Recession-mode predicting method of power battery of electric automobile

Abstract

The invention provides a recession-mode predicting method of a power battery of an electric automobile. The recession-mode predicting method comprises the following steps of: 1) carrying out data acquisition on a voltage curve in the discharging process of the power battery, and acquiring and recording the voltage curve of the power battery in discharging during the use process of the electric automobile; 2) carrying out classification on the acquired voltage curve; and 3) adopting a hidden Markov model to predict the recession mode of the power battery. The recession-mode predicting method provided by the invention has the advantages that the health condition of the battery can be evaluated in a more convenient, faster and more accurate manner, and the remaining life of the power battery of the electric automobile is predicted more accurately for one person according to the driving habits of different persons, so that the used battery can be better managed and planned.

Description

technical field [0001] The invention relates to a method for predicting a decline mode, in particular to a method for predicting a decline mode 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] Landy et al. conducted charge and discharge tests on the power battery. During the experiment, they took multiple temperatures in the range of 40-70°C as the ambient temperature, accelerated the charging and discharging of the power battery, and then based on the temperature, internal resistance and SOC change rate of the power battery, they proposed an experience Model. The model is as follows: [0006] R=At 1 / 2 +B [0007] Where: A=a{exp[b / T]}; [0008] B=c{exp[d / T]}. [0009] First of all, the above model is...

AI Technical Summary

Problems solved by technology

First of all, the above various methods are often limited to certain charging and discharging conditions, but in practical applications, the use of electric vehicles may be diverse, and its decay modes are also different. These methods existed when they were established. its limitations, so the remaining battery life predicted by these methods is not very accurate

Secondly, the above methods are too dependent on the capacity of the battery, and 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 obtain the capacity decline of electric vehicle power batteries in the actual use of electric vehicles, so the prediction will become more inaccurate

Images