Parameters and Application in Electric Vehicle Battery Charging Based on State of Power (SoP) and State of Energy (SoE)

Estimation of equivalent circuit parameters and open circuit voltage of a battery to predict its state is important for electric vehicle (EV) applications. There is a need to measure the open circuit voltage as accurately as possible as it mirrors the state-of-charge (SoC) of the battery. As calculation of the SoC by integrating the amount of current going in or out of the battery is inaccurate and requires post-processing, this investigation presents one different way to calculate the open circuit voltage and thus the state of charge while the battery is being used. This paper also presents an analytical model of the state of an EV battery pack with the concept of State of Power (SoP) and State of Energy (SoE). These figures of merits help the user to determine how far a battery pack can be used in terms of the vehicle range and acceleration/deceleration capability. LiFePo4 cells were used as the type of Li-ion battery in this investigation. This paper investigates these aspects with the help of vehicle and battery data obtained experimentally and in laboratory environment. The simulation results have been compared and validated against the experimentally obtained results

Prediction of lithium-ion battery capacity by functional monitoring data using functional principal component analysis

Lithium-ion batteries have been a promising energy storage technology for applications such as electronics, automobiles, and smart grids over the years. Extensive research was conducted to improve the prediction of the remaining capacity of the lithium-ion battery. A robust prediction model would improve the battery performance and reliability for forthcoming usage. To develop a data-driven capacity prediction model of lithium-ion batteries most of past studies employed capacity degradation data, yet very few tried using other performance monitoring variables such as temperature, voltage, and current data to estimate and predict the battery capacity. In this thesis, we aim to develop a data-driven model for predicting the capacity of lithium-ion battery adopting functional principal component analysis applied to functional monitoring data of temperature, voltage, and current observations collected from NASA Ames Prognostics Center of Excellence repository. The result of capacity prediction has been substantiated with past studies and obtained root mean square error (RMSE) of 0.009. The proposed data-driven approach performs well to predict the capacity employing functional performance measures over the life span of a lithium-ion battery

A mathematical model for mechanically-induced deterioration of the binder in lithium-ion electrodes

This study is concerned with modeling detrimental deformations of the binder phase within lithium-ion batteries that occur during cell assembly and usage. A two-dimensional poroviscoelastic model for the mechanical behavior of porous electrodes is formulated and posed on a geometry corresponding to a thin rectangular electrode, with a regular square array of microscopic circular electrode particles, stuck to a rigid base formed by the current collector. Deformation is forced both by (i) electrolyte absorption driven binder swelling, and; (ii) cyclic growth and shrinkage of electrode particles as the battery is charged and discharged. The governing equations are upscaled in order to obtain macroscopic effective-medium equations. A solution to these equations is obtained, in the asymptotic limit that the height of the rectangular electrode is much smaller than its width, that shows the macroscopic deformation is one-dimensional. The confinement of macroscopic deformations to one dimension is used to obtain boundary conditions on the microscopic problem for the deformations in a 'unit cell' centered on a single electrode particle. The resulting microscale problem is solved using numerical (finite element) techniques. The two different forcing mechanisms are found to cause distinctly different patterns of deformation within the microstructure. Swelling of the binder induces stresses that tend to lead to binder delamination from the electrode particle surfaces in a direction parallel to the current collector, whilst cycling causes stresses that tend to lead to delamination orthogonal to that caused by swelling. The differences between the cycling-induced damage in both: (i) anodes and cathodes, and; (ii) fast and slow cycling are discussed. Finally, the model predictions are compared to microscopy images of nickel manganese cobalt oxide cathodes and a qualitative agreement is found.Comment: 25 pages, 11 figure

Model-free non-invasive health assessment for battery energy storage assets

Increasing penetration of renewable energy generation in the modern power network introduces uncertainty about the energy available to maintain a balance between generation and demand due to its time-fluctuating output that is strongly dependent on the weather. With the development of energy storage technology, there is the potential for this technology to become a key element to help overcome this intermittency in a generation. However, the increasing penetration of battery energy storage within the power network introduces an additional challenge to asset owners on how to monitor and manage battery health. The accurate estimation of the health of this device is crucial in determining its reliability, power-delivering capability and ability to contribute to the operation of the whole power system. Generally, doing this requires invasive measurements or computationally expensive physics-based models, which do not scale up cost-effectively to a fleet of assets. As storage aggregation becomes more commonplace, there is a need for a health metric that will be able to predict battery health based only on the limited information available, eliminating the necessity of installation of extensive telemetry in the system. This work develops a solution to battery health prognostics by providing an alternative, a non-invasive approach to the estimation of battery health that estimates the extent to which a battery asset has been maloperated based only on the battery-operating regime imposed on the device. The model introduced in this work is based on the Hidden Markov Model, which stochastically models the battery limitations imposed by its chemistry as a combination of present and previous sequential charging actions, and articulates the preferred operating regime as a measure of health consequence. The resulting methodology is demonstrated on distribution network level electrical demand and generation data, accurately predicting maloperation under a number of battery technology scenarios. The effectiveness of the proposed battery maloperation model as a proxy for actual battery degradation for lithium-ion technology was also tested against lab tested battery degradation data, showing that the proposed health measure in terms of maloperation level reflected that measured in terms of capacity fade. The developed model can support condition monitoring and remaining useful life estimates, but in the wider context could also be used as the policy function in an automated scheduler to utilise assets while optimising their health.Increasing penetration of renewable energy generation in the modern power network introduces uncertainty about the energy available to maintain a balance between generation and demand due to its time-fluctuating output that is strongly dependent on the weather. With the development of energy storage technology, there is the potential for this technology to become a key element to help overcome this intermittency in a generation. However, the increasing penetration of battery energy storage within the power network introduces an additional challenge to asset owners on how to monitor and manage battery health. The accurate estimation of the health of this device is crucial in determining its reliability, power-delivering capability and ability to contribute to the operation of the whole power system. Generally, doing this requires invasive measurements or computationally expensive physics-based models, which do not scale up cost-effectively to a fleet of assets. As storage aggregation becomes more commonplace, there is a need for a health metric that will be able to predict battery health based only on the limited information available, eliminating the necessity of installation of extensive telemetry in the system. This work develops a solution to battery health prognostics by providing an alternative, a non-invasive approach to the estimation of battery health that estimates the extent to which a battery asset has been maloperated based only on the battery-operating regime imposed on the device. The model introduced in this work is based on the Hidden Markov Model, which stochastically models the battery limitations imposed by its chemistry as a combination of present and previous sequential charging actions, and articulates the preferred operating regime as a measure of health consequence. The resulting methodology is demonstrated on distribution network level electrical demand and generation data, accurately predicting maloperation under a number of battery technology scenarios. The effectiveness of the proposed battery maloperation model as a proxy for actual battery degradation for lithium-ion technology was also tested against lab tested battery degradation data, showing that the proposed health measure in terms of maloperation level reflected that measured in terms of capacity fade. The developed model can support condition monitoring and remaining useful life estimates, but in the wider context could also be used as the policy function in an automated scheduler to utilise assets while optimising their health

Phase Separation Dynamics in Isotropic Ion-Intercalation Particles

Lithium-ion batteries exhibit complex nonlinear dynamics, resulting from diffusion and phase transformations coupled to ion intercalation reactions. Using the recently developed Cahn-Hilliard reaction (CHR) theory, we investigate a simple mathematical model of ion intercalation in a spherical solid nanoparticle, which predicts transitions from solid-solution radial diffusion to two-phase shrinking-core dynamics. This general approach extends previous Li-ion battery models, which either neglect phase separation or postulate a spherical shrinking-core phase boundary, by predicting phase separation only under appropriate circumstances. The effect of the applied current is captured by generalized Butler-Volmer kinetics, formulated in terms of diffusional chemical potentials, and the model consistently links the evolving concentration profile to the battery voltage. We examine sources of charge/discharge asymmetry, such as asymmetric charge transfer and surface "wetting" by ions within the solid, which can lead to three distinct phase regions. In order to solve the fourth-order nonlinear CHR initial-boundary-value problem, a control-volume discretization is developed in spherical coordinates. The basic physics are illustrated by simulating many representative cases, including a simple model of the popular cathode material, lithium iron phosphate (neglecting crystal anisotropy and coherency strain). Analytical approximations are also derived for the voltage plateau as a function of the applied current

Dynamic model of lithium polymer battery: Load resistor method for electric parameters identification

Maximum battery runtime and its transients behaviors are crucial in many applications. With accurate battery models in hand, circuit designers can evaluate the performance of its developments considering the influence of a finite source of energy which has a particular dynamics; as well as the energy storage systems can be optimized. First, this work describes a complete dynamic model of a lithium polymer battery. In the sequel a simple and novel procedure is used to obtain the electric parameters of adopted model with the advantage of using only one resistor to represent the battery load and a pc-connected multimeter. The methodology used to identify the parameters of the battery model is simple, clearly explained and can be applied to various types of batteries. Simulation and experimental results are presented and discussed, demonstrating the good performance of the proposed identification methodology.Fil: Gandolfo, Daniel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Brandao, Alexandre. Universidade Federal de Viçosa; BrasilFil: Patiño, Héctor Daniel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Molina, Marcelo Gustavo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentin

A critical review of online battery remaining useful lifetime prediction methods.

Lithium-ion batteries play an important role in our daily lives. The prediction of the remaining service life of lithium-ion batteries has become an important issue. This article reviews the methods for predicting the remaining service life of lithium-ion batteries from three aspects: machine learning, adaptive filtering, and random processes. The purpose of this study is to review, classify and compare different methods proposed in the literature to predict the remaining service life of lithium-ion batteries. This article first summarizes and classifies various methods for predicting the remaining service life of lithium-ion batteries that have been proposed in recent years. On this basis, by selecting specific criteria to evaluate and compare the accuracy of different models, find the most suitable method. Finally, summarize the development of various methods. According to the research in this article, the average accuracy of machine learning is 32.02% higher than the average of the other two methods, and the prediction cycle is 9.87% shorter than the average of the other two methods

Lithium-Ion batteries modeling and state of charge estimation using Artificial Neural Network

In This paper, we propose an effective and online technique for modeling nd State of Charge (SoC) estimation of Lithium-Ion (Li-Ion) batteries using Feed Forward Neural Networks(FFNN) and Nonlinear Auto Regressive model with eXogenous input(NARX). The both Artificial Neural Network (ANN) are rained using the data collected from the batterycharging and discharging pro ess. The NARX network finds the needed battery model, where the input ariables are the battery terminal voltage, SoC at the previous sample, and the urrent, temperature at the present sample. The proposed method is imple mented on a Li-Ion battery cell to estimate online SoC. Simulation results show good estimation of theSoC