**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 x
_{i}and y_{i}. - 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.