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Method for estimating health status of lithium battery on basis of dynamic Bayesian network

A dynamic Bayesian, health status technology, applied in the direction of measuring electricity, measuring electrical variables, measuring devices, etc., can solve problems such as difficulty in measuring battery internal resistance

Active Publication Date: 2013-09-04
HANGZHOU DIANZI UNIV
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Problems solved by technology

However, it is difficult to measure the internal resistance of the battery, and the internal resistance of the battery is not only related to SOH, but also related to the state of charge of the battery (hereinafter referred to as SOC).

Method used

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  • Method for estimating health status of lithium battery on basis of dynamic Bayesian network
  • Method for estimating health status of lithium battery on basis of dynamic Bayesian network
  • Method for estimating health status of lithium battery on basis of dynamic Bayesian network

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Embodiment Construction

[0035] A method for estimating the state of health of a lithium battery based on a dynamic Bayesian network, the specific steps are:

[0036] Step 1: Acquisition of training data, specifically including the following process:

[0037] (1) Carry out a capacity test on B batteries of the same type with different durations of use and degrees of old and new: i ( i =1,2,..., B) only battery, first charge it to the cut-off voltage with a constant current of 0.4C Afterwards, carry out constant voltage charging. When the charging current drops below 0.01C, the battery is considered to be fully charged. At this time, the battery is discharged to its cut-off voltage with a current of 1C. , record the discharge time , calculate the actual capacity of the battery as , and calculate the actual capacity Q and the nominal capacity Q n The ratio ;

[0038] (2) The first step in the above step (1) i Just let the battery stand for an hour and a half. After the battery is stable, ch...

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Abstract

The invention relates to a method for estimating the health status of a lithium battery on the basis of a dynamic Bayesian network. The current method cannot meet the requirement of online detection and is poor in precision. A large amount of training data is acquired through ageing tests, and then a corresponding dynamic Bayesian network model is trained for a plurality of battery health statuses. In a real-time estimation stage, the voltage data of the battery is acquired for one time at set intervals, and the dynamic Bayesian network model to which a voltage sequence belongs is calculated through forward procedure recursion, so that the health status of the battery is accurately estimated. The health status of the battery can be conveniently estimated in real time by the method, the calculation speed is high, and the estimation is accurate.

Description

technical field [0001] The invention belongs to the field of battery technology, and in particular relates to a method for estimating the state of health of a lithium battery based on a dynamic Bayesian network. Background technique [0002] Lithium batteries have the advantages of high voltage, high energy density, low self-discharge rate and long service life, and are widely used in many fields. The life of the battery is affected by many factors, such as the working environment of the battery and the charging and discharging characteristics of the battery, so the battery life cannot always reach the number of cycles advertised by the manufacturer. If the battery is replaced prematurely, it will cause huge economic losses. Conversely, if the battery is replaced too late, it will seriously affect the reliability of the system. In order to achieve the best compromise between system reliability and economic benefits, it is necessary to know the battery's state of health (St...

Claims

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Application Information

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IPC IPC(8): G01R31/36
Inventor 何志伟高明煜马国进陈三省李芸刘国华
Owner HANGZHOU DIANZI UNIV
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