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Battery SOC dual-state switching estimation method based on BAS optimized ElmanNN-AH method

A battery and algorithm technology, applied in the field of battery SOC double-state switching algorithm based on BAS optimized ElmanNN-AH method, can solve the problems of large SOC estimation error and heavy estimation workload

Inactive Publication Date: 2019-12-17
XIAN UNIV OF SCI & TECH
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  • Application Information

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Problems solved by technology

[0004] In order to solve the problems of large estimation error and large estimation workload of SOC in the prior art, the present invention provides a more accurate estimation algorithm of SOC, that is, adopts BAS algorithm to optimize the weights in ElmanNN, and based on ElmanNN and correction The switching method of the safety time method switches different estimation algorithms for different battery states. Such a switching mode estimation algorithm can effectively improve the accuracy of SOC estimation.

Method used

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  • Battery SOC dual-state switching estimation method based on BAS optimized ElmanNN-AH method
  • Battery SOC dual-state switching estimation method based on BAS optimized ElmanNN-AH method
  • Battery SOC dual-state switching estimation method based on BAS optimized ElmanNN-AH method

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Embodiment 1

[0031] Described mode one comprises the following steps:

[0032] Step 1.1 Collect data: battery terminal voltage, current rate, ambient temperature, battery cycle times, and delivery time;

[0033] Step 1.2 Substitute the collected data into the ElmanNN S model optimized by the BAS algorithm to calculate the battery SOC value.

[0034] The steps of the S model of the ElmanNN optimized by the BAS algorithm are:

[0035] Step 1.11 uses the BAS algorithm to find the optimal initial weight threshold of ElmanNN;

[0036] Step 1.12 applies the obtained optimal initial weight threshold to the already set ElmanNN.

[0037] Further, Mode 1: The specific process of online estimation of SOC based on the ElmanNN model optimized by BAS algorithm is as follows:

[0038] 1. Construction of ElmanNN

[0039] ElmanNN (Neural Networks) is a typical dynamic recursive neural network. It is based on the basic structure of BP network and adds a succession layer to the hidden layer as a one-step...

Embodiment 2

[0085] The second mode includes the following steps:

[0086] Step 2.1 judge whether it was in working state at the previous moment;

[0087] Step 2.2 If no, collect data: battery terminal voltage, current rate, ambient temperature, use the OCV method to accurately measure the SOC value of the collected data; if so, collect data: battery terminal voltage, current rate, ambient temperature, use the collected data to The amended ampere-hour integral method is used for estimation.

[0088] The modified ampere-hour integration method introduces life factor γ and temperature factor C T And the efficiency factor η corrects the ampere-hour integral method.

[0089] Further mode two: online estimation of SOC based on modified ampere-hour integral method

[0090] When the OCV value estimated by the measured terminal voltage is in the climbing area, the SOC estimation strategy is switched to the modified ampere-hour integration method.

[0091] The definition formula of ampere-hour ...

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Abstract

The invention belongs to the field of power battery management systems, and particularly relates to a battery SOC dual-state switching estimation method based on a BAS optimized ElmanNN-AH method. Thebattery SOC dual-state switching estimation method adopts a BAS algorithm for optimizing weight in ElmanNN, and switches different estimation algorithms for different battery states according to a switching method of the ElmanNN and a correction ampere-hour method. The battery SOC dual-state switching estimation method eliminates the defects that the initial SOC is difficult to measure and errorsare accumulated when an ampere-hour integral method is adopted, reduces the training data and training times of a neural network and reduces the workload.

Description

technical field [0001] The invention belongs to the field of power battery management systems, and in particular relates to a BAS-based optimized ElmanNN-AH method battery SOC binary switching algorithm. Background technique [0002] State of Charge (SOC) is the most important parameter in the power battery management system. However, due to the internal complexity of the chemical battery, this parameter cannot be directly measured, and can only be estimated based on a model or a corresponding algorithm. [0003] At present, there are many estimation methods of SOC, but each single method has both advantages and disadvantages. The SOC measured by the open circuit voltage method is very close to the electromotive force, so it can be used as a battery SOC standard. However, it is necessary to make the battery stand still for a period of time during the measurement, so it cannot be used in work; the calculation of the ampere-hour integral method is simple, but its estimation ...

Claims

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Application Information

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IPC IPC(8): G01R31/3828G01R31/3842G06N3/04G06N3/08
CPCG01R31/3828G01R31/3842G06N3/08G06N3/045
Inventor 寇发荣王思俊
Owner XIAN UNIV OF SCI & TECH
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