# Learning Model Predictive Control Parameters via Bayesian Optimization for Battery Fast Charging

arXiv: https://arxiv.org/abs/2404.06125 · HTML: https://mlsanity.com/paper/2404.06125/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 9 Apr 2024.

## Builds on (most cited first, 5 of 5)

- [Safe Reinforcement Learning Using Robust MPC](https://mlsanity.com/paper/1906.04005/) (2019)
- [A Data-Driven Automatic Tuning Method for MPC under Uncertainty using Constrained Bayesian Optimization](https://mlsanity.com/paper/2011.11841/) (2020)
- [Machine Learning Benchmarks for the Classification of Equivalent Circuit Models from Electrochemical Impedance Spectra](https://mlsanity.com/paper/2302.03362/) (2023)
- [Interpretation of High-Dimensional Linear Regression: Effects of Nullspace and Regularization Demonstrated on Battery Data](https://mlsanity.com/paper/2309.00564/) (2023)
- [Cycle Life Prediction for Lithium-ion Batteries: Machine Learning and More](https://mlsanity.com/paper/2404.04049/) (2024)

## Built on by (most cited first, 2 of 2)

- [Safe and Stable Closed-Loop Learning for Neural-Network-Supported Model Predictive Control](https://mlsanity.com/paper/2409.10171/) (2024)
- [Safe Learning-Based Optimization of Model Predictive Control: Application to Battery Fast-Charging](https://mlsanity.com/paper/2410.04982/) (2024)

## Abstract (arXiv)

Tuning parameters in model predictive control (MPC) presents significant challenges, particularly when there is a notable discrepancy between the controller's predictions and the actual behavior of the closed-loop plant. This mismatch may stem from factors like substantial model-plant differences, limited prediction horizons that do not cover the entire time of interest, or unforeseen system disturbances. Such mismatches can jeopardize both performance and safety, including constraint satisfaction. Traditional methods address this issue by modifying the finite horizon cost function to better reflect the overall operational cost, learning parts of the prediction model from data, or implementing robust MPC strategies, which might be either computationally intensive or overly cautious. As an alternative, directly optimizing or learning the controller parameters to enhance closed-loop performance has been proposed. We apply Bayesian optimization for efficient learning of unknown model parameters and parameterized constraint backoff terms, aiming to improve closed-loop performance of battery fast charging. This approach establishes a hierarchical control framework where Bayesian optimization directly fine-tunes closed-loop behavior towards a global and long-term objective, while MPC handles lower-level, short-term control tasks. For lithium-ion battery fast charging, we show that the learning approach not only ensures safe operation but also maximizes closed-loop performance. This includes maintaining the battery's operation below its maximum terminal voltage and reducing charging times, all achieved using a standard nominal MPC model with a short horizon and notable initial model-plant mismatch.