Abormality detection device for secondary battery, abnormality detection method and program

A secondary battery and abnormality detection technology, applied in the fields of machines, vehicles using neural networks, abnormality detection systems for power storage devices, processes, products or compositions, and state estimation devices, can solve short circuit and temperature deterioration of secondary batteries And other issues

Pending Publication Date: 2020-08-14
SEMICON ENERGY LAB CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

It also depends on the user's usage, and the temperature during charging, the frequency of high-speed charging, the charging amount using regenerative braking, the timing of charging using regenerative braking, etc. may also be related to deterioration
In addition, secondary batteries used in electric vehicles and hybrid vehicles may experience abnormalities such as short circuits due to deterioration over time.

Method used

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  • Abormality detection device for secondary battery, abnormality detection method and program
  • Abormality detection device for secondary battery, abnormality detection method and program
  • Abormality detection device for secondary battery, abnormality detection method and program

Examples

Experimental program
Comparison scheme
Effect test

Embodiment approach 1

[0107] figure 1 is an example of an equivalent circuit model (also referred to as a battery model) for abnormally detected batteries. figure 1 R0 shown in is composed of series resistors, R1, R2, R3, R4 are resistors. Also, C2, C3 and C4 are capacitors.

[0108] at a certain time interval figure 1 The micro-short-circuit model 400 in the circuit applies a pulse signal to simulate the occurrence of a micro-short circuit to perform numerical simulation of the battery model. In this embodiment, numerical simulation is used for the description, but the voltage of the abnormal battery is actually monitored.

[0109] figure 1 The OCV shown in the figure is the abbreviation of Open Circuit Voltage, which is the potential difference between the positive electrode and the negative electrode when the electrochemical reaction inside the battery is in an equilibrium state after a sufficient time has elapsed after the battery is disconnected from the external circuit.

[0110] F...

Embodiment approach 2

[0116] In the present embodiment, a configuration in which a neural network (NN) is used to improve accuracy will be described below.

[0117] FIG. 3 is a diagram showing an example of a functional block. The difference from Embodiment 1 is that the neural network unit 404 is included. exist Figure 3A Among them, NN is the neural network part, which is the output LO(k).

[0118] Figure 3A The part shown by the symbol 401 in is a part of the difference (voltage difference) between the observed value (voltage) and the voltage estimated using the prior state variable. This voltage difference value is input to the neural network unit 404, and the value is compared with the learning data to determine whether or not there is an abnormality. In addition, by accumulating the data input to the neural network unit 404 as a part of the learning data, the accuracy can be further improved.

[0119] also, Figure 3B is one of the other ways. exist Figure 3B In the functional bloc...

Embodiment approach 3

[0124] In the present embodiment, a configuration in which the accuracy is further improved by using another comparator will be described below.

[0125] When investigating the relationship between the elapse of time and the voltage difference, an error different from the timing of the occurrence of the micro-short circuit was found. The present inventors found that an error occurs when charging is switched in a secondary battery. like Figure 6A As shown, it was confirmed that the terminal current changed with the discharge timing.

[0126] No overshoot on the positive side was observed during charge-discharge switching. On the other hand, if Figure 6B As shown, when a micro-short circuit occurs, overshoot occurs on both the positive side and the negative side. The overshoot on the positive side occurs because the modification of the prediction error is reflected in the next step. Overshoot occurs first on the negative side and then on the positive side. Based on this di...

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Abstract

Provided is a secondary battery control system for detecting abnormalities while predicting, with high precision, other parameters (e.g., internal resistance and SOC). A Kalman filter is used, and anobserved value (voltage) and a pre-state variable are used at certain times to detect differences in estimated voltages. A threshold value voltage is set in advance, and sudden abnormalities, specifically, micro-shorts and the like are detected according to detected differential voltages. In addition, it is preferable to use a neural network to learn the time-series differential voltage data to determine and detect whether voltage differentials are normal or abnormal.

Description

technical field [0001] One aspect of the present invention relates to an article, a method, or a manufacturing method. In addition, 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 method of manufacturing a semiconductor device, a display device, a light emitting device, an electrical storage device, a lighting device, or an electronic device. Furthermore, an aspect of the present invention relates to a charging control method for an electric storage device, a method for estimating the state of an electric storage device, and an abnormality detection method for an electric storage device, and more particularly, to a charging system for an electric storage device, a A state estimation system for an electrical storage device and an abnormality detection system for an electrical storage device. [0002] In addition, in this specification, an ele...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R19/165G01R31/389G01R31/382H01M10/48H02J7/00
CPCH01M10/48H02J7/00Y02E60/10H01M10/0525H01M2300/0065H01M10/425G01R31/392G01R31/367G01R31/3648G01R31/3842G01R19/1659G01R31/374G01R19/10G01R31/52G01R19/16576H02J7/0047B60L58/10G06N20/00G06N3/08
Inventor 高桥圭丰高耕平
Owner SEMICON ENERGY LAB CO LTD
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