Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userNonlinear Double-Capacitor Model for Rechargeable Batteries: Modeling, Identification and Validation
128
0
0.0
(
0
)
Added by
Huazhen Fang
Publication date
2019
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
This paper proposes a new equivalent circuit model for rechargeable batteries by modifying a double-capacitor model proposed in [1]. It is known that the original model can address the rate capacity effect and energy recovery effect inherent to batteries better than other models. However, it is a purely linear model and includes no representation of a batterys nonlinear phenomena. Hence, this work transforms the original model by introducing a nonlinear-mapping-based voltage source and a serial RC circuit. The modification is justified by an analogy with the single-particle model. Two parameter estimation approaches, termed 1.0 and 2.0, are designed for the new model to deal with the scenarios of constant-current and variable-current charging/discharging, respectively. In particular, the 2.0 approach proposes the notion of Wiener system identification based on maximum a posteriori estimation, which allows all the parameters to be estimated in one shot while overcoming the nonconvexity or local minima issue to obtain physically reasonable estimates. An extensive experimental evaluation shows that the proposed model offers excellent accuracy and predictive capability. A comparison against the Rint and Thevenin models further points to its superiority. With high fidelity and low mathematical complexity, this model is beneficial for various real-time battery management applications.
rate research
Read More
Parameter estimation is of foundational importance for various model-based battery management tasks, including charging control, state-of-charge estimation and aging assessment. However, it remains a challenging issue as the existing methods generally depend on cumbersome and time-consuming procedures to extract battery parameters from data. Departing from the literature, this paper sets the unique aim of identifying all the parameters offline in a one-shot procedure, including the resistance and capacitance parameters and the parameters in the parameterized function mapping from the state-of-charge to the open-circuit voltage. Considering the well-known Thevenins battery model, the study begins with the parameter identifiability analysis, showing that all the parameters are locally identifiable. Then, it formulates the parameter identification problem in a prediction-error-minimization framework. As the non-convexity intrinsic to the problem may lead to physically meaningless estimates, two methods are developed to overcome this issue. The first one is to constrain the parameter search within a reasonable space by setting parameter bounds, and the other adopts regularization of the cost function using prior parameter guess. The proposed identifiability analysis and identification methods are extensively validated through simulations and experiments.
Full charge capacity (FCC) refers to the amount of energy a battery can hold. It is the fundamental property of smartphone batteries that diminishes as the battery ages and is charged/discharged. We investigate the behavior of smartphone batteries while charging and demonstrate that the battery voltage and charging rate information can together characterize the FCC of a battery. We propose a new method for accurately estimating FCC without exposing low-level system details or introducing new hardware or system modules. We also propose and implement a collaborative FCC estimation technique that builds on crowdsourced battery data. The method finds the reference voltage curve and charging rate of a particular smartphone model from the data and then compares the curve and rate of an individual user with the model reference curve. After analyzing a large data set, we report that 55% of all devices and at least one device in 330 out of 357 unique device models lost some of their FCC. For some models, the median capacity loss exceeded 20% with the inter-quartile range being over 20 pp. The models enable debugging the performance of smartphone batteries, more accurate power modeling, and energy-aware system or application optimization.
This paper discusses a novel initialization algorithm for the estimation of nonlinear state-space models. Good initial values for the model parameters are obtained by identifying separately the linear dynamics and the nonlinear terms in the model. In particular, the nonlinear dynamic problem is transformed into an approximate static formulation, and simple regression methods are applied to obtain the solution in a fast and efficient way. The proposed method is validated by means of two measurement examples: the Wiener-Hammerstein benchmark problem, and the identification of a crystal detector.
Model instability and poor prediction of long-term behavior are common problems when modeling dynamical systems using nonlinear black-box techniques. Direct optimization of the long-term predictions, often called simulation error minimization, leads to optimization problems that are generally non-convex in the model parameters and suffer from multiple local minima. In this work we present methods which address these problems through convex optimization, based on Lagrangian relaxation, dissipation inequalities, contraction theory, and semidefinite programming. We demonstrate the proposed methods with a model order reduction task for electronic circuit design and the identification of a pneumatic actuator from experiment.
Controlling nanostructure from molecular, crystal lattice to the electrode level remains as arts in practice, where nucleation and growth of the crystals still require more fundamental understanding and precise control to shape the microstructure of metal deposits and their properties. This is vital to achieve dendrite-free Li metal anodes with high electrochemical reversibility for practical high-energy rechargeable Li batteries. Here, cryogenic-transmission electron microscopy was used to capture the dynamic growth and atomic structure of Li metal deposits at the early nucleation stage, in which a phase transition from amorphous, disordered states to a crystalline, ordered one was revealed as a function of current density and deposition time. The real-time atomic interaction over wide spatial and temporal scales was depicted by the reactive-molecular dynamics simulations. The results show that the condensation accompanied with the amorphous-to-crystalline phase transition requires sufficient exergy, mobility and time to carry out, contrary to what the classical nucleation theory predicts. These variabilities give rise to different kinetic pathways and temporal evolutions, resulting in various degrees of order and disorder nanostructure in nano-sized domains that dominate in the morphological evolution and reversibility of Li metal electrode. Compared to crystalline Li, amorphous/glassy Li outperforms in cycle life in high-energy rechargeable batteries and is the desired structure to achieve high kinetic stability for long cycle life.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
