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Method, system and device for detecting faults of electrochemical energy storage system by utilizing machine learning

An energy storage system and machine learning technology, applied in neural learning methods, instruments, computer parts, etc., can solve the problem of electrochemical energy storage system not having systematization and scalability, to reduce the amount of data, improve efficiency, The effect of reducing the amount of contrast

Pending Publication Date: 2021-08-13
池测(上海)数据科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0035] The present invention provides a method for using machine learning to detect faults in electrochemical energy storage systems to solve the problem that detection of faults in electrochemical energy storage systems is a systematic fault. Electrochemical energy storage system failures, technical issues that are not systematized and expandable

Method used

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  • Method, system and device for detecting faults of electrochemical energy storage system by utilizing machine learning
  • Method, system and device for detecting faults of electrochemical energy storage system by utilizing machine learning
  • Method, system and device for detecting faults of electrochemical energy storage system by utilizing machine learning

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example 1

[0080] Real-time data collected from sensors, such as collected data, can (but not necessarily) be sorted and pre-processed before using machine-based algorithms. Including using the collected data to perform relevant mathematical operations, or as an input to a model, after performing relevant calculations, output data, images, or states, etc. are generated. For example, you can normalize the data or convert individual data values ​​into time series data, etc. Alternatively, perform statistical calculations on data, input them into physical or mathematical models, and generate output. All kinds of data collected by sensors, as well as their processed output data, images or states, can be used as input data (reading) for the algorithm described in this patent either individually or in combination.

[0081] These data can be objects, forming a data set for detecting faults in the electrochemical energy storage system. The data set can include one or more sub-data sets, and eac...

example 2

[0099] The influence factor is the voltage value of the single battery, then:

[0100] Extracting the input object data includes the voltage data after preprocessing the voltage sensor or the voltage sequence data collected at several time points;

[0101] The battery voltage is closely related to the state of the battery. Through the detection of the battery voltage data, it can be judged whether the state of the battery or the usage is normal. Therefore, the voltage value can be used as an influencing factor for fault early warning judgment.

[0102] Described machine learning algorithm is made up of input layer, output layer, long short-term memory Long Short-term Memory with five layers of hidden layers, LSTMs network, the number of nodes in the input and output layer is 96, the number of nodes in the 1st-5 hidden layers The number of neural nodes is 20, 10, 30, 20, and 20, and the model is trained using the mean square error, and the performance of the autoencoder is mea...

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Abstract

A method for detecting fauls of an electrochemical energy storage system by utilizing machine learning comprises the following steps: providing a data set for detecting the faults of the electrochemical energy storage system, wherein the data set comprises one or more sub-data sets, and each sub-data set comprises one or more input objects or object sequences for collecting and / or processing the faults of the electrochemical energy storage system; providing an auto-encoder structure, establishing at least one machine learning algorithm training for at least one influence factor for detecting the fault of the electrochemical energy storage system, and outputting a predicted value of the influence factor; extracting at least one or more current input object data or object sequence data, projecting the object data or object sequence data to corresponding autoencoders of the set of machine learning algorithms, outputting corresponding latent vectors, and outputting predicted values of corresponding impact factors after the latent vectors are used as inputs of corresponding decoders; through comparative analysis of the prediction values, detecting the prediction possibility of the fault of the electrochemical energy storage system.

Description

technical field [0001] The invention relates to the field of fault detection of electrochemical energy storage systems, in particular to a method and system for detecting faults of electrochemical energy storage systems using machine learning. Background technique [0002] Electrochemical energy storage systems are an important part of electric vehicles and grid applications. Such energy storage systems, such as lithium-ion batteries, lead-acid batteries, fuel cells, flow batteries, etc., are composed of individual smaller units, such as batteries, battery packs, modules, etc. combined to achieve the appropriate storage capacity and performance. For example, a battery system composed of multiple lithium-ion cells can be used as an energy storage system for industrial and consumer applications, such as electric vehicles and power grid applications. These batteries can be monitored to ensure their safety and performance using a battery management system. [0003] A fault in...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/044G06F18/2414G06F18/214
Inventor 不公告发明人
Owner 池测(上海)数据科技有限公司
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