Predictive control of car refrigeration cycle with an electric compressor

Lim, Tae Hoon; Shin, Younggy; Kim, Sanghun; Kwon, Chunkyu

doi:10.1016/j.applthermaleng.2017.08.125

Cited by 15 publications

(6 citation statements)

References 12 publications

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“…These datasets include various refrigerant inlet and outlet conditions under the ideal vapor-compression cycle assumption for the compressor, the entire speed range under different vehicle speeds for the fan, and the entire speed range with different coolant temperatures for the pump. The compressor power consumption is modeled as a static component [37]. The compressor power consumption is expressed as a function of the refrigerant temperature difference between the compressor and evaporator (T r,c − T r,e , indicating the pressure ratio) and the refrigerant mass flow rate ṁcomp .…”

Section: Power Consumption Modelsmentioning

confidence: 99%

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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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Section: Power Consumption Modelsmentioning

confidence: 99%

“…For simplicity, γ = 1 is used in this study. The cost-to-go value from the data-driven function (37) and its normalized error against data gathered from the simulations are presented in Fig. 9(a) and (b), respectively.…”

Section: Approximated Value Functionmentioning

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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“…For the air conditioning system, Khayyam presented an adaptive intelligent controller that can achieve an improvement of about 1% of the energy consumption compared with fuzzy air conditioning with the look-ahead system [9]. Lim et al proposed a model predictive control with an optimization algorithm of quadratic programming, reducing the energy consumption by 2.65% compared with the conventional feedback control [10]. Huang et al presented an energy-saving set-point optimizer with a sliding mode controller.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In active suspension, the energy is consumed to generate the active actuator force F a to protect the body from vibration due to road roughness. Thus, the energy consumption of active suspension can be calculated by Equation (10).…”

Section: Vertical Quarter-car Modelmentioning

confidence: 99%

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Vehicle Optimal Control Design to Meet the 1.5 °C Target: A Control Design Framework for Vehicle Subsystems

Chen

Ding

et al. 2019

Energies

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Current studies have achieved energy savings of vehicle subsystems through various control strategies, but these control strategies lack a benchmark to measure whether these energy savings are sufficient. This work proposes a control design framework that uses the 1.5 °C target in the Paris Agreement as a benchmark to measure the adequacy of energy savings of vehicle subsystems. This control design framework involves two points. One is the conversion of the 1.5 °C target into a constraint on the energy consumption of a vehicle subsystem. The other is the optimal control design of the vehicle subsystem under this constraint. To describe the specific application of this control design framework, we conduct a case study concerning the control design of active suspension in a battery electric light-duty vehicle. By comparison with a widely used linear quadratic regulator (LQR) method, we find that this control design framework can both ensure the performance comparable to the LQR method and help to meet the 1.5 °C target in the Paris Climate Agreement. In addition, a sensitivity analysis shows that the control effect is hardly changed by battery electric vehicle market share and electricity CO2 intensity. This work might provide insight on ways that the automotive industry could contribute to the Paris Agreement.

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A Self-learning intelligent passenger vehicle comfort cooling system control strategy

Xie

Liu

et al. 2020

Applied Thermal Engineering

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Predictive control of car refrigeration cycle with an electric compressor

Cited by 15 publications

References 12 publications

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

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

Vehicle Optimal Control Design to Meet the 1.5 °C Target: A Control Design Framework for Vehicle Subsystems

A Self-learning intelligent passenger vehicle comfort cooling system control strategy

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