• Title/Summary/Keyword: SOH

Search Result 1,204, Processing Time 0.024 seconds

Artificial Neural Network based SOH prediction of lithium-ion battery (ANN을 이용한 리튬이온 배터리의 SOH 예측기법 연구)

  • Kwon, Sanguk;Han, Dongho;Kim, Jonghoon
    • Proceedings of the KIPE Conference
    • /
    • 2018.11a
    • /
    • pp.133-134
    • /
    • 2018
  • 배터리의 효율적인 사용을 위해 배터리 관리 시스템(BMS)는 중요하다. 그 중 배터리의 잔존 수명을 나타내는 지표인 SOH(State of Health)를 예측하기 위해 본 논문에서는 18650 리튬이온 셀에 전기적 노화 실험(Cycle Life Test)을 적용하였다. 방전 용량 및 저항 변화에 의한 SOH 변화를 인공 신경망(Artificial Neural Network)을 사용하여 예측하도록 설계하고 이에 대한 검증을 수행하였다.

  • PDF

A Study on the Diagnosis of Secondary Battery by Phase Response (위상응답에 의한 이차전지의 진단에 관한 연구)

  • Park, Seung-Gon;Kang, Dea-Soo
    • The Journal of the Institute of Internet, Broadcasting and Communication
    • /
    • v.19 no.4
    • /
    • pp.99-104
    • /
    • 2019
  • It was proposed the use of an inducive element to measure the SOH of a secondary battery by phase response. As a result of simulating the Randles equivalent model of a secondary battery, the inductive element used as the load has a high response characteristic and increases the maximum phase response frequency band. In order to obtain the frequency band in which the phase response characteristic of the secondary battery is well observed, the phase response was measured with the change of the inductance value of the inductive element, 33uH,49mohm inductive element with the maximum phase response at 631Hz was used. The phase response measurements for secondary battery with different SOH showed that the phase response for each 20% of SOH showed a difference of about 3.8(degree), enabling the SOH diagnosis of secondary battery by the phase measurement for the inductive element.

A Novel Battery State of Health Estimation Method Based on Outlier Detection Algorithm

  • Piao, Chang-hao;Hu, Zi-hao;Su, Ling;Zhao, Jian-fei
    • Journal of Electrical Engineering and Technology
    • /
    • v.11 no.6
    • /
    • pp.1802-1811
    • /
    • 2016
  • A novel battery SOH estimation algorithm based on outlier detection has been presented. The Battery state of health (SOH) is one of the most important parameters that describes the usability state of the power battery system. Firstly, a battery system model with lifetime fading characteristic was established, and the battery characteristic parameters were acquired from the lifetime fading process. Then, the outlier detection method based on angular distribution was used to identify the outliers among the battery behaviors. Lastly, the functional relationship between battery SOH and the outlier distribution was obtained by polynomial fitting method. The experimental results show that the algorithm can identify the outliers accurately, and the absolute error between the SOH estimation value and true value is less than 3%.

Discrete Wavelet Transform-based SOH Prediction using the Voltage Deviation among the Cells of Li-Ion Battery Pack (배터리 팩의 셀간 전압편차를 이용한 이산 웨이블릿 변환(DWT) 기반 SOH 예측방법)

  • Kim, J.H.;Kim, W.J.;Park, J.H.;Park, J.P.
    • Proceedings of the KIPE Conference
    • /
    • 2012.11a
    • /
    • pp.149-150
    • /
    • 2012
  • 본 논문에서는 배터리 팩을 구성하는 셀간의 전압편차를 이용한 이산 웨이블릿 변환(DWT;discrete wavelet transform) 기반 SOH(State-of-health) 예측방법을 소개한다. 충방전 전압은 DWT의 다해상도 분석(MRA;multi-resolution analysis)을 이용한 시간-주파수 분석을 통해 고주파 전압 성분(detail;$D_n$)과 저주파 전압 성분(approximation;$A_n$)으로 추가 분해되어 SOH 예측을 위한 추가정보를 제공한다. 각 성분의 통계처리(표준편차)를 통해 노화 이전과 이후의 성분값을 비교한다. 즉 프레시 배터리팩과 노화된 팩의 표준편차 기반 셀간 불균형을 서로 비교하여 SOH 예측이 가능하다.

  • PDF

Diagnosis of State Of Health(SOH) for Battery Management System(BMS) (축전지관리시스템(BMS)을 위한 건강상태(SOH) 진단방법)

  • Song Jin-Wan;Kim Hyo-Sung;Lee Ben
    • Proceedings of the KIPE Conference
    • /
    • 2006.06a
    • /
    • pp.266-269
    • /
    • 2006
  • 현대사회에서 축전지라 불리우는 2차 전지는 그 용도가 중요하지만, 비선형적이고 다양한 파라메타에 따른 복잡한 특성 때문에 그 사용법에 있어서 발전에 제한을 받아왔다 [1][2]. 각 배터리셀의 건강상태(SOH)를 실시간으로 정확히 파악하는 것은 장비의 안정된 운전과 원활한 축전지관리를 위하여 필수적이다. 본 논문에서는 축전지의 내부컨덕턴스를 측정하는 간접적인 방법에 의하여 장비의 운전이나 축전지의 수명에 영향을 주지 않고 축전지의 건강상태(SOH)를 실시간으로 진단하는 방법을 제시하고, 실제로 120개의 축전지에 대한 컨덕턴스 자료에 의하여 건강상태를 진단하고 교체시기를 판단한다.

  • PDF

Online State-of-health(SOH) estimation for a LiMn2O4 cell based on fuzzy-logic

  • Kim, Jonghoon;Nikitenkov, Dmitry;Park, Jungpil
    • Proceedings of the KIPE Conference
    • /
    • 2013.07a
    • /
    • pp.447-448
    • /
    • 2013
  • This paper investigates a new approach based on the fuzzy-logic controlled methodology that is suitable for analyzing and evaluating large format $LiMn_2O_4$ cell performance via online state-of-health (SOH) estimation for energy storage system (ESS) applications. First of all, the values of the cell resistance R and maximum cell capacity $Q_{max}$ are calculated from three factors such as voltage, current, and time that were measured by discharging/charging sequence. Then, using two values R and $Q_{max}$ previously calculated, present SOH of an arbitrary $LiMn_2O_4$ cell can be estimated using the defined fuzzy-logic inference system. The main advantage of this approach is wide parameters tuning possibility for good correspondence of SOH decay with other accurate estimation method and the possibility to perform suitable online SOH estimation.

  • PDF

Study on analysis of SOH estimation tendency according to C-rate of Li-ion battery using DEKF (이중 확장 칼만 필터를 활용한 리튬이온 배터리의 C-rate별 노화에 따른 SOH 추정 경향성 분석 연구)

  • Kim, Gun-Woo;Park, Jin-Hyung;Kim, Min-O;Kim, Jong-Hoon
    • Proceedings of the KIPE Conference
    • /
    • 2019.11a
    • /
    • pp.194-195
    • /
    • 2019
  • 배터리는 사용 기간과 회수가 증가함에 따라 수명이 점차 감소한다. SOH(State-Of-Health)는 배터리의 초기 상태와 현재 상태를 비교하여 배터리의 수명 상태를 나타내는 지표이며, 이는 배터리를 사용함에 있어서배터리의 현재 충전상태를 나타내는 SOC(State-Of-Charge)와 함께 정확한 추정을 필요로 한다. 본 논문에서는 리튬이온 배터리를 C-rate에 따라 노화시키며 각 C-rate별 SOH 추정 경향성을 분석하였다. 배터리의 SOC와 SOH는 확장 칼만 필터를 병렬적으로 사용하는 이중 확장 칼만 필터를 활용하여 추정한다. 배터리의 노화실험은 완전충전과 완전충전을 반복하는 전류 프로파일을 인가하였으며, 실험은 상온(25℃)에서 실행하였다.

  • PDF

Aging Process and SOH Estimation of Li-ion Battery (Li-ion 배터리의 열화 과정 및 SOH 판별방법)

  • Park, Ilkyu;Kong, Seil;Cha, Hanju
    • Proceedings of the KIPE Conference
    • /
    • 2012.07a
    • /
    • pp.552-553
    • /
    • 2012
  • 본 논문에서는 리튬 이온(Li-ion) 배터리의 열화과정 및 SOH(State-of-Health, 잔존수명) 판별방법에 대해 분석하였다. 리튬 이온 배터리의 SOH는 충/방전 주기의 횟수와 방법 및 전류에 따라 달라지며, 특히 온도에 따라 임피던스가 민감하게 변화하며, 그 과정에서 OCV(Open Circuit Voltage, 개방전압)가 변하게 된다. 따라서 온도변화와 배터리의 충/방전 과정에서 변화하는 임피던스의 특성과 그에 따른 OCV 변화를 고려하여 SOH 판별하는 방법과 리튬 이온 배터리의 열화 과정을 분석하여 소개한다.

  • PDF

EKF Based SOH State Estimation Algorithm for UAV Li-Po Battery Pack (무인항공기 리튬폴리머 배터리팩용 EKF 기반 SOH 상태추정 알고리즘)

  • Jung, Sunghun
    • Journal of the Korea Convergence Society
    • /
    • v.8 no.6
    • /
    • pp.237-243
    • /
    • 2017
  • Ignorance of battery pack life could bring unexpected UAV crashes and so the SOH estimation became a next important factor to the SOC estimation. In contrast to the EV applications, the small UAV could not carry heavy and complex BMS and so it is required to apply a simple, light, cheap, but powerful BMS to prevent any accident. In this paper, we show two SOH estimation methods, using internal resistance and using $SOC_I$ and $SOC_V$ with CF. Results show that the SOH becomes about 92% after 30 number of discharging cycles.

EV Battery State Estimation using Real-time Driving Data from Various Routes (전기차 주행 데이터에 의한 경로별 배터리 상태 추정)

  • Yang, Seungmoo;Kim, Dong-Wan;Kim, Eel-Hwan
    • The Transactions of the Korean Institute of Power Electronics
    • /
    • v.24 no.3
    • /
    • pp.139-146
    • /
    • 2019
  • As the number of electric vehicles (EVs) in Jejudo Island increases, the secondary use of EV batteries is becoming increasingly mandatory not only in reducing greenhouse gas emissions but also in promoting resource conservation. For the secondary use of EV batteries, their capacity and performance at the end of automotive service should be evaluated properly. In this study, the battery state information from the on-board diagnostics or OBD2 port was acquired in real time while driving three distinct routes in Jejudo Island, and then the battery operating characteristics were assessed with the driving routes. The route with higher altitude led to higher current output, i.e., higher C-rate, which would reportedly deteriorate state of health (SOH) faster. In addition, the SOH obtained from the battery management system (BMS) of a 2017 Kia Soul EV with a mileage of 55,000 km was 100.2%, which was unexpectedly high. This finding was confirmed by the SOH estimation based on the ratio of the current integral to the change in state of charge. The SOH larger than 100% can be attributed to the rated capacity that was lower than the nominal capacity in EV application. Therefore, considering the driving environment and understanding the SOH estimation process will be beneficial and necessary in evaluating the capacity and performance of retired batteries for post-vehicle applications.