A5091442780- Advanced Battery Technologies Research
- Advancements in Battery Materials
- Advanced Control Systems Optimization
- Advanced battery technologies research
- Wastewater Treatment and Nitrogen Removal
- Wireless Sensor Networks for Data Analysis
- Sensor Technology and Measurement Systems
- Fault Detection and Control Systems
- Fuel Cells and Related Materials
- Neural Networks and Applications
- Fuzzy Logic and Control Systems
- Advanced Battery Materials and Technologies
Université Libre de Bruxelles
2019-2024
Simón Bolívar University
2014-2018
Universidad Simón Bolívar
2014
This paper presents a two-layer distributed model predictive control (MPC) algorithm developed for safe and fast charge of Li-ion batteries. The low-level MPC makes use an equivalent hydraulic (EHM) that captures the dynamics relevant internal electro-chemical states. controller calculates optimal charging current satisfies constraints associated with side reactions in anode cathode. high-level solves temperature feasibility problem using battery thermal by setting boundaries on square...
This article proposes and compares a new family of low-complexity control schemes for the fast charge lithium-ion (Li-ion) battery cells accounting degradation constraints. These are based on two-level architecture, where low-level linear quadratic regulator (LQR) ensures stability tracking applied reference, while an outer layer, explicit reference governor (ERG), enforces constraints satisfaction by manipulating lower level. The ERG is construction suitable Lyapunov level set contained...
Nonlinear model predictive controllers based on neural networks are implemented in this paper to regulate the activated-sludge process. The simulation protocol BSM1 is used apply controller schemes and study closed loop process behavior different situations. Also input-output data gathered from benchmark for training. Control results under dry-weather perturbations satisfactory when a combined NLMPC - Classic PI control system tested. This scheme has shown best performance compared...
The spread of electrical storage devices continues to be underpinned by the limited charging currents that can applied.The limitation arises from lack sufficient high power stations, either at home or along roads and highways, maximum admissible current applied battery before undesirable degradation mechanisms are triggered.Accordingly, most traditional protocols limit as a function standing state charge battery.These designed empirically restrict potential benefit more flexible...
A nonlinear model predictive controller based on neural networks is designed in this paper to regulate the nitrogen removal activated-sludge process. The benchmark simulation (BSM1) used implement and study its behavior different situations. Also input-output data are gathered from for training. Control results under dry-weather perturbations satisfactory when compared other types of NLMPC.
A nonlinear model with neural networks structure is identified in this paper from input-output data of the activated-sludge process. The simulation protocol BSM1 used as a benchmark to gather operating for training and validations. constituted by two feed-forward estimate oxygen nitrates concentrations. performance each NN assessed virtual sensor variable estimation, or predictive process control purposes.
This work introduces and compares low-computational cost MPC-based algorithms based on a reduced-order electrochemical model, the Equivalent Hydraulic Model (EHM), for fast charge balance of string battery cells accounting degradation-related phenomena. The is carried out through fully shunting grid. scenarios are described in two different approaches, namely: binary variables pulse with modulation (PWM). Due to complexity solving nonlinear non-convex constrained optimal problems, we...