1,351 research outputs found
Model Predictive Control Strategies for Advanced Battery Management Systems
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.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
Fast Charging of Lithium-Ion Batteries Using Deep Bayesian Optimization with Recurrent Neural Network
Fast charging has attracted increasing attention from the battery community
for electrical vehicles (EVs) to alleviate range anxiety and reduce charging
time for EVs. However, inappropriate charging strategies would cause severe
degradation of batteries or even hazardous accidents. To optimize fast-charging
strategies under various constraints, particularly safety limits, we propose a
novel deep Bayesian optimization (BO) approach that utilizes Bayesian recurrent
neural network (BRNN) as the surrogate model, given its capability in handling
sequential data. In addition, a combined acquisition function of expected
improvement (EI) and upper confidence bound (UCB) is developed to better
balance the exploitation and exploration. The effectiveness of the proposed
approach is demonstrated on the PETLION, a porous electrode theory-based
battery simulator. Our method is also compared with the state-of-the-art BO
methods that use Gaussian process (GP) and non-recurrent network as surrogate
models. The results verify the superior performance of the proposed fast
charging approaches, which mainly results from that: (i) the BRNN-based
surrogate model provides a more precise prediction of battery lifetime than
that based on GP or non-recurrent network; and (ii) the combined acquisition
function outperforms traditional EI or UCB criteria in exploring the optimal
charging protocol that maintains the longest battery lifetime
Constrained optimal control of monotone systems with applications to battery fast-charging
Enabling fast charging for lithium ion batteries is critical to accelerating
the green energy transition. As such, there has been significant interest in
tailored fast-charging protocols computed from the solutions of constrained
optimal control problems. Here, we derive necessity conditions for a fast
charging protocol based upon monotone control systems theory
Gaussian process regression for forecasting battery state of health
Accurately predicting the future capacity and remaining useful life of
batteries is necessary to ensure reliable system operation and to minimise
maintenance costs. The complex nature of battery degradation has meant that
mechanistic modelling of capacity fade has thus far remained intractable;
however, with the advent of cloud-connected devices, data from cells in various
applications is becoming increasingly available, and the feasibility of
data-driven methods for battery prognostics is increasing. Here we propose
Gaussian process (GP) regression for forecasting battery state of health, and
highlight various advantages of GPs over other data-driven and mechanistic
approaches. GPs are a type of Bayesian non-parametric method, and hence can
model complex systems whilst handling uncertainty in a principled manner. Prior
information can be exploited by GPs in a variety of ways: explicit mean
functions can be used if the functional form of the underlying degradation
model is available, and multiple-output GPs can effectively exploit
correlations between data from different cells. We demonstrate the predictive
capability of GPs for short-term and long-term (remaining useful life)
forecasting on a selection of capacity vs. cycle datasets from lithium-ion
cells.Comment: 13 pages, 7 figures, published in the Journal of Power Sources, 201
A review of fractional-order techniques applied to lithium-ion batteries, lead-acid batteries, and supercapacitors
Electrochemical energy storage systems play an important role in diverse applications, such as electrified transportation and integration of renewable energy with the electrical grid. To facilitate model-based management for extracting full system potentials, proper mathematical models are imperative. Due to extra degrees of freedom brought by differentiation derivatives, fractional-order models may be able to better describe the dynamic behaviors of electrochemical systems. This paper provides a critical overview of fractional-order techniques for managing lithium-ion batteries, lead-acid batteries, and supercapacitors. Starting with the basic concepts and technical tools from fractional-order calculus, the modeling principles for these energy systems are presented by identifying disperse dynamic processes and using electrochemical impedance spectroscopy. Available battery/supercapacitor models are comprehensively reviewed, and the advantages of fractional types are discussed. Two case studies demonstrate the accuracy and computational efficiency of fractional-order models. These models offer 15–30% higher accuracy than their integer-order analogues, but have reasonable complexity. Consequently, fractional-order models can be good candidates for the development of advanced b attery/supercapacitor management systems. Finally, the main technical challenges facing electrochemical energy storage system modeling, state estimation, and control in the fractional-order domain, as well as future research directions, are highlighted
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