Electric Vehicle Power Battery Life Prediction Method

Abstract

The invention provides a prediction method of electric vehicle power battery service life. The method includes the following steps that (1) data collection is conducted on the voltage curve of a power battery in the process of electro-discharge, battery residual life characterization is extracted, and a recession point is gained; (2) clustering is conducted on the collected voltage curve by the adoption of an ART2 neural network, and recession pattern classification is conducted on the voltage curve; (3) predication is conducted on the recession pattern of the power battery by the adoption of a weighted Markov model; (4) a single mode of recession pattern is built; (5) prediction is conducted on the residual service life of the power battery by the adoption of a linear superposition method. By means of the prediction method of electric vehicle power battery service life, the health condition of batteries can be evaluated conveniently, quickly and accurately, the residual service life of the power battery of an electric vehicle can be accurately predicted aiming to individuals according to different driving habits of different people, and the battery can be better managed, planned and used.

Description

technical field [0001] The invention relates to a life prediction method, in particular to a life prediction method for a power battery of an electric vehicle. Background technique [0002] In terms of power battery life prediction methods, it can be roughly divided into 1) model method and 2) data-driven method. [0003] 1) Model method [0004] At present, many power battery life predictions are done using model methods. [0005] Broussely et al. (see: Broussely M, Herreyre S, Biensan P, et al. Aging mechanism in Li ion cells and calendar life predictions [J]. Journal of PowerSources, 2001, 97: 13-21.) analyzed lithium batteries in different The attenuation of battery capacity during storage at different temperatures (15, 30, 40 and 60°C) and different voltages (3.8, 3.9 and 4.0V). They believe that after the negative electrode solid electrolyte interface (SEI) film is formed, the side reaction between the electrolyte and the surface of the interface film will cause the...

AI Technical Summary

Problems solved by technology

Its disadvantages are: the model needs fine parameters, and the complexity is high; the battery aging mechanism is complicated, which is the result of the joint action of multiple factors. The current research is not very thorough, and the test for aging factors is more complicated. The current parameters The model method often only considers one or several factors, while ignoring other factors, it is difficult to establish a perfect aging mechanism model, which increases the error

[0014] To sum up, the data-driven method is based on a large amount of experimental data, and uses various data analysis and learning methods to mine the hidden information to predict the decline of battery capacity. The aging law in a single experiment or under a single working condition cannot represent all applications; it is unrealistic to test all possible life-influencing factors in practical applications, and it is too dependent on capacity as a characteristic quantity. Capacity test method - both the rated current discharge and the 10-minute high current discharge need to stop the test, which is not allowed for online detection

First of all, the aging mechanism of the power battery involved in the above various model methods is complex and not perfect, and it is difficult to establish an accurate decline model, so the remaining life of the battery predicted by these methods is not very accurate

Secondly, the above various methods are often limited to one or several charging and discharging conditions, but in practical applications, the use of electric vehicles may be varied, and their decay modes are also different. There are limitations when using these methods, so the remaining battery life predicted by these methods is not very accurate

Finally, the above methods are all too dependent on the capacity of the battery. The remaining life of the battery can be predicted through the change of the past capacity of the battery. However, as mentioned above, it is difficult to accurately measure the power battery of an electric vehicle online in the actual use of electric vehicles. capacity, so forecasts will become less accurate

Images