Charge state estimation apparatus for secondary battery, abnormality detection apparatus for secondary battery, abnormality detection method for secondary battery, and management system for secondary battery

A technology for secondary battery and abnormality detection, which is applied to the field of vehicles using neural networks, can solve problems such as circuit errors, achieve high-precision abnormality detection, realize abnormality detection, and improve the accuracy of abnormality detection.

Pending Publication Date: 2020-10-16
SEMICON ENERGY LAB CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Larger electromagnetic noise produces electromagnetic interference (EMI: Electromagnetic Interference) that affects the operation of other devices through power lines, etc., sometimes causing circuit malfunctions, for example.

Method used

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  • Charge state estimation apparatus for secondary battery, abnormality detection apparatus for secondary battery, abnormality detection method for secondary battery, and management system for secondary battery
  • Charge state estimation apparatus for secondary battery, abnormality detection apparatus for secondary battery, abnormality detection method for secondary battery, and management system for secondary battery
  • Charge state estimation apparatus for secondary battery, abnormality detection apparatus for secondary battery, abnormality detection method for secondary battery, and management system for secondary battery

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Experimental program
Comparison scheme
Effect test

Embodiment approach 1

[0060] In this embodiment, refer to Figure 1A An example of application to an electric vehicle (EV) is shown.

[0061] The electric vehicle is provided with a first battery 301 serving as a secondary battery for main driving and a second battery 311 that supplies electric power to an inverter 312 that starts an engine 304 . In this embodiment, the abnormality monitoring unit 300 driven by the power source of the second battery 311 collectively monitors a plurality of secondary batteries constituting the first battery 301 . Furthermore, a correction unit 320 is provided in which a signal that cancels unnecessary noise from the engine 304 and the like is generated, the signal is corrected, and the corrected signal is input to the abnormality monitoring unit 300 . Abnormality monitoring unit 300 performs abnormality detection of micro-short circuits and state-of-charge estimation using calculations. Note that the abnormality monitoring unit 300 monitors the temperature of a tem...

Embodiment approach 2

[0124] According to Embodiment 1, burst noise such as a micro short circuit can be detected. When the value calculated in the above formula 8 exceeds the threshold value, the micro-short circuit can be identified, other noises can be classified, and machine learning can be performed by associating the noise with the driving mode.

[0125] If it can be correlated like a micro-short circuit, it can be seen that the cause of the noise is the secondary battery. In addition, it collects data from engines, inverters, converters, wireless modules, etc., analyzes and learns to identify what kind of noise the noise is, and classifies the noise. If an abnormality is detected, not only an abnormality of the secondary battery but also a failure or a sign of failure of a motor, an inverter, a converter, a wireless module, etc. can be detected.

[0126] In addition, when forming a signal for canceling noise, the noise can be canceled by superimposing the inverted signal, and the charging r...

Embodiment approach 3

[0140] In this embodiment, an example of the structure of the neural network NN used for the neural network processing at the time of the SOC estimation process performed by CPU501 shown in FIG. 4 in Embodiment 2 is shown.

[0141] Figure 5A An example of a neural network of one embodiment of the present invention is shown. Figure 5A The shown neural network NN comprises an input layer IL, an output layer OL and a hidden layer (intermediate layer) HL. The neural network NN may be constituted by a neural network including a plurality of hidden layers HL, that is, a deep neural network. Also, learning in deep neural networks is sometimes referred to as deep learning.

[0142] Figure 5A The illustrated output layer OL, input layer IL and hidden layer HL each have a plurality of neuron networks, the neuron networks arranged in different layers being connected to one another via synaptic circuits.

[0143] The neural network NN has a function of analyzing the state of the seco...

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Abstract

Provided is a control method for a secondary battery in which a malfunction is less likely to occur and which can perform abnormality detection with high accuracy. This charge state estimation apparatus for a secondary battery includes: a device for generating an electromagnetic noise; a first detection means for measuring a voltage value of the secondary battery that is electrically connected with the device; a second detection means for detecting a current value of the secondary battery that is electrically connected with the device; a correction means for extracting a causal relationship between the electromagnetic noise and a driving pattern from data that is obtained by using the first detection means or the second detection means and that includes a plurality of electromagnetic noises, and performing data correction on the basis of the causal relationship; and a calculation means for calculating a charging rate by using a regression model on the basis of data after the data correction.

Description

technical field [0001] One aspect of the present invention relates to an article, a method, or a manufacturing method. The present invention relates to a process (process), machine (machine), product (manufacture) or composition (composition of matter). One aspect of the present invention relates to a semiconductor device, a display device, a light emitting device, a secondary battery, a lighting device, or an electronic device. Moreover, one aspect of this invention relates to the abnormality detection method of a secondary battery, and the charging control method of a secondary battery. In particular, it relates to a secondary battery abnormality detection system, a secondary battery charging system, and a secondary battery management system (also referred to as a BMS "battery management system"). [0002] In addition, in this specification, an electric storage device refers to all elements and devices having an electric storage function. For example, the power storage de...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/367G01R31/392H01M10/42H01M10/48H02J7/00G01R31/3842
CPCG01R31/367G01R31/392H01M10/42H01M10/48H02J7/00Y02E60/10H01M10/0525H01M10/0562H01M2220/20H01M2010/4271G01R31/382B60L3/0046B60L2240/547B60L2240/549B60L58/10B60L2260/46B60L2260/48Y02T10/70G01R31/3842G01R31/378G01R31/3648
Inventor 高桥圭楠纮慈伊佐敏行千田章裕山内谅栗城和贵田岛亮太
Owner SEMICON ENERGY LAB CO LTD
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