Lecture notes and recordings for ECE5730: Physics-Based Battery Management Systems Algorithms
If you are not registered at UCCS to take this course for credit, and if you wish to do so, please contact Dr. Gregory Plett using the information provided in the Section 0 notes. This course can be taken at the graduate level as part of the Masters of Science in Electrical Engineering option in Battery Controls. See the IDEATE web site for more details. For textbook information, please see the Physics-Based Methods textbook web site.- Course introduction and syllabus. [PDF]
- Redundant Parameter Elimination. [PDF]
- 1.1: Introduction to the course.
- 1.2: The Doyle-Fuller-Newman P2D model.
- 1.3: Reducing the number of model parameers: method.
- 1.4: Reducing the number of model parameters: solid.
- 1.5: Reducing the number of model parameters: electrolyte, kinetics.
- 1.6: Summary of reformulated PDEs.
- 1.7: Recovering the original electrochemical variables.
- 1.app: Chapter 1 appendices.
- Modeling Electrochemical Impedance. [PDF]
- 2.1: More detail required at the solid/electrolyte interface.
- 2.2: Upgrade 1: Multi-species-multi-reaction (MSMR) model.
- 2.3: Upgrade 2: Electrical double layer.
- 2.4: Ideal interfacial impedance model.
- 2.5: Upgrade 3: Adding double-layer CPE behavior.
- 2.6: Upgrades 4&5: Adding solid-diffusion CPE, SOC dependence.
- 2.7: Seeking full-cell impedance model.
- 2.8: Full cell impedance response.
- 2.9: Nyquist (Cole-Cole) plots.
- 2.10: MATLAB toolbox.
- 2.app: Chapter 2 appendices.
- Model Parameter Estimation. [PDF]
- 3.1: Teardown versus nonteardown approaches.
- 3.2: OCP testing and data analysis on a half cell.
- 3.3: Converting dis/charge data to OCP estimate.
- 3.4: Examples of estimating OCP.
- 3.5: OCV testing to find electrode operating windows.
- 3.6: Discharge testing to calibrate positive-electrode OCP.
- 3.7: Converting SPM to biased stoichiometry.
- 3.8: Setting up the SPM regression; results.
- 3.9: Pulse-resistance testing.
- 3.10: Solving for pulse-test math factors.
- 3.11: Estimating cell-model parameter values using synthetic data.
- 3.12: Application to a physical cell.
- 3.13: Frequency-response testing: concept and complications.
- 3.14: Frequency-response testing: Results.
- 3.15: Pseudo-steady-state testing.
- 3.16: MATLAB toolbox.
- 3.app: Chapter 3 appendices.
- Efficient Time-Domain Simulation. [PDF]
- 4.1: Introduction and context.
- 4.2: Convert continuous-time to discrete-time frequency response.
- 4.3: The hybrid realization algorithm (HRA).
- 4.4: Final form of A, B, C, and D.
- 4.5: Simulating a (single) cell in the time domain, near a setpoint.
- 4.6: Simulation results near a ROM setpoint.
- 4.7: Simulating over a wide operating range (output blending).
- 4.8: Simulation results over wide operating range.
- 4.9: Simulating constant voltage and constant power.
- 4.10: Simulating battery packs.
- 4.11: MATLAB toolbox.
- 4.app: Chapter 4 appendices.
- Electrochemical Internal Variables Estimation. [PDF]
- 5.1: Introduction.
- 5.2: Review of sequential probabilistic inference.
- 5.3: The eight-step process.
- 5.4: Setup for xKF with output-blended models.
- 5.5: Principles of EKF and SPKF.
- 5.6: EKF with the output-blended model.
- 5.7: SPKF with the output-blended model..
- 5.8: Example of xKF code in operation.
- 5.9: MATLAB toolbox.
- 5.app: Chapter 5 appendices.
- Diagnosis and Prognosis of Degradation. [PDF]
- 6.1: Introduction.
- 6.2: Degradation indicators, modes, mechanisms.
- 6.3: Diagnosis versus prognosis.
- 6.4: Changes to OCV, θ0, and θ100 as a diagnostic.
- 6.5: Loosely coupled tracking of θs in both electrodes.
- 6.6: Adapting a model to track cell variations and aging.
- 6.7: Tracking cell variations and aging via selective approaches.
- 6.8: BMS prognostics and cell 1D thermal model.
- 6.9: Physics-based reduced-order thermal simulations.
- 6.10: Cell degradation models.
- 6.app: Chapter 6 appendices.
- Optimal Fast Charge. [PDF]
- 7.1: Fast-charge control problem.
- 7.2: Fast-charge limitations.
- 7.3: Model-predictive control.
- 7.4: MPC: The classic algorithm.
- 7.5: Optimal MPC solution: Unconstrained case.
- 7.6: Optimal MPC solution: Constrained case.
- 7.7: MPC fast charge of lithium-ion cells.
- 7.8: MPC implementation.
- 7.9: Example of MPC-EKF fast charge.
- 7.10: Experimental validation.
- 7.11 and 7.app: MATLAB toolbox and chapter appendices.
- Computing Dynamic Power Limits. [PDF]
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1420 Austin Bluffs Pkwy, Colorado Springs, CO USA 80918
719-255-8227 (UCCS), 800-990-8227
