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Prediction method for life of secondary battery based on particle filter and mechanism model

A secondary battery and mechanism model technology, which is applied in the fields of electrical digital data processing, special data processing applications, instruments, etc., can solve the problems of poor accuracy of battery life prediction results and neglect of the mechanism characteristics of the prediction object, and achieve high accuracy and prediction The effect of error reduction and accurate prediction

Active Publication Date: 2016-10-26
珠海中力新能源科技有限公司
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Problems solved by technology

[0005] The invention aims to solve the problem that the traditional particle filter-based secondary battery life prediction is completely based on data-driven, ignoring the mechanism characteristics of the predicted object, resulting in poor accuracy of battery life prediction results

Method used

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  • Prediction method for life of secondary battery based on particle filter and mechanism model
  • Prediction method for life of secondary battery based on particle filter and mechanism model
  • Prediction method for life of secondary battery based on particle filter and mechanism model

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specific Embodiment approach 1

[0020] Specific implementation mode one: refer to Figure 1 to Figure 3 Specifically explain this embodiment, a secondary battery life prediction method that combines a particle filter and a mechanism model described in this embodiment, it includes the following content:

[0021] Step 1, constructing a mechanism model of the secondary battery, the mechanism model of the secondary battery can simulate the curve of the charging and discharging voltage of the battery as a function of time under any current condition;

[0022] Step 2. Training phase: Aging the secondary battery in step 1 under normal operating conditions for a period of time, and measuring the charging and discharging curves of the secondary battery during the aging process off-line with a fixed number of charge and discharge cycles at each interval using dynamic conditions. The voltage obtained at this time is the actual secondary battery output voltage U,

[0023] Input the same dynamic working condition curren...

specific Embodiment approach 2

[0059] Specific embodiment 2: This embodiment is a further description of the life prediction method of a secondary battery combined with a particle filter and a mechanism model described in specific embodiment 1. In this embodiment, in step 1, the secondary battery The mechanism model is:

[0060] U(t)=f[I(t), P(k)] (Formula 2),

[0061] In the formula, I(t) is the given current, f is the function map, P(k) is the parameter set of the secondary battery, k is the number of charge and discharge cycles, and the parameter set P changes with the increase of the number of charge and discharge k, U(t) is the external measurable voltage of the secondary battery.

specific Embodiment approach 3

[0062] Specific embodiment three: This embodiment is a further description of a secondary battery life prediction method that combines a particle filter and a mechanism model described in specific embodiment one. In this embodiment, in step two, the objective function is:

[0063]

[0064] In the formula, I(t) is the given current, P is the parameter set to be identified, S is the search space of the parameter set, N is the number of data points on the curve of voltage versus time, is the simulation output voltage of the mechanism model, and U is the actual battery output voltage.

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Abstract

Provided is a prediction method for life of a secondary battery based on particle filter and a mechanism model. The invention is aimed at solving the problem in the prior art that the prediction for life of a secondary battery is totally based on data drive and does not take the defect of an object mechanism so that a prediction result for the life of an electrochemical power source has poor accuracy.In a training stage, a particle filter method is utilized for tracking the actual value of internal state variables for the battery such that a regression equation of state variables varying with charge-discharge cycle frequency changes is obtained as a new state equation. In a prediction stage, the new state equation is utilized for calculating estimation values of state variables during unknown charge-discharge circulation in order to generate multiple particles. Multiple estimation values for capacity observation values are taken into an observation equation such that medians of estimation values for multiple capacity observation value are utilized for predicting the battery capacity in the future during the charge-discharge circulation. When the pre-set battery capacity reaches the lower limit, difference value between cycle numbers corresponding to the capacity observation value and cycle numbers used in the training stage is used as the residual number of cycles available for the battery.

Description

technical field [0001] The invention relates to a new battery life prediction method combining secondary battery (including lithium ion battery and lead-acid battery, hereinafter referred to as battery) mechanism model simulation technology and particle filter algorithm. It belongs to the field of equipment reliability. Background technique [0002] In recent years, secondary rechargeable batteries such as lead-acid batteries and lithium-ion batteries have been widely used in fields such as electric vehicles and smart grids. From the perspective of use, battery life has become a bottleneck restricting the development of electric vehicles and smart grids. [0003] Accurately predicting battery life is a basic requirement for state-based battery system maintenance, and is crucial to improving battery system reliability and cost savings. The life prediction methods of batteries can be divided into three categories: "Based on aging mechanism", it is necessary to know and model...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
CPCG06F30/367
Inventor 吕超葛腾飞丛巍李俊夫刘璇
Owner 珠海中力新能源科技有限公司
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