Method for predicting remaining service life of battery based on maximum expectation-unscented particle filter

An unscented particle filter and maximum expectation technology, applied in interdisciplinary fields, can solve problems such as battery capacity increase, battery remaining service life prediction error, etc., and achieve the effect of reducing prediction error

Active Publication Date: 2022-07-22
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that a sudden increase in the existing battery capacity will cause a large error in the prediction of the

Method used

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  • Method for predicting remaining service life of battery based on maximum expectation-unscented particle filter
  • Method for predicting remaining service life of battery based on maximum expectation-unscented particle filter
  • Method for predicting remaining service life of battery based on maximum expectation-unscented particle filter

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specific Embodiment approach 1

[0036] Embodiment 1: The specific process of the battery remaining service life prediction method based on the maximum expectation-unscented particle filter in this embodiment is as follows:

[0037] Step 1. Extract the battery capacity data in the kth working process;

[0038] Extracting the capacity data of the battery in the kth actual working cycle, that is, the charge and discharge test can be carried out after each working cycle of the battery, and the battery capacity data under the corresponding cycle can be obtained as the input of the battery remaining service life prediction method.

[0039]Step 2. According to the battery capacity data in the k-th working process extracted in step 1, construct a dynamic battery degradation model based on unscented particle filtering, which is used to describe the degradation process of the battery;

[0040] Step 3. Estimate the process noise and measurement noise in the battery degradation model adaptively by using a maximum expect...

specific Embodiment approach 2

[0047] Embodiment 2: The difference between this embodiment and Embodiment 1 is that in step 2, a dynamic battery degradation model based on unscented particle filtering is constructed according to the battery capacity data extracted in step 1 during the k-th working process , which is used to describe the degradation process of the battery; the specific process is:

[0048] Compared with the Kalman filter algorithm and the extended Kalman filter algorithm, the advantage of particle filter is that it can deal with the problem of parameter identification of nonlinear and non-Gaussian systems. It should be noted that the traditional particle filter algorithm directly uses the prior probability density function as the importance function. However, the particles generated from the prior distribution do not necessarily reflect the true particle distribution, and the measurement information at the current moment of the system is ignored. To this end, the present invention considers...

specific Embodiment approach 3

[0118] Embodiment 3: The difference between this embodiment and Embodiment 1 or 2 is that in step 3, a maximum expectation-based algorithm is used to adaptively estimate the process noise and measurement noise in the battery degradation model;

[0119] Specifically, for the process noise and measurement noise in the dynamic degradation model based on unscented particle filtering, the maximum expectation algorithm is used for adaptive estimation. recursive update;

[0120] The specific process is:

[0121] The unknown parameter vector formed by process noise and measurement noise can be expressed as:

[0122] The essence of the EM algorithm is to maximize the joint likelihood function p(x under the premise of the posterior probability. 0:k ,y 1:k |Ξ) to achieve maximum likelihood estimation of unknown parameters Ξ. Known battery capacity data y from initial to k-th duty cycle 1:k =[y 1 ,y2,…,y k ], the joint log-likelihood function L can be constructed k (Ξ) is shown ...

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Abstract

The invention discloses a battery remaining service life prediction method based on maximum expectation-unscented particle filtering, and relates to a battery remaining service life prediction method. The objective of the invention is to solve the problem that the sudden increase of the capacity of the existing battery causes a great error in the prediction of the remaining service life of the battery. The method comprises the following steps: 1, extracting battery capacity data in the kth working process; 2, constructing a dynamic battery degradation model based on unscented particle filtering; 3, adaptively estimating process noise and measurement noise in the battery degradation model by adopting an expectation maximization algorithm; the process noise and the measurement noise are used in the (k + 1) th working process, and a dynamic battery degradation model based on unscented particle filtering is constructed; 4, judging whether a capacity regeneration phenomenon occurs in the kth working process or not; 5, solving a confidence interval between the remaining service life of the battery and the battery capacity; the method is used in the field of residual service life prediction of the battery.

Description

technical field [0001] The invention relates to the interdisciplinary field of combining state space theory, statistical analysis and prediction of the remaining service life of a lithium ion battery, in particular to a method for predicting the remaining service life of a battery. Background technique [0002] As an important energy storage device, lithium-ion batteries have become an important component of electric vehicles due to their high energy density, wide operating temperature range, and long cycle life. As the number of times of charging and discharging lithium-ion batteries increases, the capacity of the battery will gradually decrease, which will greatly reduce the reliability of the system or equipment, and even prone to catastrophic accidents in severe cases. Therefore, accurately predicting the remaining useful life (RUL) of the lithium-ion battery, in other words, accurately estimating the time elapsed from the current moment to the complete failure of the ba...

Claims

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Application Information

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IPC IPC(8): G01R31/367G01R31/392
CPCG01R31/367G01R31/392
Inventor 张九思李翔罗浩蒋宇辰吴诗梦田纪伦尹珅
Owner HARBIN INST OF TECH
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