Joint management regulation and control method and system for automobile air conditioners

The unified air conditioning management method and system address the challenge of dynamic adjustment by using adaptive algorithms and reinforcement learning to optimize control signals, resulting in improved comfort and efficiency in automobile air conditioning systems.

CN119682470APending Publication Date: 2025-03-25JIANGSU HAOFENG AUTO PARTS
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Patent Information

Application Number
CN202411851919.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-16
Publication Date
2025-03-25

AI Technical Summary

Technical Problem

Existing automobile air conditioning systems lack the ability to dynamically adjust control strategies based on real-time environmental changes and individual passenger needs, failing to achieve a balance between comfort and energy efficiency.

Method used

A method and system for unified air conditioning management that involves collecting vehicle and environmental data, passenger comfort requirements, and using adaptive algorithms and reinforcement learning to optimize control signals, incorporating dynamic weight coefficients and real-time feedback for continuous optimization.

Benefits of technology

This approach enhances passenger comfort and optimizes air conditioning system performance by dynamically adjusting to environmental changes and passenger needs, achieving a balance between comfort and energy efficiency.

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Abstract

The invention relates to the technical field of automobile air conditioner control, in particular to an automobile air conditioner combined management regulation and control method and system.The method comprises the steps that automobile interior and exterior environment data and automobile state information are obtained, and an automobile comprehensive data set is obtained; passenger comfort demand data are obtained; calculating an energy consumption demand and a comfort demand of the air conditioner based on the vehicle comprehensive data set and the passenger comfort demand data, and generating a preliminary optimization control signal; the optimized control signal is adjusted in real time through a self-adaptive algorithm according to the change of the environment inside and outside the vehicle and the sensible temperature feedback of passengers; and continuously collecting passenger feedback data and environment change data, and optimizing a control signal based on a reinforcement learning model to obtain a final dynamic management regulation and control scheme. By means of the air conditioning system, the problems that an existing air conditioning system cannot comprehensively consider changes of the environment inside and outside the automobile and individual requirements of passengers and lacks dynamic regulation and control and self-learning optimization capacity are effectively solved, the comfort of the passengers is improved, and the overall performance of the air conditioning system is optimized.
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