Method of predicting service life of power cell based on big data

Abstract

The present invention discloses a method of predicting service life of a power cell based on big data. Parameter big data of the same type of the power cell are measured randomly under a different depth of discharge, a different temperature and a different rate of discharge. Big data analysis is performed on the parameter big data to obtain an analytic relation of the big data. Current big data are measured by a voltage-current sensor, simultaneously an improved Amp-hour integral method is adopted to obtain a State of Charge (SOC) and the depth of discharge is obtained from the relation withthe sum of the depth of discharge and the SOC being 1. Under a different depth of discharge, the throughput of one charging and discharging of the cell is equivalently deemed as an equivalent throughput. According to the equivalent throughput and a rated throughput, the residual service life of the cell can be calculated. The prediction method of the present invention, from characteristics of thepower cell, corrects the throughput of an effective charging and discharging with regard to the depth of discharge, an ambient temperature and the size of discharging current. Meanwhile, according tothe characteristics of the power cell, the method optimizes test of an initial SOC, thereby making a prediction result more accurate.

Description

technical field [0001] The invention belongs to the technical field of battery life prediction, and in particular relates to a power battery life prediction method based on big data. Background technique [0002] The current methods for predicting battery life in the market generally use complex algorithms to predict. There is a relatively simple method for predicting battery life based on the throughput method based on big data. However, there is no throughput method for the characteristics of power batteries. Moreover, the current throughput method generally only considers the impact of discharge depth on effective charge and discharge. The impact of throughput, the calculation results are not accurate. According to the characteristics of the power battery, it can be known that the depth of discharge will affect the number of cycles, the ambient temperature will affect the size of the open circuit voltage when SOC is measured, and the initial state of charge will affect t...

AI Technical Summary

Problems solved by technology

There is a relatively simple method for predicting battery life based on the throughput method based on big data. However, there is no throughput method for the characteristics of power batteries. Moreover, the current throughput method generally only considers the impact of discharge depth on effective charge and discharge. The impact of throughput, the calculation result is not accurate

According to the characteristics of the power battery, it can be known that the depth of discharge will affect the number of cycles, the ambient temperature will affect the size of the open circuit voltage when SOC is measured, and the initial state of charge will affect the size of the open circuit voltage at the rated temperature and the rest time when the open circuit voltage is measured. Moreover, these characteristics of the power battery are different from those of other batteries, which makes it not accurate enough to predict the life of the power battery with the existing throughput method

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