Energy‐efficient cabin climate control of electric vehicles using linear time‐varying model predictive control

Chen, Youyi; Kwak, Kyoung Hyun; Kim, Jaewoong; Kim, Youngki; Jung, Dohoy

doi:10.1002/oca.2816

Cited by 10 publications

(4 citation statements)

References 25 publications

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“…Chen et al [19] discuss the significance of real-time optimal control in improving the energy efficiency of cabin climate control systems in EVs. They introduced a novel lineartime-varying (LTV) MPC approach, which simplifies the optimization process and conducts a comprehensive parametric study to identify robust weighting factors.…”

Section: Introductionmentioning

confidence: 99%

Controller Design for Air Conditioner of a Vehicle with Three Control Inputs Using Model Predictive Control

Parent,

Defoe,

Rahimi

2024

Modelling

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Fuel consumption optimization is a critical field of research within the automotive industry to meet consumer expectations and regulatory requirements. A reduction in fuel consumption can be achieved by reducing the energy consumed by the vehicle. Several subsystems contribute to the overall energy consumption of the vehicle, including the air conditioning (A/C) system. The loads within the A/C system are mainly contributed by the compressor, condenser fan, and underhood aerodynamic drag, which are the components targeted for overall vehicle energy use reduction in this paper. This paper explores a new avenue for A/C system control by considering the power consumption due to vehicle drag (regulated by the condenser fan and active grille shutters (AGS)) to reduce the energy consumption of the A/C system and improve the overall vehicle fuel economy. The control approach used in this paper is model predictive control (MPC). The controller is designed in Simulink, where the compressor clutch signal, condenser fan speed, and AGS open-fraction are inputs. The controller is connected to a high-fidelity vehicle model in Gamma Technologies (GT)-Suite (which is treated as the real physical vehicle) to form a software-in-the-loop simulation environment, where the controller sends actuator inputs to GT-Suite and the vehicle response is sent back to the controller in Simulink. Quadratic programming is used to solve the MPC optimization problem and determine the optimal input trajectory at each time step. The results indicate that using MPC to control the compressor clutch, condenser fan, and AGS can provide a 37.6% reduction in the overall A/C system energy consumption and a 32.7% reduction in the error for the air temperature reference tracking compared to the conventional baseline proportional integral derivative control present in the GT-Suite model.

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Section: Introductionmentioning

confidence: 99%

Controller Design for Air Conditioner of a Vehicle with Three Control Inputs Using Model Predictive Control

Parent,

Defoe,

Rahimi

2024

Modelling

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“…Other important aspects, such as indoor air quality, passenger comfort, and humidity, should be considered. Nevertheless, using MPC has made it possible to demonstrate initial energy-saving potential [16][17][18]. This paper presents a model predictive control strategy for the energy-efficient air conditioning of BEVs.…”

Section: Introductionmentioning

confidence: 99%

A Predictive Cabin Conditioning Strategy for Battery Electric Vehicles

Schutzeich,

Pischinger,

Hemkemeyer

et al. 2024

WEVJ

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This paper is based on the work presented at EVS36 in Sacramento. The core of the work deals with the cabin climate control of battery electric vehicles (BEV) using model predictive control (MPC) approaches. These aim to reduce the energy demand for cabin air conditioning while maintaining comfort and air quality. The first step briefly overviews model predictive control approaches and the respective fundamentals. Afterward, the modeling for the system dynamics is explained. The challenge for the system model considering humid air is discussed, and the first implementation method is presented. With the added equations for the air quality and humidity, a logic to prevent window fogging was developed to improve safety. Ultimately, model-in-the-loop (MiL) investigations identified an energy-saving potential of up to 15.4% for cold and 39.7% for hot conditions compared to a rule-based strategy. In addition, the investigations carried out showed that it was also possible to improve indoor comfort by specifically influencing the air quality and humidity. Together with the safety criteria introduced to prevent window fogging, it was possible to present a strategy that can significantly improve thermal management for the cabin in modern BEVs.

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“…Therefore, since the 20th century, the development of energy saving approaches to increase energy efficiency has attracted attention of many automakers. Some researchers have developed an optimal velocity planning scheme using traffic signal phase and timing (SPAT) [3,4], efficient regenerative braking systems to recharge batteries [5], and energy-efficient cabin climate control [6,7].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Efficiency of Connected and Automated Buses Platooning in Mixed Traffic Environment

et al. 2022

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Due to the battery capacity limitation of battery electric vehicles (BEVs), the importance of minimizing energy consumption has been increasing in recent years. In the mean time, for improving vehicle energy efficiency, platooning has attracted attention of several automakers. Using the connected and automated vehicles (CAVs) technology, platooning can achieve a longer driving range while preserving a closer distance from the preceding vehicle, resulting in the minimization of the aerodynamic force. However, undesired behaviors of human-driven vehicles (HVs) in the platooning group can prohibit the maximization of the energy efficiency. In this paper, we developed a speed planner based on the model predictive control (MPC) to minimize the total platooning energy consumption, and HVs were programmed to maintain a long enough distance from the preceding vehicle to avoid collision. The simulations were performed to determine how HV influences the efficiencies of the platooning group, which is composed of CAVs and HVs together, in several scenarios including the different positions and numbers of the HVs. Test results show that the CAVs planned by our approach reduces energy consumption by about 4% or more than 4% compared to that of the HVs.

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Energy‐efficient cabin climate control of electric vehicles using linear time‐varying model predictive control

Cited by 10 publications

References 25 publications

Controller Design for Air Conditioner of a Vehicle with Three Control Inputs Using Model Predictive Control

Controller Design for Air Conditioner of a Vehicle with Three Control Inputs Using Model Predictive Control

A Predictive Cabin Conditioning Strategy for Battery Electric Vehicles

Evaluating the Efficiency of Connected and Automated Buses Platooning in Mixed Traffic Environment

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