A Dynamic Programming Approach for Thermal Comfort Control in Electric Vehicles

Lahlou, Anas; Ossart, Florence; Boudard, Emmanuel; Roy, Francis; Bakhouya, Mohamed

doi:10.1109/vppc.2018.8604983

Cited by 17 publications

(13 citation statements)

References 17 publications

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“…For this purpose, we formulate an optimization problem that consists into minimizing the global thermal comfort defined by (21) for a given amount of energy. Solving this problem using the dynamic programming approach presented in [20] provides reference solutions against which the real-time algorithm efficiency can be assessed:…”

Section: Resultsmentioning

confidence: 99%

“…In a former paper [20], we proposed to optimize the global passenger's thermal comfort over a trip, while taking into account the actual quantity of energy available for operating the HVAC system. The idea was that, if there is not enough embedded energy for both traction and thermal comfort for the planned trip, the thermal comfort should be diminished in order to save energy and allow the vehicle to reach its final destination.…”

Section: Introductionmentioning

confidence: 99%

“…An important point of this work is that we model the thermal comfort by the predicted mean vote criterion (PMV), proposed by Fanger in [22], and that reliably reflects the human perception of temperature and humidity [23]. The proposed approach was tested for 4000 test-cases and compared to an optimal dynamic programming-based approach [20]. The results show that the proposed real-time control is able to adjust the thermal comfort in case of low battery state-of-charge, and provides a near optimal tradeoff between energy consumption and thermal discomfort.…”

Section: Introductionmentioning

confidence: 99%

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A Real-Time Approach for Thermal Comfort Management in Electric Vehicles

et al. 2020

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The HVAC system represents the main auxiliary load in electric vehicles, but passengers’ thermal comfort expectations are always increasing. Hence, a compromise is needed between energy consumption and thermal comfort. The present paper proposes a real-time thermal comfort management strategy that adapts the thermal comfort according to the energy available for operating the HVAC system. The thermal comfort is evaluated thanks to the “Predicted Mean Vote”, representative of passenger’s thermal sensations. Based on traffic and weather predictions for a given trip, the algorithm first estimates the energy required for the traction and the energy available for thermal comfort. Then, it determines the best thermal comfort that can be provided in these energetic conditions and controls the HVAC system accordingly. The algorithm is tested for a wide variety of meteorological and traffic scenarios. Results show that the energy estimators have a good accuracy. The absolute relative error is about 1.7% for the first one (traction), and almost 4.1% for the second one (thermal comfort). The effectiveness of the proposed thermal comfort management strategy is assessed by comparing it to an off-line optimal control approach based on dynamic programming. Simulation results show that the proposed approach is near-optimal, with a slight increase of discomfort by only 3%.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Real-Time Approach for Thermal Comfort Management in Electric Vehicles

et al. 2020

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“…Another study in [19] has provided a simple air conditioning power consumption model (validated in real conditions). It takes in consideration temperatures/humidity outside cabin, inside and required by the driver.…”

Section: B Air Conditioning Modelmentioning

confidence: 99%

Energy Management For Electric Vehicles in Smart Cities: A Deep Learning Approach

Laroui

Dridi

Afifi

et al. 2019

2019 15th International Wireless Communications &Amp; Mobile Computing Conference (IWCMC)

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We propose a solution for Electric Vehicle (EV) energy management in smart cities, where a deep learning approach is used to enhance the energy consumption of electric vehicles by trajectory and delay predictions. Two Recurrent Neural Networks are adapted and trained on 60 days of urban traffic. The trained networks show precise prediction of trajectory and delay, even for long prediction intervals. An algorithm is designed and applied on well known energy models for traction and air conditioning. We show how it can prevent from a battery exhaustion. Experimental results combining both RNN and energy models demonstrate the efficiency of the proposed solution in terms of route trajectory and delay prediction, enhancing the energy management.

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“…The system is able to adjust the work status freely per requirement and avoid consuming additional power output; yet, it still is limited by battery jar capacity. Only reducing energy consumption of the air-conditioning system can elevate the durability of electric vehicle effectively [25].…”

Section: Structure Of Low-energy-consumption Vehicle Air-conditionmentioning

confidence: 99%

Design and Implementation of a Low-Energy-Consumption Air-Conditioning Control System for Smart Vehicle

Weng

Kau

2019

Journal of Healthcare Engineering

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About 7% of people’s daily time is spent in taking vehicles between office and home. Besides, with the improvement of the living standard in today’s society, people’s requirements for a comfortable environment inside the car are constantly increasing and this must rely on an effective vehicle air conditioner to maintain the comfort of the cabin environment. In general, a vehicle air conditioner uses the air-mixing mode to regulate the temperature control system. In this mode of operation, the compressor needs to work continuously, which is extremely energy consuming. The vehicle’s air conditioner is greatly affected by the inner and outer heat load, which are generated therein. Furthermore, the heat load is instantly changeable. Therefore, only when the controller can adapt to the feature of heat load, then we can find the optimal control method, thus enabling the vehicle’s air conditioner to interact with the actual heat load to supply the balanced cooling capacity and, as a result, create the most comfortable environment inside the cabin with minimum energy consumption. For this purpose, we bring up in this paper a low-energy-consumption smart vehicle air-conditioning control system to detect total heat load, which can change the vehicle’s air-conditioning capacity mode to maintain the average temperature at 25.2°C∼26.2°C and the average humidity at 46.6%∼54.4% in the cabin. When the inner heat load is stable, the rest times of the compressor can reach 16∼23 times per hour, which attains a rate of fuel saving around 21%∼28%. With the proposed architecture, the purpose of the low-energy-consumption vehicle air-conditioning system can be achieved, which, at the same time, creates a comfortable environment inside the cabin.

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A Dynamic Programming Approach for Thermal Comfort Control in Electric Vehicles

Cited by 17 publications

References 17 publications

A Real-Time Approach for Thermal Comfort Management in Electric Vehicles

A Real-Time Approach for Thermal Comfort Management in Electric Vehicles

Energy Management For Electric Vehicles in Smart Cities: A Deep Learning Approach

Design and Implementation of a Low-Energy-Consumption Air-Conditioning Control System for Smart Vehicle

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