Non-Linear Model Predictive Control of Cabin Temperature and Air Quality in Fully Electric Vehicles

Glos, Jan; Otava, Lukas; Václavek, Pavel

doi:10.1109/tvt.2021.3054170

Cited by 32 publications

(8 citation statements)

References 36 publications

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“…Note that subscript 𝑖 is from 0 to the time horizon ℎ as the dimensions of these vectors change with the size of the time horizon. To make the formulation easier, the system equations are written in { 𝑥 𝑘+1 = 𝐴 𝑑 𝑥 𝑘 + 𝐵 𝑑 ∆𝑢 𝑘 + 𝐵 𝑑 𝑢 𝑘−1 𝑢 𝑘 = 𝑢 𝑘−1 + ∆𝑢 𝑘 𝑦 𝑘+1 = 𝐶 𝑑 𝑥 𝑘+1 + 𝐷 𝑑 ∆𝑢 𝑘 + 𝐷 𝑑 𝑢 𝑘−1 (14) where ∆𝑢 𝑘 ≜ 𝑢 𝑘 − 𝑢 𝑘−1 . Rewriting the model in this form incorporates the rate of change of the controller inputs into the state-space model.…”

Section: St : Dynamic Model and Constraintsmentioning

confidence: 99%

“…The IMPC strategy saves more energy than other control strategies researched in their paper. Glos et al [14] presented an application of NMPC algorithms for energy-efficient control of cabin temperature and air quality in fully electric vehicles (FEVs). The authors developed simplified dynamic models of the FEV cabin and HVAC system and formulated an optimal control problem that minimized power consumption while satisfying the user's comfort and safety requirements.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, some studies have not considered all possible control inputs in the overall design, such as missing AGS [11]. Furthermore, most studies, especially in recent years, focus on EVs as energy consumption and management has a much greater role in such vehicles [12][13][14][15]. This has left fossil-fueled energy management and A/C energy efficiency on the back burner in recent years.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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: St : Dynamic Model and Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Parent,

Defoe,

Rahimi

2024

Modelling

View full text Add to dashboard Cite

show abstract

“…However, the algorithm does not account for the optimal control of the refrigerant cycle components. Glos et al [14] proposed an NMPC for an EV HVAC system. In their MPC algorithm, the heat transfer rate from the refrigerant cycle is considered as a control input, which significantly reduces the computational load as it avoids the need to compute the two-phase flow heat transfer.…”

Section: B Literature Reviewmentioning

confidence: 99%

Integrated Thermal Management of Electric Vehicles Based on Model Predictive Control With Approximated Value Function

Chen,

Kwak,

Kim

et al. 2024

IEEE Access

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The thermal management system (TMS) in electric vehicles (EVs), including climate control and battery thermal regulation, consumes more energy than any other auxiliary components. Therefore, optimizing TMS control is crucial for enhancing EV driving range. However, the complexity of the TMS, described by a differential algebraic system, poses challenges for real-time optimal control. This study proposes model predictive control (MPC)-based solutions for integrated TMS operation in EVs. An optimal thermal management problem is formulated using economic nonlinear MPC (NMPC), and its performance is evaluated. To reduce computational load, an approximated value function (VF) is introduced based on the economic NMPC results. A linear-time-varying MPC (LTV-MPC) with the approximated VF is proposed for real-time implementation using quadratic programming, and through simulations it is compared with the baseline NMPC controller and a rule-based (RB) controller. Results reveal that the LTV-MPC with an approximated VF performs similarly to NMPC while offering slightly compromised cooling performance. It also significantly reduces the computational time by a factor of 10 4 compared with NMPC owing to the short prediction horizon enabled by the approximated VF. Furthermore, when compared with the RB controller, the proposed LTV-MPC achieves energy savings in the range of 22.3% to 29.8%.

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“…However, the heating is not considered as well as the optimal control of the refrigerant cycle components. Glos et al 16 proposed an NMPC for an EV climate control system. In the considered system, the duct air for the cabin is cooled by a coolant circuit that is between the duct and refrigerant cycle.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Kwak

Kim

et al. 2021

Optim Control Appl Methods

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A cabin climate control system, often referred to as a heating, ventilation, and air conditioning (HVAC) system, is one of the largest auxiliary loads of an electric vehicle (EV), and the real-time optimal control of HVAC brings a significant energy-saving potential. In this article, a linear-time-varying (LTV) model predictive control (MPC)-based approach is presented for energy-efficient cabin climate control of EVs. A modification is made to the cost function in the considered MPC problem to simplify the Hessian matrix in utilizing quadratic programming for real-time computation. A rigorous parametric study is conducted to determine optimal weighting factors that work robustly under various operating conditions. Then, the performance of the proposed LTV-MPC controller is compared against a rule-based (RB) controller and a nonlinear economic MPC (NEMPC) benchmark. Compared with the RB controller benchmark, the LTV-MPC reaches the target cabin temperature at least 69 s faster with 3.2% to 15% less HVAC system energy consumption, and the averaged cabin temperature difference is 0.7 • C at most. Compared with the NEMPC, the LTV-MPC controller can achieve comparable performance in temperature regulation and energy consumption with fast computation time: the maximum differences in temperature and energy consumption are 0.4 • C and 2.6%, respectively, and the computational time is reduced 72.4% on average with the LTV-MPC.

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Non-Linear Model Predictive Control of Cabin Temperature and Air Quality in Fully Electric Vehicles

Cited by 32 publications

References 36 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

Integrated Thermal Management of Electric Vehicles Based on Model Predictive Control With Approximated Value Function

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

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