Online vehicle-mounted battery SOC (state of charge) prediction method based on big data and extreme learning machine

Abstract

The invention discloses an online vehicle-mounted battery SOC (state of charge) prediction method based on big data and an extreme learning machine. The external characteristic parameters of the battery such as voltage, current, temperature and internal resistance are selected, the large amount of online acquired external characteristic parameters of the battery are integrated through a big data method, a big data system for SOC prediction is formed, the data thus can be effectively excavated later, and the prediction precision is ensured; and through the extreme learning machine method, effective data most closely related to SOC prediction are found out, and the SOC is further accurately predicted according to the excavated effective data. The method has the advantages of high predictionprecision and strong practicability and the like.

Description

technical field [0001] The invention relates to a method for predicting the state of charge (SOC) of a vehicle-mounted battery. According to the external characteristic parameters such as the voltage, current, temperature, and internal resistance of the battery, based on big data and an extreme learning machine method, the SOC is calculated. Accurate predictions. Background technique [0002] As a key component of the power system of electric vehicles or hybrid vehicles, batteries are crucial to the power, safety and economy of the vehicle system. In order to ensure good battery performance and prolong its service life, it is necessary to manage and control the battery reasonably, but the premise must be to obtain the state of charge (SOC) of the battery accurately and reliably. As an internal characteristic of the battery, SOC cannot be directly measured, but can only be obtained by predicting some directly measured external characteristic parameters such as battery voltag...

AI Technical Summary

Problems solved by technology

Secondly, the external characteristic parameters of the battery include voltage, current, temperature and internal resistance, etc. In the actual prediction, not all parameters are used for prediction, and better prediction results can be obtained. There will be many combinations of prediction inputs, such as ( Voltage, current), (voltage, current, temperature), (voltage, current, internal resistance), etc., different combinations may result in different classification effects, and the accuracy is bound to be different, so the optimal input combination remains to be determined

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