier for evaluation of the setpoint tracking constraints (dashed purple box in Figure 6) and cost function (dashed orange box; Equation (2)).…”

Section: Optimisation Methodsmentioning

confidence: 99%

“…An off-line artificial neural network model-based optimisation of control trajectories is presented in [23], and carried out for two different heat pump operating modes in cold weather conditions for minimal energy consumption. Alternative off-line optimisation method is the multi-objective genetic algorithm-based optimisation, as employed in [24] to determine optimal A/C system low-level control inputs in the form of look-up tables. If the control strategy structure is already available, multi-objective optimisation may also be employed to find optimal control strategy calibration parameters [25].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper the genetic algorithm-based optimisation method from [24] is extended and applied to obtain optimal control input allocation maps of an advanced BEV HVAC system from [18], with the aim of maximising the HVAC efficiency in the heat pump operating mode. The optimal allocation maps are used within a hierarchical cabin temperature control system.…”

Section: Introductionmentioning

confidence: 99%

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Optimisation of Control Input Allocation Maps for Electric Vehicle Heat Pump-based Cabin Heating Systems

2020

Self Cite

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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.

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Section: Optimisation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimisation of Control Input Allocation Maps for Electric Vehicle Heat Pump-based Cabin Heating Systems

2020

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“…Finally, Ref. [12] developed an optimised control model for three stages to enhance the efficiency of the ACS. These stages include optimising the ACS operating point based on the control inputs and relevant actuators.…”

Section: Introductionmentioning

confidence: 99%

A NARX Model to Predict Cabin Air Temperature to Ameliorate HVAC Functionality

Kolachalama

Malik

2021

Vehicles

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Vehicular technology has integrated many features in the system, which enhances the safety and comfort of the user. Among these features, heating, ventilation, and air conditioning (HVAC) is the only feature that maintains the set cabin air temperature (CAT). The user’s command drives the set CAT, and the thermostat provides feedback to the HVAC to maintain the set CAT. The CAT is increased by extracting the heat from the engine surface produced by the fuel combustion, whereas the CAT is reduced by the known processes of the air conditioning system (ACS). Therefore, the CAT driven by the user’s command may not be optimal, and estimating the optimal CAT is still unsolved. In this work, we propose a new process where the user can input a range for CAT instead of a single value. Optimal HVAC criteria were defined, and the CAT was estimated by performing iterative analysis in the user-selected range satisfying the criteria. The HVAC criteria were defined based on two measurable parameters: air conditioning refrigerant fluid pressure (ACRFP) and engine surface temperature (EST) empirically defined as the vector CATOP. In this article, a NARX DL model was used by mapping the vehicle-level vectors (VLV) to predict the CATOP in real-time using field data obtained from a 2020 Cadillac CT5 test vehicle. Utilising the DL model, CATOP for future time steps was predicted by varying the CAT in the definite range and applying HVAC criteria. Thus, an optimal set CAT was estimated, corresponding to the optimal CATOP defined by the HVAC criteria. We performed the validation of the DL model for multiple datasets using traditional statistical techniques, namely, signal-to-noise ratio (SNR) values, first-order derivatives (FOD), and root-mean-square error (RMSE).

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“…This method could potentially predict thermal comfort in real-time and thus reduce the power consumption of the ACS. Finally, reference [13] developed an optimised control model for three stages to enhance the efficiency of the ACS. These stages include optimising the ACS operating point based on the control inputs and relevant actuators.…”

Section: Introductionmentioning

confidence: 99%

A NARX model to Predict Cabin Air Temperature to Ameliorate HVAC Functionality

Kolachalama¹,

Malik²

2021

Preprint

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The vehicular technology has integrated many features in the system, which enhances the safety and comfort of the user. Among these features, heating, ventilation, and air conditioning (HVAC) is the only feature that maintains the set cabin air temperature (CAT). The user’s command drives the set CAT, and the thermostat provides feedback to the HVAC to maintain the set CAT. The CAT is increased by extracting the heat from the engine surface produced by the fuel combustion, whereas the CAT is reduced by the known processes of the air conditioning system (ACS). Therefore, the CAT driven by the user’s command may not be optimal, and estimating the optimal CAT is still unsolved. In this work, the user was allowed to input a range for CAT instead of a single value. Optimal HVAC criteria were defined, and the CAT was estimated by performing iterative analysis in the user-selected range satisfying the criteria. The HVAC criteria were defined based on two measurable parameters: air conditioning refrigerant fluid pressure (ACRFP) and engine surface temperature (EST) empirically defined as the vector CATOP. In this article, a NARX DL model by mapping the vehicle-level vectors (VLV) to predict the CATOP in real-time using field data obtained from a 2020 Cadillac CT5 test vehicle. Utilising the DL model, CATOP for future time steps were predicted by varying the CAT in the definite range and applying HVAC criteria. Thus, an optimal set CAT was estimated, corresponding to the optimal CATOP defined by the HVAC criteria. We performed the validation of the DL model for multiple datasets using traditional statistical techniques, namely, signal-to-noise ratio (SNR) values, first-order derivatives (FOD), and root-mean-square error (RMSE).

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Optimization of Control Parameters of Vehicle Air-Conditioning System for Maximum Efficiency

Cited by 8 publications

References 8 publications

Optimisation of Control Input Allocation Maps for Electric Vehicle Heat Pump-based Cabin Heating Systems

Optimisation of Control Input Allocation Maps for Electric Vehicle Heat Pump-based Cabin Heating Systems

A NARX Model to Predict Cabin Air Temperature to Ameliorate HVAC Functionality

A NARX model to Predict Cabin Air Temperature to Ameliorate HVAC Functionality

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