Low-voltage battery failure prediction method based on new energy vehicle memory card data

Abstract

The invention relates to a low voltage storage battery failure prediction method based on new energy vehicle memory card data, and belongs to the technical field of data processing for transportation. The method includes the following steps of collecting all driving data samples which are transmitted to a data detection center in real time and are stored in T-BOX system memory cards mounted on various types of new energy vehicles, wherein the driving data samples comprise voltage change data of vehicle-mounted low-voltage storage batteries in the entire running process of the new energy vehicles; performing data statistics and excavation on the voltage change data of the vehicle-mounted low voltage batteries to obtain a basis for follow-up failure prediction determination of the various new energy vehicles, and carrying out failure prediction of a to be tested vehicle-mounted low voltage battery of a new energy vehicle. The method disclosed by the invention can provide an owner with a more real-time, convenient, fast and accurate battery failure prediction result than the traditional detection method.

Description

technical field [0001] The invention belongs to the technical field of data processing in transportation, and in particular relates to a low-voltage storage battery failure prediction method based on T-BOX memory card data of new energy vehicles. Background technique [0002] The T-BOX system on new energy vehicles is a set of electronic equipment that can realize vehicle data collection, storage, processing and transmission. T-BOX can deeply read the car CAN bus data and private protocols in the memory card. T-BOX The terminal has a dual-core processing OBD module and a dual-core processing CPU framework, respectively collects the bus data related to the car bus Dcan, Kcan, PTcan and the reverse control of the private protocol, and transmits the data to the cloud server through the GPRS network to provide vehicle condition reports, driving Reports, fuel consumption statistics, failure reminders, violation inquiries, location track, driving behavior, security and anti-theft,...

AI Technical Summary

Problems solved by technology

[0011] The common disadvantage of scheme 1 and scheme 2 is that the fault detection operation is more professional, and it is difficult for ordinary car owners to use; the real-time performance is poor, and the low-voltage battery must be removed from the car, and it can be judged whether there is a fault when it is measured by a professional. For car owners, it is difficult to know the specific time and frequency of detection

In addition, Option 1 emits a lot of heat during the test process and cannot continuously test multiple batteries; the tested battery must be recharged before it can be measured again; and the same result cannot be obtained after multiple measurements

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