Data-driven lithium ion battery cycle life prediction method based on AR (Autoregressive) model and RPF (Regularized Particle Filtering) algorithm

A lithium-ion battery, AR model technology, applied in the direction of measuring electricity, measuring devices, measuring electrical variables, etc., can solve the problems of complex forecasting method modeling and difficult parameter identification, and achieve trend forecasting, a good method framework, and improve accuracy. Effect

Inactive Publication Date: 2012-11-14
HARBIN INST OF TECH
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Problems solved by technology

[0007] In order to solve the problem of complex modeling and difficult parameter identification of the model-based prediction method in the existing lithium-ion battery cycle life prediction method, this applicati

Method used

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  • Data-driven lithium ion battery cycle life prediction method based on AR (Autoregressive) model and RPF (Regularized Particle Filtering) algorithm
  • Data-driven lithium ion battery cycle life prediction method based on AR (Autoregressive) model and RPF (Regularized Particle Filtering) algorithm
  • Data-driven lithium ion battery cycle life prediction method based on AR (Autoregressive) model and RPF (Regularized Particle Filtering) algorithm

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specific Embodiment approach 1

[0029] Specific implementation mode one: the data-driven lithium-ion battery cycle life prediction method based on the AR model and the RPF algorithm described in this implementation mode is:

[0030] 1) Monitor various physical parameters of the lithium-ion battery to be predicted, and obtain monitoring data;

[0031] 2) Use the RPF algorithm to track the state of the battery capacity data of the monitoring data, and determine the unknown parameter β in the empirical model of the RPF particle battery degradation battery 1 and beta 2 ;

[0032] 3) Initialize and set the starting point of prediction, the number of particles N, and the process noise W in the regularized particle filter model k The covariance R of , the observation noise V in the regularized particle filter model k The covariance Q of , the threshold U of the end of battery life;

[0033] 4) Determine the training length Length according to the prediction starting point, and use the battery capacity historica...

specific Embodiment approach 2

[0044] Embodiment 2: This embodiment is a further limitation of the data-driven lithium-ion battery cycle life prediction method based on the AR model and the RPF algorithm described in Embodiment 1. In Embodiment 1, the specific process of step 1 is as follows :

specific Embodiment approach 3

[0045] Specific embodiment three: The described RPF particle battery degraded battery empirical model is:

[0046] C k+1 = η C C k +β 1 exp(-β 2 / Δt k ) (1)

[0047] Among them, C k Indicates the charge capacity of the kth charge-discharge cycle, Δt k Indicates the rest time from the kth cycle to the k+1th cycle, β 1 and beta 2 is the parameter to be determined.

[0048] PF-based battery cycle life prediction method such as figure 2 shown. The entire prediction framework consists of four parts: sensor data acquisition and processing, data feature extraction, particle filter state tracking, long-term prediction of battery capacity, and cycle life calculation.

[0049] The PF algorithm in the prediction needs to establish a state transition equation and an observation equation based on the battery degradation process model, but in actual working conditions, it is difficult to establish an accurate physical model of the battery degradation process based on the ele...

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Abstract

A data-driven lithium ion battery cycle life prediction method based on an AR (Autoregressive) model and an RPF (Regularized Particle Filtering) algorithm relates to a lithium ion battery cycle life prediction method and belongs to the technical field of data prediction. The invention solves the problems in the existing lithium ion battery cycle life prediction method that the model-based prediction method is complicated in modeling, and parameters are difficult to identify. The data-driven lithium ion battery cycle life prediction method combines time sequence analysis with particle filter method and comprises the following steps: the AR model is firstly utilized to realize the multi-step prediction on battery performance degradation process time sequence data; and then, aiming at the problem of uncertainty expression of the cycle life prediction result, the regularized particle filtering method is introduced, and a lithium ion battery cycle life prediction method framework is proposed. The method proposed by the invention can be used for effectively predicating the cycle life of a lithium ion battery and realizes the output of probability density distribution of the predication result, has good computational efficiency and uncertainty expression ability.

Description

technical field [0001] The invention relates to a lithium ion battery cycle life prediction method and the technical field of data prediction. Background technique [0002] As an advanced battery technology developed at the end of the 20th century, lithium-ion batteries have the advantages of high energy ratio, high voltage, good low temperature performance, low self-discharge rate and no memory effect, etc., and have been widely used in notebook computers, cameras and mobile communications It can be said that lithium-ion batteries have gradually become the key and supporting technology in many important fields in the future. [0003] As the core unit of many key electronic devices and complex systems, lithium-ion batteries play a vital role in the function of the entire electronic system. But at the same time, due to its own practical problems such as safety management, performance degradation and life estimation, people have to pay full attention to its storage, use and m...

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Application Information

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IPC IPC(8): G01R31/36
Inventor 刘大同马云彤罗悦王红庞景月彭宇
Owner HARBIN INST OF TECH
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