Method for optimizing service life of automobile thermal manager based on deep learning

By introducing predictive coding networks and deep learning into automotive thermal management control, real-time perception and optimization of thermal manager lifespan are achieved. This solves the problem of undetected lifespan degradation in existing technologies, improves the accuracy of thermal manager lifespan prediction and system adaptability, and extends the service life of the thermal manager.

CN121756830AInactive Publication Date: 2026-03-31ZHUHAI WISDOM HI-TECH ELECTRIC TECH CO LTD
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Patent Information

Application Number
CN202511977281.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-25
Publication Date
2026-03-31
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Existing thermal management control methods fail to continuously detect the impact of lifespan degradation during normal system operation and lack technical means to directly introduce lifespan prediction errors into model updates and control decisions. As a result, thermal manager lifespan degradation is often only discovered when the system is close to failure, making it difficult to proactively suppress and optimize lifespan consumption.

Method used

By employing predictive coding networks and deep learning, life constraints are introduced into the automotive thermal management control process. Through multi-source time series data modeling, life evolution perception and proactive optimization are achieved. Operational state prediction pathways and lifespan state prediction pathways are constructed. Operational prediction errors and lifespan prediction errors are used to drive network updates and generate control commands for lifespan constraint optimization.

Benefits of technology

It realizes synchronous modeling and joint optimization control of the thermal manager's operating status and lifespan status, improves the effectiveness and availability of lifespan prediction results, can actively suppress lifespan consumption during normal system operation, extend the service life of the thermal manager, and improve the reliability and economy of the whole vehicle operation.

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Abstract

The invention discloses an automobile thermal manager life optimization method based on deep learning, and the method comprises the following steps: collecting data, recording a timestamp, and forming a multi-source time sequence; executing preprocessing and resampling, and constructing a running state vector sequence; constructing an input fragment sequence and an operation state prediction target sequence of the predictive coding network; setting an operation state prediction path and a life state prediction path, and generating an operation state prediction sequence and a life state prediction sequence; calculating an operation prediction error sequence, and calculating a life prediction error sequence; executing error-driven updating and updating parameters to obtain a predictive coding network subjected to life constraint optimization; calculating a life cost index and a thermal management performance index to jointly construct a control target; and updating a heat management control strategy, and generating and applying a control instruction sequence. The predictive coding network and deep learning are adopted, the service life constraint is introduced into the automobile thermal management control process, and service life evolution perception and active optimization are achieved.
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