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Pulse Perturbation for Battery Management
Pulse Perturbation for Battery Management
Pulse Perturbation for Battery Management

상세정보

자료유형  
 학위논문 서양
최종처리일시  
20250211151114
ISBN  
9798382770680
DDC  
621.3
저자명  
Li, Alan Gen.
서명/저자  
Pulse Perturbation for Battery Management
발행사항  
[Sl] : Columbia University, 2024
발행사항  
Ann Arbor : ProQuest Dissertations & Theses, 2024
형태사항  
129 p
주기사항  
Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
주기사항  
Advisor: Preindl, Matthias.
학위논문주기  
Thesis (Ph.D.)--Columbia University, 2024.
초록/해제  
요약Lithium-ion battery responses to bipolar pulse perturbations of less than two minute duration and one C-rate amplitude are studied as general-purpose diagnostics signals that encode the cell impedance, remaining charge, and degradation level. It is shown that the information is derived from a combination of the linear and nonlinear system dynamics of the electrochemical overpotentials, open-circuit voltage change, and hysteresis of the cell. Experimental data is analyzed using an equivalent circuit composed of a conventional resistor-capacitor pair model, a square-root-order convolution-defined diffusion element, and a piece-wise-linear open-circuit voltage element. This bipolar pulse model disaggregates the battery voltage response into its constituent dynamics and allows the nonlinearities to be isolated. The nonlinearities are crucial features which allow the battery charge, health, and incremental capacity features to be regressed directly from the pulse voltage response using ridge regression and feedforward neural networks. Assessment of different pulse shapes suggests that the diagnostics power of the pulse may increase with higher amplitude and shorter duration. Real-world applications are then investigated, including the estimation of charge imbalance using the series-module pulse response, and state-space formulation of the convolution-defined diffusion element.Further refinement of the pulse techniques could simplify battery diagnostics by providing, from a single pulse diagnostic, the key states of charge, health, and power necessary to operate a reliable system.
일반주제명  
Electrical engineering
일반주제명  
Chemistry
일반주제명  
Energy
일반주제명  
Applied physics
키워드  
Lithium-ion battery
키워드  
Cell impedance
키워드  
Bipolar pulse model
키워드  
Battery voltage response
기타저자  
Columbia University Electrical Engineering
기본자료저록  
Dissertations Abstracts International. 85-11B.
전자적 위치 및 접속  
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■035    ▼a(MiAaPQ)AAI31145054
■040    ▼aMiAaPQ▼cMiAaPQ
■0820  ▼a621.3
■1001  ▼aLi,  Alan  Gen.
■24510▼aPulse  Perturbation  for  Battery  Management
■260    ▼a[Sl]▼bColumbia  University▼c2024
■260  1▼aAnn  Arbor▼bProQuest  Dissertations  &  Theses▼c2024
■300    ▼a129  p
■500    ▼aSource:  Dissertations  Abstracts  International,  Volume:  85-11,  Section:  B.
■500    ▼aAdvisor:  Preindl,  Matthias.
■5021  ▼aThesis  (Ph.D.)--Columbia  University,  2024.
■520    ▼aLithium-ion  battery  responses  to  bipolar  pulse  perturbations  of  less  than  two  minute  duration  and  one  C-rate  amplitude  are  studied  as  general-purpose  diagnostics  signals  that  encode  the  cell  impedance,  remaining  charge,  and  degradation  level.  It  is  shown  that  the  information  is  derived  from  a  combination  of  the  linear  and  nonlinear  system  dynamics  of  the  electrochemical  overpotentials,  open-circuit  voltage  change,  and  hysteresis  of  the  cell.  Experimental  data  is  analyzed  using  an  equivalent  circuit  composed  of  a  conventional  resistor-capacitor  pair  model,  a  square-root-order  convolution-defined  diffusion  element,  and  a  piece-wise-linear  open-circuit  voltage  element.  This  bipolar  pulse  model  disaggregates  the  battery  voltage  response  into  its  constituent  dynamics  and  allows  the  nonlinearities  to  be  isolated.  The  nonlinearities  are  crucial  features  which  allow  the  battery  charge,  health,  and  incremental  capacity  features  to  be  regressed  directly  from  the  pulse  voltage  response  using  ridge  regression  and  feedforward  neural  networks.  Assessment  of  different  pulse  shapes  suggests  that  the  diagnostics  power  of  the  pulse  may  increase  with  higher  amplitude  and  shorter  duration.  Real-world  applications  are  then  investigated,  including  the  estimation  of  charge  imbalance  using  the  series-module  pulse  response,  and  state-space  formulation  of  the  convolution-defined  diffusion  element.Further  refinement  of  the  pulse  techniques  could  simplify  battery  diagnostics  by  providing,  from  a  single  pulse  diagnostic,  the  key  states  of  charge,  health,  and  power  necessary  to  operate  a  reliable  system.
■590    ▼aSchool  code:  0054.
■650  4▼aElectrical  engineering
■650  4▼aChemistry
■650  4▼aEnergy
■650  4▼aApplied  physics
■653    ▼aLithium-ion  battery
■653    ▼aCell  impedance
■653    ▼aBipolar  pulse  model
■653    ▼aBattery  voltage  response
■690    ▼a0544
■690    ▼a0485
■690    ▼a0791
■690    ▼a0215
■71020▼aColumbia  University▼bElectrical  Engineering.
■7730  ▼tDissertations  Abstracts  International▼g85-11B.
■790    ▼a0054
■791    ▼aPh.D.
■792    ▼a2024
■793    ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160769▼nKERIS▼z이  자료의  원문은  한국교육학술정보원에서  제공합니다.

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