Energetic optimization of automotive thermal systems using mixed-integer programming and model predictive control

Esqueda-Merino, Donovan; Dubray-Demol, Alexandra; Olaru, Sorin; Godoy, Emmanuel; Dumur, Didier

doi:10.1109/cca.2013.6662762

Cited by 3 publications

(4 citation statements)

References 12 publications

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“…In general, it is very short, typically between 1 and 30 s [15,16]. In rare cases, it can reach 10 min [17], 20 min [18] or even 30 min [19].…”

Section: Energy and Thermal Comfort Management Methodsmentioning

confidence: 99%

“…A set of 65 differential equations is built by writing the heat balance at each node of the model. into 16 segments (head, chest back, pelvis, left hand, right hand, left thigh, right thigh, left leg, right leg, left foot, and right foot), indexed by letter in the model Equations (17)(18)(19)(20)(21). Each body segments is in turn divided into four layers (core, muscle, fat, and skin), indexed by the letter .…”

Section: Thermo-physiological Modelmentioning

confidence: 99%

“…A set of 65 differential equations is built by writing the heat balance at each node of the model. The four layers of each of the sixteen body parts are modeled by the heat balance Equations (17)(18)(19)(20), where ( , ) and ( , ) denote the temperature and the heat capacity of the node ( , ), whereas and denote the temperature and heat capacity of the blood. Equation ( 21) represents the heat balance of the blood, which circulates through the whole body: The four layers of each of the sixteen body parts are modeled by the heat balance Equations ( 17)-( 20), where T (i,j) and C (i,j) denote the temperature and the heat capacity of the node (i, j), whereas T b and C b denote the temperature and heat capacity of the blood.…”

Section: Thermo-physiological Modelmentioning

confidence: 99%

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Optimal Management of Thermal Comfort and Driving Range in Electric Vehicles

et al. 2020

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The HVAC system represents the main auxiliary load in battery-powered electric vehicles (BEVs) and requires efficient control approaches that balance energy saving and thermal comfort. On the one hand, passengers always demand more comfort, but on the other hand the HVAC system consumption strongly impacts the vehicle’s driving range, which constitutes a major concern in BEVs. In this paper, a thermal comfort management approach that optimizes the thermal comfort while preserving the driving range during a trip is proposed. The electric vehicle is first modeled together with the HVAC and the passengers’ thermo-physiological behavior. Then, the thermal comfort management issue is formulated as an optimization problem solved by dynamic programing. Two representative test-cases of hot climates and traffic situations are simulated. In the first one, the energetic cost and ratio of improved comfort is quantified for different meteorological and traffic conditions. The second one highlights the traffic situation in which a trade-off between the driving speed and thermal comfort is important. A large number of weather and traffic situations are simulated and results show the efficiency of the proposed approach in minimizing energy consumption while maintaining a good comfort.

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“…In general, it is very short, typically between 1 and 30 s [15,16]. In rare cases, it can reach 10 min [17], 20 min [18] or even 30 min [19].…”

Section: Energy and Thermal Comfort Management Methodsmentioning

confidence: 99%

Section: Thermo-physiological Modelmentioning

confidence: 99%

Section: Thermo-physiological Modelmentioning

confidence: 99%

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Optimal Management of Thermal Comfort and Driving Range in Electric Vehicles

et al. 2020

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“…This is due to the chosen prediction horizon, which in general is a short horizon of the order of 1 to 30s [14,15]. In rare cases, the horizon can reach 10 min [16], 20 min [17] or even 30 min [18], but the entire trip horizon is not considered in order to adjust the passenger's thermal comfort if needed. Secondly, the passengers' thermal comfort is often modeled by a target temperature of 25 • C. Yet, this criterion can lead to thermal discomfort, with either hot or cold sensations, depending on the humidity and radiation temperature in the cabin [16].…”

Section: Introductionmentioning

confidence: 99%

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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