Electric heavy truck battery fault pre-diagnosis method and device, electronic equipment and medium

By obtaining the operating condition data of the battery system and using the pre-trained battery fault warning model to generate prediagnosis orders, the problem that the battery fault diagnosis method in the prior art cannot achieve real-time online prediagnosis and low accuracy is achieved, and efficient Accurate battery failure prediagnosis and dynamic management.

CN119471416APending Publication Date: 2025-02-18HUNAN XINGBIDA NETLINK TECH CO LTD
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Patent Information

Application Number
CN202411539361.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-10-31
Publication Date
2025-02-18

AI Technical Summary

Technical Problem

Existing battery fault diagnosis methods cannot achieve real-time online prediagnosis, and the diagnosis accuracy is not high, making it difficult to effectively warn and manage battery faults.

Method used

By obtaining the operating condition data of the battery system, including temperature, current, voltage and state of charge, a pre-diagnosis order is generated based on a pre-trained battery fault warning model, including fault vehicle, fault type and fault time, and through applications, applets or The text message forwards the order to the service engineer for processing.

Benefits of technology

It realizes efficient, accurate and forward-looking battery failure prediagnosis, accurately locates the time point of failure in advance, and improves battery maintenance efficiency and use safety.

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Abstract

The invention provides an electric heavy truck battery fault pre-diagnosis method and device, electronic equipment and a medium. The method comprises the following steps: acquiring working condition data of a battery system; wherein the working condition data at least comprises the temperature, current, voltage and charge state of the battery; based on a pre-trained battery fault early warning model, generating a pre-diagnosis order according to the working condition data of the battery system; wherein the battery fault early warning model is obtained by performing training optimization according to a training sample set formed by vehicle working condition data and corresponding fault labels, and the pre-diagnosis order comprises a fault vehicle, a fault type and fault time; and based on the pre-diagnosis order, processing the pre-diagnosed fault, and closing the pre-diagnosis order after the processing is completed. According to the method, battery fault early warning is carried out through the pre-trained battery fault early warning model, the fault occurrence time point can be accurately positioned in advance, efficient, accurate and prospective battery fault pre-diagnosis is realized, dynamic management of the battery fault is further realized, and the battery maintenance efficiency and the use safety are improved.
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