1,720,967 research outputs found
Model Predictive Control Strategies for Advanced Battery Management Systems
Full text linkConsumer electronics, wearable and personal health devices, power networks, microgrids, and hybrid electric vehicles (HEVs) are some of the many applications where Lithium-ion (Li-ion) batteries are employed. From a manufacturer point of view, the optimal design and management of such electrochemical accumulators are important aspects for ensuring safe and profitable operations. The adoption of mathematical models can support the achievement of the best performance, while saving time and money. In the literature, all the models used to describe the behavior of a Li-ion battery belong to one of the two following families: (i) Equivalent Circuit Models (ECMs), and (ii) Electrochemical Models (EMs). While the former family represents the battery dynamics by means of electrical circuits, the latter resorts to first principles laws of modeling. As a first contribution, this Thesis provides a thorough investigation of the pseudo-two-dimensional (P2D) Li-ion battery EM. In particular, the objectives are to provide: (i) a detailed description of the model formulation, (ii) the Li-ION SIMulation BAttery (LIONSIMBA) toolbox as a finite volume Matlab implementation of the P2D model, for design, simulation, and control of Li-ion cells or battery packs, (iii) a validation of the proposed tool 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, and a battery pack of series connected cells. The second contribution is related to the development of several charging strategies for Advanced Battery Management Systems (ABMSs), where predictive approaches are employed to attain optimal control. Model Predictive Control (MPC) refers to a particular family of control algorithms that, according to a mathematical model, predicts the future behavior of a plant, while considering inputs and outputs constraints. According to this paradigm, in this Thesis different ABMSs strategies have been developed, and their effectiveness shown through simulations. Due to the complexity of the P2D model, its inclusion within an MPC context could prevent the online application of the control algorithm. For this reason, different approximations of the P2D dynamics are proposed and their MPC formulations carefully explained. In particular, finite step response, autoregressive exogenous, piecewise affine, and linear time varying approximations are presented. For all the aforementioned reformulations, the closed-loop performance are evaluated considering the P2D implementation of LIONSIMBA as the real plant. The closed-loop simulations highlight the suitability of the MPC paradigm to be employed for the development of the future ABMSs. In fact, its ability to predict the future behavior of the cell while considering operating constraints can help in preventing possible safety issues and improving the charging performance. Finally, the reliability and efficiency of the proposed Matlab toolbox in simulating the P2D dynamics, support the idea that LIONSIMBA can significantly contribute in the advance of the battery field.Consumer electronics, wearable and personal health devices, power networks, microgrids, and hybrid electric vehicles (HEVs) are some of the many applications where Lithium-ion (Li-ion) batteries are employed. From a manufacturer point of view, the optimal design and management of such electrochemical accumulators are important aspects for ensuring safe and profitable operations. The adoption of mathematical models can support the achievement of the best performance, while saving time and money. In the literature, all the models used to describe the behavior of a Li-ion battery belong to one of the two following families: (i) Equivalent Circuit Models (ECMs), and (ii) Electrochemical Models (EMs). While the former family represents the battery dynamics by means of electrical circuits, the latter resorts to first principles laws of modeling. As a first contribution, this Thesis provides a thorough investigation of the pseudo-two-dimensional (P2D) Li-ion battery EM. In particular, the objectives are to provide: (i) a detailed description of the model formulation, (ii) the Li-ION SIMulation BAttery (LIONSIMBA) toolbox as a finite volume Matlab implementation of the P2D model, for design, simulation, and control of Li-ion cells or battery packs, (iii) a validation of the proposed tool 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, and a battery pack of series connected cells. The second contribution is related to the development of several charging strategies for Advanced Battery Management Systems (ABMSs), where predictive approaches are employed to attain optimal control. Model Predictive Control (MPC) refers to a particular family of control algorithms that, according to a mathematical model, predicts the future behavior of a plant, while considering inputs and outputs constraints. According to this paradigm, in this Thesis different ABMSs strategies have been developed, and their effectiveness shown through simulations. Due to the complexity of the P2D model, its inclusion within an MPC context could prevent the online application of the control algorithm. For this reason, different approximations of the P2D dynamics are proposed and their MPC formulations carefully explained. In particular, finite step response, autoregressive exogenous, piecewise affine, and linear time varying approximations are presented. For all the aforementioned reformulations, the closed-loop performance are evaluated considering the P2D implementation of LIONSIMBA as the real plant. The closed-loop simulations highlight the suitability of the MPC paradigm to be employed for the development of the future ABMSs. In fact, its ability to predict the future behavior of the cell while considering operating constraints can help in preventing possible safety issues and improving the charging performance. Finally, the reliability and efficiency of the proposed Matlab toolbox in simulating the P2D dynamics, support the idea that LIONSIMBA can significantly contribute in the advance of the battery field
Optimal Health-aware Charging Protocol for Lithium-ion Batteries: A Fast Model Predictive Control Approach
No full textA mixed integer SDP approach for the optimal placement of energy storage devices in power grids with renewable penetration
No full textFilm growth minimization in a Li-ion cell: A Pseudo Two Dimensional model-based optimal charging approach
No full textSafety, fast charging and aging are among the most important issues when dealing with high power battery packs in electric and hybrid vehicles. Today's charging strategies are designed to guarantee safe operation in a conservative way, but are far from being optimal in terms of aging reduction and time charging minimization. For this reason, the interest of the research is focused on developing model-based battery management systems. Comparing standard charging strategies with health-aware optimization-based ones is difficult since they usually provide different charging times: are we willing to charge the battery in more time if this comes with an aging reduction? When a customer is faced with such a question, the answer is not so trivial. For this reason, in this paper we provide health-aware Pseudo Two Dimensional model-based strategies with the same charging time of standard CC-Cv protocols. The results show that significant aging improvements can be obtained, even by constraining the charging time to be the same as the CC-Cv protocol
Design of Piecewise Affine and Linear Time Varying based Model Predictive Control Strategies for Advanced Battery Management Systems
No full textAssessing the Performance of Model-Based Energy Saving Charging Strategies in Li-Ion Cells
No full textLi-ion batteries are widely employed as sources of portable energy. Advanced Battery Management Systems (ABMSs) rely on models to optimize battery performance (e.g. time-charge, life-time). The aim of this work is to assess if an energy efficiency improvement with respect to standard charging protocols can be obtained from model-based optimization in the time or in the frequency domain. Time-domain optimization is first considered and it is shown that only negligible improvements can be obtained. Then, motivated by successful experiments (e.g. [1], [2]), frequency-domain optimization is also investigated. Within this context, we prove that linear models are inadequate for providing any improvement. Surprisingly, even the mostly used electrochemical model available in literature (P2D), fails to capture the energy dissipation reduction with sinusoidal input current. These results show that currently available models are not suitable for the design of model-based energy saving strategies
Optimising lithium-ion cell design for plug-in hybrid and battery electric vehicles
Full text linkIncreased driving range and enhanced fast charging capabilities are two immediate goals of transport electrification. However, these are of competing nature, leading to increased energy and power demand respectively from the on-board battery pack. By fine-tuning the number of layers versus active electrode material of a lithium ion pouch cell, tailored designs targeting either of these goals can be obtained. Achieving this trade-off through iterative empirical testing of layer choices is expensive and often produces sub-optimal designs. This paper presents a model-based methodology for determining the optimal number of layers, maximising usable energy whilst satisfying specific acceleration and fast charging targets. The proposed methodology accounts for the critical need to avoid lithium plating during fast charging and searches for the optimal layer configuration considering a range of thermal conditions. A numerical implementation of a cell model using a hybrid finite volume-spectral scheme is presented, wherein the model equations are suitably reformulated to directly accept power inputs, facilitating rapid and accurate searching of the layer design space. Electrode materials exhibiting high solid phase diffusion rates are highlighted as being equally as important for extended range as the development of new materials with higher inherent capacity. The proposed methodology is demonstrated for the common module design of a battery pack in a plug-in hybrid vehicle, thereby illustrating how the cost of derivative vehicle models can be reduced. To facilitate model based layer optimisation, the open-source toolbox, BOLD (Battery Optimal Layer Design) is provided
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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