Battery Management System for Future Electric Vehicles

The future of electric vehicles relies nearly entirely on the design, monitoring, and control of the vehicle battery and its associated systems. Along with an initial optimal design of the cell/pack-level structure, the runtime performance of the battery needs to be continuously monitored and optimized for a safe and reliable operation and prolonged life. Improved charging techniques need to be developed to protect and preserve the battery. The scope of this Special Issue is to address all the above issues by promoting innovative design concepts, modeling and state estimation techniques, charging/discharging management, and hybridization with other storage components

Set-based state estimation and fault diagnosis using constrained zonotopes and applications

This doctoral thesis develops new methods for set-based state estimation and active fault diagnosis (AFD) of (i) nonlinear discrete-time systems, (ii) discrete-time nonlinear systems whose trajectories satisfy nonlinear equality constraints (called invariants), (iii) linear descriptor systems, and (iv) joint state and parameter estimation of nonlinear descriptor systems. Set-based estimation aims to compute tight enclosures of the possible system states in each time step subject to unknown-but-bounded uncertainties. To address this issue, the present doctoral thesis proposes new methods for efficiently propagating constrained zonotopes (CZs) through nonlinear mappings. Besides, this thesis improves the standard prediction-update framework for systems with invariants using new algorithms for refining CZs based on nonlinear constraints. In addition, this thesis introduces a new approach for set-based AFD of a class of nonlinear discrete-time systems. An affine parametrization of the reachable sets is obtained for the design of an optimal input for set-based AFD. In addition, this thesis presents new methods based on CZs for set-valued state estimation and AFD of linear descriptor systems. Linear static constraints on the state variables can be directly incorporated into CZs. Moreover, this thesis proposes a new representation for unbounded sets based on zonotopes, which allows to develop methods for state estimation and AFD also of unstable linear descriptor systems, without the knowledge of an enclosure of all the trajectories of the system. This thesis also develops a new method for set-based joint state and parameter estimation of nonlinear descriptor systems using CZs in a unified framework. Lastly, this manuscript applies the proposed set-based state estimation and AFD methods using CZs to unmanned aerial vehicles, water distribution networks, and a lithium-ion cell.Comment: My PhD Thesis from Federal University of Minas Gerais, Brazil. Most of the research work has already been published in DOIs 10.1109/CDC.2018.8618678, 10.23919/ECC.2018.8550353, 10.1016/j.automatica.2019.108614, 10.1016/j.ifacol.2020.12.2484, 10.1016/j.ifacol.2021.08.308, 10.1016/j.automatica.2021.109638, 10.1109/TCST.2021.3130534, 10.1016/j.automatica.2022.11042

FPGA-based implementation of real-time identification procedures for adaptive control in photovoltaic applications

2013 - 2014In this thesis two adaptive Maximum Power Point Tracking (MPPT) techniques for PhotoVoltaic (PV) applications, which are based on two different real-time identification procedures are proposed. The algorithms are implemented on the same low-cost Field Programmable Gate Array (FPGA) device in charge of controlling the switching converter that processes the power produced by the PV array. The Perturb & Observe (P&O) algorithm is the most common MPPT technique. Its efficiency is mainly related to two parameters: the perturbation amplitude and the perturbation period Tp. The optimal values of such parameters depend on the PV array type and on the irradiance and temperature conditions thereof, as well as on the parameters of the power processing circuit. Thus, a method for dynamically adapt the P&O parameters would be very useful for increasing the P&O MPPT performances. Several approaches presented in the current literature are focused on the adaptation of the perturbation amplitude. In this thesis, on the contrary, the on-line optimization of the value of Tp is proposed. The effects of such a parameter on both the tracking speed and the stationary MPPT efficiency are pointed out. Besides, the need for a real-time identification technique for identifying the minimum acceptable value of Tp in the actual PV operating conditions is demonstrated. Two different identification procedures aimed at developing the aforementioned adaptive MPPT controllers have been studied: the Cross-Correlation Method (CCM) and the Dual Kalman Filter (DKF). The first one belongs to the non-parametric techniques and allows identifying the impulse response and the frequency response of the PV system. Instead, the DKF is a model-based approach which estimates the states and the parameters of the system. One of the aims of this thesis is to demonstrate the usefulness of these identification procedures for the optimization of the PV P&O MPPT performances. In order to achieve a good trade-off between the desired performances and the cost of the controller, hardware digital solutions, such as FPGA, are adopted. They are able to reduce the execution time by exploiting the intrinsic parallelism of the algorithm to be implemented. Then, in this work, the challenging design of a high performances hardware architecture for the identification algorithms is dealt with. Moreover, the implemented identification techniques are compared in terms of accuracy, identification time and used hardware resources. Several simulations and experimental tests demonstrate the feasibility of the developed identification procedures. In fact, the proposed adaptive MPPT controllers suitably change in few tens of milliseconds the value of Tp ensuring a stable MPPT behaviour. The developed FPGA-based architectures of both the identification techniques is promising for embedding other functions that are of interest in the field of PV systems, e.g. related to on-line monitoring or diagnostic purposes. The work has been developed in co-tutorship between the Systèmes et Applications des Technologies de l’Information et de l’Energie (SATIE) laboratory in the Université de Cergy-Pontoise (France) and the Circuiti Elettronici di Potenza laboratory in the Universitá degli Studi di Salerno (Italy). The work has been supported by the Université Franco-Italienne by means the Vinci project 2013 n. C2-29. [edited by author]XIII n.s