KR970071027A - How to measure battery charge of electric vehicle using neural network - Google Patents

How to measure battery charge of electric vehicle using neural network Download PDF

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Publication number
KR970071027A
KR970071027A KR1019960009768A KR19960009768A KR970071027A KR 970071027 A KR970071027 A KR 970071027A KR 1019960009768 A KR1019960009768 A KR 1019960009768A KR 19960009768 A KR19960009768 A KR 19960009768A KR 970071027 A KR970071027 A KR 970071027A
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KR
South Korea
Prior art keywords
battery
neural network
electric vehicle
output
amount
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Application number
KR1019960009768A
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Korean (ko)
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KR100191917B1 (en
Inventor
김천호
Original Assignee
김영귀
기아자동차 주식회사
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Priority to KR1019960009768A priority Critical patent/KR100191917B1/en
Publication of KR970071027A publication Critical patent/KR970071027A/en
Application granted granted Critical
Publication of KR100191917B1 publication Critical patent/KR100191917B1/en

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]

Abstract

본 발명은 뉴랄 네트워크를 이용한 전기자동차의 베터리 충전량 측정방법에 관한 것으로 기존 알고리즘이 방전 전류에 대해 충전량을 계산하는 것과는 달리 파워에 대해서 충전량을 계산하고 Nonlinear 한 요소를 뉴랄 네트워크를 통한 학습으로 구현하여 배터리에서 출력되는 파워의 다이나믹한 변화와 온도변화에도 정확한 충전량을 구해 줄 수 있어 운전자가 언제 배터리가 방전되어 정지할 것인가를 걱정할 필요가 없다.The present invention relates to a method of measuring the battery charge amount of an electric vehicle using a neural network. Unlike the conventional algorithm that calculates the charge amount with respect to the discharge current, the charge amount is calculated for the power, It is not necessary to worry about when the battery will be discharged and stopped when the battery is discharged.

Description

뉴랄 네트워크를 이용한 전기자동차의 베터리 충전량 측정방법How to measure battery charge of electric vehicle using neural network

본 내용은 요부공개 건이므로 전문내용을 수록하지 않았음Since this is a trivial issue, I did not include the contents of the text.

제1도는 본 발명의 뉴랄 네트워크를 이용한 베터리 충전량 측정방법을 도시하는 도면.FIG. 1 is a view showing a method of measuring a battery charge amount using a neural network of the present invention. FIG.

Claims (1)

배터리(1)는 인버터(2)를 통하여 모터(3)에 에너지를 공급하며 배터리(1)에도 온도센서(4)가 부착되어 충전량 알고리즘에 온도신호를 보내주고 충전량 알고리즘은 배터리(1)의 전압과 전류로부터 파워를 검출하여 사용하며 충전량 알고리즘은 2개의 뉴랄 네크워크 NN1 과 NN2로 구성되는 전기장동차에 있어서, NN1은 배터리의 온도와 출력 파워를 입력으로 하고 출력은 배터리의 가능한 총 에너지양을 내며 NN2는 NN1의 입력인 온도와 출력과 더불어 NN1의 출력인 가능 에너지 그리고 현재의 충전량을 입력으로 하고 출력은 DOD(depth of discharge)의 변화량을 내며 이 출력을 적분하면 현재의 DOD가 되고 1에서 빼면 충전량을 얻을 수 있고 이렇게 얻은충전량은 NN2의 입력으로도 들어가게 이루어진 뉴랄 네트워크를 이용한 전기자동차의 배터리 충전량 측정방법.The battery 1 supplies energy to the motor 3 via the inverter 2 and the temperature sensor 4 is also attached to the battery 1 to send a temperature signal to the charge amount algorithm, In the electric field circuit consisting of two neural networks NN1 and NN2, NN1 inputs the battery temperature and output power, the output gives the total possible energy amount of battery, and NN2 Is the input of NN1, together with the temperature and the output, the possible energy of the output of NN1, and the current charge, and the output gives the amount of change in depth of discharge (DOD) And the amount of charge thus obtained is entered into the input of the NN2. ※ 참고사항 : 최초출원 내용에 의하여 공개하는 것임.※ Note: It is disclosed by the contents of the first application.
KR1019960009768A 1996-04-01 1996-04-01 The detecting method of battery charging amount for electric-automobile using neural-network KR100191917B1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
KR1019960009768A KR100191917B1 (en) 1996-04-01 1996-04-01 The detecting method of battery charging amount for electric-automobile using neural-network

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
KR1019960009768A KR100191917B1 (en) 1996-04-01 1996-04-01 The detecting method of battery charging amount for electric-automobile using neural-network

Publications (2)

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KR970071027A true KR970071027A (en) 1997-11-07
KR100191917B1 KR100191917B1 (en) 1999-06-15

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KR1019960009768A KR100191917B1 (en) 1996-04-01 1996-04-01 The detecting method of battery charging amount for electric-automobile using neural-network

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20030092808A (en) * 2002-05-31 2003-12-06 현대자동차주식회사 a method for calculation a battery state of charge in electric vehicle
CN109307852A (en) * 2018-09-06 2019-02-05 中国电力科学研究院有限公司 A kind of method and system of the measurement error of determining electric automobile charging pile electric energy metering device

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR100906908B1 (en) 2006-12-11 2009-07-08 현대자동차주식회사 Method for controlling battery charging of hybrid electric vehicle
KR102065120B1 (en) * 2018-09-27 2020-02-11 경북대학교 산학협력단 Battery charging state estimation method based on neural network

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20030092808A (en) * 2002-05-31 2003-12-06 현대자동차주식회사 a method for calculation a battery state of charge in electric vehicle
CN109307852A (en) * 2018-09-06 2019-02-05 中国电力科学研究院有限公司 A kind of method and system of the measurement error of determining electric automobile charging pile electric energy metering device

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Publication number Publication date
KR100191917B1 (en) 1999-06-15

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