Thermal management optimization of a heat-pump-based HVAC system for cabin conditioning in electric vehicles

Nunzio, Giovanni De; Sciarretta, Antonio; Steiner, Alois; Mladek, Alexander

doi:10.1109/ever.2018.8362408

Cited by 13 publications

(5 citation statements)

References 7 publications

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“…They are used to evaluate the maximum gains that can be reached by a proper energy management. Such approaches require solving an optimization problems considering the whole trip, usually by dynamic programming (DP) [3] or by the Pontryagin minimum principle (PMP) [4]. In practice, these approaches are not suitable for implementation in real systems, since the future conditions are uncertain, and the solving algorithms are time consuming.…”

Section: Energy and Thermal Comfort Management Methodsmentioning

confidence: 99%

“…In this work, the optimization problem was solved by dynamic programming. Another work [4] uses the PMP algorithm, after linearizing and simplifying the HVAC model. Although PMP is computationally efficient, the initialization of the costate variables can be tricky, especially if there are several state variables.…”

Section: Energy and Thermal Comfort Management Methodsmentioning

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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Section: Energy and Thermal Comfort Management Methodsmentioning

confidence: 99%

Section: Energy and Thermal Comfort Management Methodsmentioning

confidence: 99%

Optimal Management of Thermal Comfort and Driving Range in Electric Vehicles

et al. 2020

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“…The study [18] concludes that using a predictive control for scheduling energy loads can significantly improve the energy consumption of a Thermal Management (TM) system in BEVs. The study [19] proposes to use the thermal mass of the traction battery to store thermal energy when the vehicle is connected to the grid. The stored thermal energy can then be used during driving to enhance the heat-up performance of the passenger cabin.…”

Section: A Previous Research In the Fieldmentioning

confidence: 99%

Innovative HMI and Control Concept for Efficient Thermal Management of Electric Vehicles

Dvorak

Kruger

Wang

2022

IEEE Trans. Intell. Transport. Syst.

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This study presents a practical design and implementation of a Thermal Management (TM) concept for Battery-Electric Vehicles (BEVs) including a novel Human-Machine Interface (HMI). The developed system uses a combination of convective air conditioning and radiation heating to cool down and heat up the passengers efficiently. The proposed contribution can be subdivided into four parts: 1) description of the Heating, Ventilation and Air Conditioning (HVAC) system; 2) development of a novel HMI; 3) derivation of an appropriate operating strategy concept; 4) validation of the entire TM system based on a user study using a demonstrator vehicle. The results show that the developed TM concept can substantially support the user to operate the HVAC system more efficiently. A user study was conducted to evaluate passenger comfort and usability. The study revealed that users tend to operate a conventional input panel of an HVAC system inefficiently in some cases. They could achieve better task performance with the new HMI proposed in this study. However, the acceptance of the new concept was rated lower for the new system, as users were already accustomed to the conventional input panel design.

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“…Some authors successfully customized passenger comfort by integrating a learning module [3,4]. In order to assess the effectiveness of these approaches, some authors have compared them to off-line optimal control methods like dynamic programming [5][6][7] and the minimum principle of Pontryagin [8]. In the second category of on-line approaches, the model predictive control model (MPC) is practically the only optimal algorithm that has been studied for HVAC control.…”

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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Thermal management optimization of a heat-pump-based HVAC system for cabin conditioning in electric vehicles

Cited by 13 publications

References 7 publications

Optimal Management of Thermal Comfort and Driving Range in Electric Vehicles

Optimal Management of Thermal Comfort and Driving Range in Electric Vehicles

Innovative HMI and Control Concept for Efficient Thermal Management of Electric Vehicles

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

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