Lecture notes and recordings for ECE5720: Battery Management and Control
To play any of the lecture recording files (below), QuickTime is required. This software may be downloaded (free) from http://www.apple.com/quicktime/download/. Each of the recordings is on the order of 20 to 30 minutes long, and between about 40 and 50 Mb in size. 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 Equivalent-Circuit Methods textbook web site. (Also note that the focus of this course is on algorithms. For a course that has more of a BMS hardware perspective, you may wish to confer Dr. Ania Mitros' BMS Course.)- Course introduction and syllabus. [PDF]
- Battery-Management-System Requirements. [PDF]
- 1.1: Introduction and BMS functionality.
- 1.2: Requirements 1a-c: Sensing.
- 1.3: Requirement 1d: High-voltage contactor control.
- 1.4: Requirements 1e-f: Isolation sensing and thermal control.
- 1.5: Requirements 2 and 3: Protection and interface.
- 1.6: Requirement 4a: State-of-charge estimation.
- 1.7: Requirement 4b: Energy and power estimation.
- Simulating Battery Packs. [PDF]
- 2.1: Modeling approach #1: Equivalent-circuit models.
- 2.2: Modeling approach #2: Physics-based.
- 2.3: Simulating an electric vehicle.
- 2.4: Equations for vehicle dynamics.
- 2.5: Vehicle range calculations, example.
- 2.6: Simulating constant power and voltage.
- 2.7: Simulating battery packs.
- Battery State Estimation. [PDF]
- 3.1: Preliminary definitions.
- 3.2: Some approaches to estimate state of charge.
- 3.3: Review of probability.
- 3.4: Overview of vector random (stochastic) processes.
- 3.5: Sequential-probabilistic-inference solution.
- 3.6: The six-step process.
- 3.7: Deriving the linear Kalman filter.
- 3.8: Visualizing the Kalman filter.
- 3.9: MATLAB code for the Kalman filter steps.
- 3.10: Practical considerations.
- 3.11: The extended Kalman filter (EKF).
- 3.12: An EKF example, with code.
- 3.13: Preparing to implement EKF on ESC model.
- 3.14: Implementing EKF on ESC model.
- 3.15: Problems with EKF, improved with sigma-point methods.
- 3.16: The SPKF steps.
- 3.17: An SPKF example, with code.
- 3.18: Implementing SPKF on ESC model.
- 3.19: Real-world issues pertaining to sensors, initialization.
- 3.20: Real-world issues: Speed, solved by “bar-delta” filtering.
- 3.21: Bar-delta filtering using the ESC cell model.
- 3.22: Example of bar-delta, using desktop validation.
- Battery Health Estimation. [PDF]
- 4.1: Introduction.
- 4.2: Lithium-ion aging: Negative electrode.
- 4.3: Lithium-ion aging: Positive electrode.
- 4.4: Sensitivity of voltage to ESR and total capacity.
- 4.5: A Kalman filter framework for estimating parameters.
- 4.6: EKF for parameter estimation.
- 4.7: Simultaneous state and parameter estimation.
- 4.8: Robustness and speed.
- 4.9: The problem with least-squares capacity estimates.
- 4.10: Derivation of weighted ordinary least squares.
- 4.11: Derivation of weighted total least squares.
- 4.12: Goodness of the model fit and confidence intervals.
- 4.13: Simplified method with proportional confidence on xi and yi.
- 4.14: Approximate full solution: Cost function.
- 4.15: Approximate full solution: Derivation.
- 4.16: Example simulations, HEV cases.
- 4.17: Example simulations, BEV cases.
- 4.18: Discussion of simulations.
- Cell Balancing. [PDF]
- 5.1: Causes (and not causes) of imbalance.
- 5.2: Design choices when implementing balancing.
- 5.3: Circuits for balancing (1): Passive.
- 5.4: Circuits for balancing (2): Active, capacitive.
- 5.5: Circuits for balancing (3): Active, inductive and dc-dc.
- 5.6: How quickly must I balance a pack?.
- 5.7: Some results of balancing simulations.
- Voltage-Based Power-Limit Estimation. [PDF]
- 6.1: Problem definition.
- 6.2: Voltage-based rate limits, using simple cell model.
- 6.3: Voltage-based rate limits, using comprehensive cell model.
- 6.4: Bisection search.
- 6.5: Power-limits estimation example.
- Physics-Based Optimal Controls. [PDF]
- 7.1: Degradation as basis for power limits.
- 7.2: Full-order model of SEI formation and growth.
- 7.3: Simplifying the model.
- 7.4: Simplifying the calculation.
- 7.5: Comparing the models.
- 7.6: Lithium deposition on overcharge.
- 7.7: Simulation and results.
- 7.8: Optimized controls for power estimation.
- 7.9: Plug-in charging.
- 7.10: Fast-charge example.
- 7.11: Dynamic power calculation.
© University of Colorado Colorado Springs
1420 Austin Bluffs Pkwy, Colorado Springs, CO USA 80918
719-255-8227 (UCCS), 800-990-8227
1420 Austin Bluffs Pkwy, Colorado Springs, CO USA 80918
719-255-8227 (UCCS), 800-990-8227
