Modeling Air Conditioning System with Storage Evaporator for Vehicle Energy Management

Zhang, Quansheng; Canova, Marcello

doi:10.1007/978-3-319-33590-2_11

Cited by 7 publications

(6 citation statements)

References 25 publications

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“…Emails: {autowang, jingsun, mamini}@umich.edu tion power related losses, whereas the impact of thermal management has not been fully explored. Previous research has addressed the energy management of the A/C system for vehicles with traditional internal combustion engines (ICEs) [4] [5], where the A/C compressor is belt-driven by the ICE. The energy management problem considered in these references was solved by a dynamic programming (DP) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Climate Control of Connected and Automated Vehicles for Improved Energy Efficiency

Wang

Kolmanovsky

Amini

et al. 2018

2018 Annual American Control Conference (ACC)

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This paper considers an application of model predictive control to automotive air conditioning (A/C) system in future connected and automated vehicles (CAVs) with battery electric or hybrid electric powertrains. A control-oriented prediction model for A/C system is proposed, identified, and validated against a higher fidelity simulation model (CoolSim). Based on the developed prediction model, a nonlinear model predictive control (NMPC) problem is formulated and solved online to minimize the energy consumption of the A/C system. Simulation results illustrate the desirable characteristics of the proposed NMPC solution such as being able to enforce physical constraints of the A/C system and maintain cabin temperature within a specified range. Moreover, it is shown that by utilizing the vehicle speed preview and through coordinated adjustment of the cabin temperature constraints, energy efficiency improvements of up to 9% can be achieved.

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Section: Introductionmentioning

confidence: 99%

Model Predictive Climate Control of Connected and Automated Vehicles for Improved Energy Efficiency

Wang

Kolmanovsky

Amini

et al. 2018

2018 Annual American Control Conference (ACC)

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“…However, setting PID parameters requires expertise or engineering experience, making their control often suboptimal and challenging to meet cabin thermal comfort and low energy consumption requirements simultaneously. Model-based optimization control methods, such as dynamic programming [5] and model predictive control [6], have been extensively researched because they can achieve optimal results while satisfying air conditioning system constraints. However, these methods require highly accurate and simplified dynamic models of the air conditioning system and have high hardware computational demands, limiting their practical applicability.…”

Section: Introductionmentioning

confidence: 99%

Energy-Saving and Thermal Comfort Control of Electric Vehicle Air Conditioning Systems with Deep Reinforcement Learning

Dai,

Li,

Liu

et al. 2024

Preprint

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This study employs deep reinforcement learning algorithms, including Deep Q-Network, Deep Deterministic Policy Gradient, Twin Delayed Deep Deterministic Policy Gradient, and Soft Actor-Critic, to control the air conditioning system of electric vehicles to improve thermal comfort and reduce energy consumption. Additionally, random adjustments to environmental temperature and solar radiation intensity during the training process are made to enhance the algorithms' applicability. The results demonstrate that these algorithms significantly reduce energy consumption while maintaining thermal comfort. Notably, the Deep Deterministic Policy Gradient algorithm achieves an impressive 37.6% reduction in energy consumption. Comparative analysis among the algorithms reveals that Deep Q-Network, Deep Deterministic Policy Gradient, and Twin Delayed Deep Deterministic Policy Gradient exhibit relatively stable control behaviors. In contrast, the Soft Actor-Critic algorithm's compressor control curve exhibits more significant fluctuations, potentially leading to mechanical wear. Deep Q-Network, Deep Deterministic Policy Gradient, and Twin Delayed Deep Deterministic Policy Gradient algorithms consistently demonstrate effective thermal comfort control and energy-saving performance in various operating conditions.

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“…Overheating can also drastically reduce battery life and durability. Optimizing the battery thermal management system, providing a healthy reaction temperature for charging and discharging the battery pack, and prolonging the service life of the battery are the focus of research [1]. In addition, the passenger cabin air conditioning system of new energy vehicles is also directly linked to the comfort of passengers, which is a special attention of consumers.…”

Section: Introductionmentioning

confidence: 99%

Research Status of New Energy Vehicle Thermal Management

Zhu

2024

HSET

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Thermal management of new energy vehicles, as a way to break through the driving range of new energy vehicles and one of the guarantees of safety performance, has been paid more and more attention to. In order to improve the thermal management efficiency of new energy vehicles, the thermal management mode of the whole vehicle should be optimized, so that different parts can work at a suitable temperature, so as to ensure the functional safety and service life of the vehicle. Based on this, this paper analyzes the passenger cabin thermal management system through Positive Temperature Coefficient (PTC) heating air conditioning, heat pump air conditioning and other technologies. This paper introduces the new energy vehicle battery thermal management system from the aspects of direct cooling mode and heating mode, introduces the battery thermal management system through the analysis of flow charts, and introduces the cooling mode of electric control and motor, and pays attention to some cutting-edge research progress, and summarizes the automobile thermal management technology theory.

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Modeling Air Conditioning System with Storage Evaporator for Vehicle Energy Management

Cited by 7 publications

References 25 publications

Model Predictive Climate Control of Connected and Automated Vehicles for Improved Energy Efficiency

Model Predictive Climate Control of Connected and Automated Vehicles for Improved Energy Efficiency

Energy-Saving and Thermal Comfort Control of Electric Vehicle Air Conditioning Systems with Deep Reinforcement Learning

Research Status of New Energy Vehicle Thermal Management

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