LIONSIMBA: A Matlab Framework Based on a Finite Volume Model Suitable for Li-Ion Battery Design, Simulation, and Control

Consumer electronics, wearable and personal health devices, power networks, microgrids, and hybrid electric vehicles (HEVs) are some of the many applications of lithium-ion batteries. Their optimal design and management are important for safe and profitable operations. The use of accurate mathematical models can help in achieving the best performance. This article provides a detailed description of a finite volume method (FVM) for a pseudo-two-dimensional (P2D) Li-ion battery model suitable for the development of model-based advanced battery management systems. The objectives of this work are to provide: (i) a detailed description of the model formulation, (ii) a parametrizable Matlab framework for battery design, simulation, and control of Li-ion cells or battery packs, (iii) a validation of the proposed numerical implementation with respect to the COMSOL MultiPhysics commercial software and the Newman’s DUALFOIL code, and (iv) some demonstrative simulations involving thermal dynamics, a hybrid charge-discharge cycle emulating the throttle of an HEV, a model predictive control of state of charge, and a battery pack simulatio

Reinforcement Learning with Partial Parametric Model Knowledge

We adapt reinforcement learning (RL) methods for continuous control to bridge the gap between complete ignorance and perfect knowledge of the environment. Our method, Partial Knowledge Least Squares Policy Iteration (PLSPI), takes inspiration from both model-free RL and model-based control. It uses incomplete information from a partial model and retains RL's data-driven adaption towards optimal performance. The linear quadratic regulator provides a case study; numerical experiments demonstrate the effectiveness and resulting benefits of the proposed method.Comment: IFAC World Congress 202

Data Quality Over Quantity: Pitfalls and Guidelines for Process Analytics

A significant portion of the effort involved in advanced process control, process analytics, and machine learning involves acquiring and preparing data. Literature often emphasizes increasingly complex modelling techniques with incremental performance improvements. However, when industrial case studies are published they often lack important details on data acquisition and preparation. Although data pre-processing is unfairly maligned as trivial and technically uninteresting, in practice it has an out-sized influence on the success of real-world artificial intelligence applications. This work describes best practices for acquiring and preparing operating data to pursue data-driven modelling and control opportunities in industrial processes. We present practical considerations for pre-processing industrial time series data to inform the efficient development of reliable soft sensors that provide valuable process insights.Comment: This work has been accepted to the 22nd IFAC World Congress 202

Stabilizing reinforcement learning control: A modular framework for optimizing over all stable behavior

We propose a framework for the design of feedback controllers that combines the optimization-driven and model-free advantages of deep reinforcement learning with the stability guarantees provided by using the Youla-Kucera parameterization to define the search domain. Recent advances in behavioral systems allow us to construct a data-driven internal model; this enables an alternative realization of the Youla-Kucera parameterization based entirely on input-output exploration data. Perhaps of independent interest, we formulate and analyze the stability of such data-driven models in the presence of noise. The Youla-Kucera approach requires a stable "parameter" for controller design. For the training of reinforcement learning agents, the set of all stable linear operators is given explicitly through a matrix factorization approach. Moreover, a nonlinear extension is given using a neural network to express a parameterized set of stable operators, which enables seamless integration with standard deep learning libraries. Finally, we show how these ideas can also be applied to tune fixed-structure controllers.Comment: Preprint; 18 pages. arXiv admin note: text overlap with arXiv:2304.0342