Battery management systems. Volume III, Physics-based methods / Gregory L. Plett, M. Scott Trimboli.

Format:

Book

Author/Creator:

Plett, Gregory L., author.

Trimboli, M. Scott, author.

Language:

English

Subjects (All):

Electric batteries.

Physical Description:

1 online resource (397 pages)

Place of Publication:

Norwood, MA : Artech House, [2024]

Summary:

This book explores the intricacies of battery management systems with a focus on physics and methods. It is an advanced technical resource aimed at professionals and researchers in the field of energy systems, providing in-depth analysis and methodologies for optimizing battery performance and longevity. The text delves into the theoretical foundations of battery technology, offering insights into the latest innovations and applications, especially in the context of sustainable energy solutions. It serves as a significant reference for those involved in the design and management of battery systems, providing practical guidance alongside comprehensive theoretical discussions. Generated by AI.

Contents:

Intro

Battery Management Systems ,Volume III, Physics-Based Methods

Contents

Preface

1 Redundant Parameter Elimination

1.1 Background topics and a roadmap to this book

1.2 Lithium-ion cell models

1.3 Review of DFN model

1.4 Reducing number of parameters: method

1.4.1 Intensive versus extensive quantities

1.4.2 How are we going to do this?

1.5 Reducing number of parameters: application

1.6 Summary of reformulated model equations

1.7 Recovering original electrochemical variables

1.8 Where to from here?

1.A Summary of variables

1.A.1 Notation conventions for variables and parameters

1.A.2 Variables in the standard DFN model reviewed in this chapter

1.A.3 Variables and parameters used only in derivations in this chapter, but not thereafter

2 Modeling Electrochemical Impedance

2.1 MSMR model

2.2 Electrical double layer

2.3 Ideal interface impedance

2.3.1 Impedance at particle/film interface

2.3.2 Impedance at film/electrolyte interface

2.3.3 Overall interfacial impedance model

2.4 Adding double-layer constant-phase-element behavior

2.4.1 Modified CPE for double layer

2.5 Adding solid-diffusion CPE behavior

2.6 SOC-dependent solid diffusivity

2.7 Summary of nonideal interfacial model

2.8 Summary of modified model PDEs

2.9 Deriving TFs for all electrochemical variables

2.9.1 Finding the form of the eQre( ˜ x, s)/Iapp(s) TF

2.9.2 Solving for coefficients in eQre( ˜ x, s)/Iapp(s)

2.9.3 Interfacial lithium-flux TF

2.9.5 Nonfaradaic interfacial lithium-flux TF

2.9.6 Phase-potential-difference TF

2.9.7 Solid-surface-concentration TF

2.9.8 Solid-potential TF

2.9.9 Electrolyte potential TF

2.10 Frequency-response example

2.11 Full-cell impedance

2.12 Nyquist (Cole-Cole) plots

2.13 MATLAB toolbox

2.14 Where to from here?.

2.A Summary of variables

2.B NMC30-cell parameters

2.C Closed-form solution for TF cr k(s) functions

2.D Transfer-function limits

2.D.1 Integrator residues

2.D.2 Low-frequency gains

2.D.3 High-frequency gains

3 Model Parameter Estimation

3.1 Teardown versus nonteardown approaches

3.1.1 Parameter estimation via cell teardown

3.1.2 Parameter estimation without cell teardown

3.1.3 A balanced approach

3.2 OCP testing

3.2.1 Half-cell testing to determine electrode OCP relationship

3.2.2 Initial data processing

3.2.3 Overcoming three data-processing problems

3.3 Estimating OCP

3.3.1 Voltage averaging (method 1)

3.3.2 SOC averaging (method 2)

3.3.3 Diagonal interpolation (method 3)

3.3.4 Step 2: Convert relative to absolute relationships forMethods 1-3

3.3.5 Direct MSMR model fit (method 4)

3.4 Validating OCP

3.4.1 Validation of methods using simulation

3.4.2 Application of methods to physical half cells

3.4.3 Temperature dependence of OCP

3.5 OCV testing

3.5.1 Determining cell OCV versus SOC

3.5.2 Estimating boundaries

3.6 Validating boundaries

3.6.1 Estimating operating boundaries using simulation data

3.6.2 Estimating operating boundaries using laboratory data

3.7 Estimating OCP without requiring cell teardown

3.8 Validating nonteardown OCP estimation

3.8.1 Nonteardown application to simulation data

3.8.2 Nonteardown application to laboratory cell data

3.9 Discharge testing

3.10 Calibrating OCP

3.10.1 Rederiving SPM equations to calibrate OCP

3.10.2 Converting the SPM to biased stoichiometry

3.10.3 Setting up the SPM regression

3.10.4 Resolving R0 when regressing parameter values

3.10.5 Evaluating the SPM with ideal parameter values

3.11 Validating calibration

3.11.1 Simulation results and strategy.

3.11.2 Application to physical cell

3.11.3 Application to Panasonic-cell data using teardown OCP

3.11.4 Application to Panasonic-cell data using nonteardown OCP

3.12 Pulse-resistance testing

3.12.1 Determine ˜f n s-e( ˜ x = 0, t = 0+) and ˜f p s-e( ˜ x = 3, t = 0+)

3.12.2 Determine ˜fpe ( ˜ x = 3, t = 0+) and Dv

3.12.3 Summary of the pulse-resistance calculation

3.13 Estimating pulse-resistance-test parameters

3.13.1 Challenges when estimating R0

3.13.2 Development of a practical pulse-resistance-test model

3.13.3 Implementation of a pulse-resistance-test data-processing method

3.14 Validating pulse-resistance-test parameters

3.14.1 Estimating cell-model parameter values using synthetic data

3.14.2 Application to a physical cell

3.14.3 Implementation of data-processing method: Panasonic cell

3.14.4 Parameter estimation using lab bR0 data

3.15 Frequency-response (EIS) testing

3.15.1 Distribution of relaxation times (DRT)

3.15.2 Decomposing full-cell impedance

3.15.3 Understanding the interface model components

3.15.4 Initializing specific model parameter estimates

3.15.5 Preprocessing laboratory EIS data

3.16 Validating frequency-response-test parameters

3.16.1 Application to a simulated cell

3.16.2 Application to a physical cell

3.16.3 Estimating LPM parameter values using laboratory data

3.17 Pseudo-steady-state (PSS) testing to find ȳ

3.17.1 PSS electrolyte approximation

3.17.2 PSS average solid-surface approximation

3.17.3 Kinetics inversion

3.17.4 Cell voltage

3.17.5 Summary of the PSS-ROM

3.18 Validating the ȳ estimate

3.19 Temperature dependence

3.20 MATLAB toolbox

3.21 Where to from here?

3.A Summary of variables

3.B SPM derivation

3.C Finding ˜f p e (3, 0+) from ˜f rs -e( ˜ x, 0+)

3.C.1 Negative electrode.

3.C.3 Positive electrode

3.C.4 The double integral of irf+dl( ˜ x, t)

3.D Initializing estimates

3.D.1 Lumped double-layer capacitance C̄ rd l

3.D.2 Lumped resistances R̄r dl and R̄

3.D.3 Lumped conductances ̄kr and s̄r

3.D.4 Lumped ionic conductivity coefficient ̄kD

3.D.5 Electrolyte transport ratio

3.D.6 MSMR interface kinetics parameters aj and k̄0,j

3.D.7 Electrolyte lithium content ̄qe

3.D.8 Lumped solid diffusivity D̄s,ref

3.D.9 CPE exponents nf and ndl

3.E GDRT algorithm

4 Efficient Time-Domain Simulation

4.1 Convert continuous- to discrete-time frequency response

4.2 Illustrating frequency-response conversion

4.3 The hybrid realization algorithm (HRA)

4.3.1 Summarizing the method to determine A using the HRA

4.4 Final form of A, B, C, and D

4.4.1 Solving for the state-space B matrix

4.4.2 Solving for the state-space C matrix

4.4.3 Solving for the state-space D matrix

4.4.4 Handling integration dynamics

4.5 Sample HRA results

4.6 Simulating a cell in the time domain, near a setpoint

4.6.1 Update cell state of charge z [k]

4.6.2 Interfacial lithium fluxes

4.6.3 Solid surface stoichiometry

4.6.4 Phase potential difference

4.6.5 Potential in solid

4.6.8 Cell voltage

4.6.9 A short summary

4.7 Simulation results near a ROM setpoint

4.8 Simulating a cell over a wide operating range

4.9 Simulation results over a wide operating range

4.9.1 Constant-current discharge profile

4.9.2 Dynamic current profile

4.10 Simulating constant voltage and constant power

4.10.1 Simulating constant voltage

4.10.2 Simulating constant power

4.11 Simulating battery packs

4.11.1 Series-connected cells

4.11.2 Logically modular battery packs

4.11.3 Simulating PCM-based packs

4.11.4 Simulating SCM-based packs.

4.11.5 Example simulations of PCM-based and SCM-based packs

4.12 MATLAB toolbox

4.13 Where to from here?

4.A Summary of variables

5 Electrochemical Internal Variables Estimation

5.1 Review of sequential probabilistic inference

5.2 The eight-step process

5.3 Setup for xKF with output-blended models

5.4 EKF and SPKF principles

5.4.1 The extended Kalman filter (EKF)

5.4.2 The sigma-point Kalman filter (SPKF)

5.5 EKF with the output-blended model

5.6 SPKF with the output-blended model

5.7 Example of xKF code in operation

5.7.1 Charge-neutral UDDS internal-variable estimation results

5.7.2 C/5 discharge profile internal-variable estimation results

5.7.3 Long-duration UDDS profile internal-variable estimation results

5.8 MATLAB toolbox

5.9 Where to from here?

5.A Summary of variables

5.B EKF derivative matrices

5.B.1 Computing derivatives of cell voltage with respect to states

5.B.2 Computing derivatives of electrochemical variables with respect to states

6 Diagnosis and Prognosisof Degradation

6.1 Degradation indicators

6.1.1 Capacity fade

6.1.2 Power fade

6.2 Diagnosis versus prognosis

6.3 BMS diagnostics

6.4 Changes to OCV, qr0, and qr100 as a diagnostic

6.4.1 Changes to OCV due to LLI

6.4.2 Changes to OCV due to LAM

6.5 Loosely coupled tracking of qrs in both electrodes

6.6 Adapting a model to track cell variations and aging

6.7 Selecting a model that describes present dynamics

6.7.1 Interaction

6.7.2 Filtering

6.7.3 Combination

6.8 BMS prognostics

6.9 Cell 1D thermal model

6.9.1 Converting thermal model into lumped-parameter form

6.9.2 Physics-based reduced-order model

6.9.3 Deriving the gradient TFs

6.9.4 Simulation results and discussion

6.10 Cell degradation models.

6.10.1 Full-order model of SEI layer growth.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

Description based on print version record.

ISBN:

9781630819057

1630819050

OCLC:

1427665264