A method for predicting power battery safety status based on a multi-level model structure
By combining a multi-level structural model with power battery and vehicle parameter information, the battery safety status is predicted, solving the complex and tedious detection problems in existing technologies and achieving rapid and accurate health status judgment and safety warnings.
CN119147971BActive Publication Date: 2025-09-09DONGFENG MOTOR GRP
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Patent Information
- Application Number
- CN202411064454.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-05
- Publication Date
- 2025-09-09
- Estimated Expiration
- 2044-08-05
AI Technical Summary
Technical Problem
In the existing technology, power battery safety monitoring relies on electrochemical parameters. The detection process is complex and tedious, and it is impossible to quickly and accurately judge the battery health status.
Method used
A method based on a multi-level structural model is adopted, combining power battery status information and vehicle parameter information, and the battery safety status is predicted through a battery status model, a driving behavior evaluation model and a power battery safety prediction model.
Benefits of technology
The speed and accuracy of judging the health status of power batteries have been improved, and safety warnings can be sent in a timely manner to reduce the risk of accidents.
✦ Generated by Eureka AI based on patent content.
Abstract
A method, device, equipment and storage medium for predicting the safety status of a power battery based on a multi-level structure model, comprising: obtaining data to be predicted within a first preset time period before a current moment according to a received prediction instruction, wherein the data to be predicted includes power battery status information and vehicle parameter information; based on a preset multi-level structure model, predicting the safety status information of the power battery within a first preset time period after a current moment according to the power battery status information and vehicle parameter information, wherein the preset multi-level structure model includes a battery status model, a driving behavior evaluation model and a power battery safety prediction model, solving the technical problem in related technologies of using internal electrochemical parameters of power batteries for safety monitoring, but with a complex detection process and cumbersome procedures, and being unable to simply and quickly determine the health status of the power battery, thereby improving not only the rate of determining the health status of the power battery, but also the accuracy.
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