Mathematical Modeling and Capacity Fading Study in Porous Current Collector Based Lithium Ion Battery

Lithium ion (Li-ion) batteries are primary energy storage devices especially in electronic gadgets, electric vehicles and for stationary storage of intermittent renewable energy. These applications demand durable Li-ion batteries with higher energy density. Energy density can be increased either by finding novel electrode materials or by modifying the existing design of the battery. The electrode materials or modified design should not only increase energy density, but also should control the capacity fading of the battery. In this work, existing mathematical model of Li-ion battery was adjusted in the case of the porous current collector. The discharge performance and capacity fading of the porous current collector based Li-ion battery was compared with non-porous current collector Li-ion battery. The electrode averaged model (EAM) was used to simulate the discharge performance of the battery. The capacity fade was compared by comparing the film growth, change of initial electrode state of charge (SOC) and change in solid phase diffusion coefficient with cycling. Both simulation and experimental results have shown the porous current collector based Li-ion battery achieves greater than the theoretical specific capacity of electrode active materials for the first few cycles of operations. In this work, Lithium titanate was considered as an electrode active material which has a theoretical specific capacity of 175 mAh/g. Simulation and experiment have predicted specific capacities of 238 mAh/g and 235 mAh/g respectively in the case of the porous current collector. Simulation result showed the porous current collector Li-ion batteries reaches the end of useful life after 100 more cycles than the non-porous current collector batteries under similar conditions of operation

A Review on Temperature-Dependent Electrochemical Properties, Aging, and Performance of Lithium-Ion Cells

Temperature heavily affects the behavior of any energy storage chemistries. In particular, lithium-ion batteries (LIBs) play a significant role in almost all storage application fields, including Electric Vehicles (EVs). Therefore, a full comprehension of the influence of the temperature on the key cell components and their governing equations is mandatory for the effective integration of LIBs into the application. If the battery is exposed to extreme thermal environments or the desired temperature cannot be maintained, the rates of chemical reactions and/or the mobility of the active species may change drastically. The alteration of properties of LIBs with temperature may create at best a performance problem and at worst a safety problem. Despite the presence of many reports on LIBs in the literature, their industrial realization has still been difficult, as the technologies developed in different labs have not been standardized yet. Thus, the field requires a systematic analysis of the effect of temperature on the critical properties of LIBs. In this paper, we report a comprehensive review of the effect of temperature on the properties of LIBs such as performance, cycle life, and safety. In addition, we focus on the alterations in resistances, energy losses, physicochemical properties, and aging mechanism when the temperature of LIBs are not under control

Online condition monitoring of lithium-ion and lead acid batteries for renewable energy applications

Electrochemical Impedance Spectroscopy (EIS) has been largely employed for the study of reaction kinetics and condition monitoring of batteries during different operational conditions, such as: Temperature, State of Charge (SoC) and State of Health (SoH) etc. The EIS plot translates to the impedance profile of a battery and is fitted to an Equivalent Electric Circuit (EEC) that model the physicochemical processes occurring in the batteries. To precisely monitor the condition of the batteries, Kramers-Kronig relation: linearity, stability and causality as well as the appropriate perturbation amplitude applied during EIS should be adhered to. Regardless of the accuracy of EIS, its lengthy acquisition time makes it impracticable for online measurement. Different broadband signals have been proposed in literature to shorten EIS measurement time, with different researchers favouring one technique over the other. Nonetheless, broadband signals applied to characterize a battery must be reasonably accurate, with little effect on the systems instrumentation. The major objective of this study is to explore the differences in the internal chemistries of the lithium-ion and lead acid batteries and to reduce the time associated with their condition monitoring using EIS. In this regard, this study firstly queries the methodology for EIS experiments, by investigating the optimum perturbation amplitude for EIS measurement on both the lead acid and lithium-ion batteries. Secondly, this study utilizes electrochemical equations to predict the dynamics and operational conditions associated with batteries. It also investigates the effect of different operational conditions on the lead acid and lithium-ion batteries after EEC parameters have been extracted from EIS measurements. Furthermore, different broadband excitation techniques for rapid diagnostics are explored. An online condition monitoring system is implemented through the utilization of a DC-DC converter that is used to interface the battery with the load. The online system is applied alongside the different broadband signals. The deviation in the broadband impedance spectroscopy result is compared against the Frequency Response Analyzer (FRA) to determine the most suitable technique for battery state estimation. Based on the comparisons, the adoption of a novel technique – Chirp Broadband Signal Excitation (CBSE) is proposed for online condition monitoring of batteries, as it has the advantage of being faster and precise at the most important frequency decade of the impedance spectrum of batteries

Overview of Lithium-Ion battery modeling methods for state-of-charge estimation in electrical vehicles

As a critical indictor in the Battery Management System (BMS), State of Charge (SOC) is closely related to the reliable and safe operation of lithium-ion (Li-ion) batteries. Model-based methods are an effective solution for accurate and robust SOC estimation, the performance of which heavily relies on the battery model. This paper mainly focuses on battery modeling methods, which have the potential to be used in a model-based SOC estimation structure. Battery modeling methods are classified into four categories on the basis of their theoretical foundations, and their expressions and features are detailed. Furthermore, the four battery modeling methods are compared in terms of their pros and cons. Future research directions are also presented. In addition, after optimizing the parameters of the battery models by a Genetic Algorithm (GA), four typical battery models including a combined model, two RC Equivalent Circuit Model (ECM), a Single Particle Model (SPM), and a Support Vector Machine (SVM) battery model are compared in terms of their accuracy and execution time

Transient electrochemical heat transfer modeling and experimental validation of a large sized LiFePO4/graphite battery

The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.ijheatmasstransfer.2017.03.005 © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Both measurement and modeling of thermal performance in lithium-ion battery cell are considered crucial as they directly affect the safety. Even though the operation of a lithium-ion battery cell is transient phenomena in most cases, most available thermal models for lithium-ion battery cell predicts only steady-state temperature fields. This paper presents a mathematical model to predict the transient temperature distributions of a large sized 20Ah-LiFePO4 prismatic battery at different C-rates. In this regard, the lithium-ion battery is placed in a vertical position on a stand inside the lab with an ambient air cooling and the battery is discharged under constant current rate of 1C, 2C, 3C, and 4C in order to provide quantitative data regarding thermal behavior of lithium-ion batteries. Additionally, IR images are taken for the same battery cell during discharging. The present model predictions are in very good agreement with the experimental data and also with an IR imaging for temperature profiles. The present results show that the increased C-rates result in increased temperatures on the principle surface of the battery. (C) 2017 Elsevier Ltd. All rights reserved

Online modelling and state-of-charge estimation for lithium-titanate battery

Superior safety, is a promising energy storage element for electric vehicles. Its features can be fully utilised by using a fast charger and a high performance battery management system. Battery model is vital to a battery charger design for characterising the charging behaviours of a battery. Additionally, a robust state-ofcharge (SoC) estimation should be realised for a reliable battery management. This thesis develops a battery model for charger design and a robust method for SoC estimation by using MATLAB. The thesis proposed a transfer function-based battery model which is applicable for small-signal analysis and large-signal simulation of battery charger design, in order to capture the charging profiles of LTO battery. Busse’s adaptive rule, which has simple computations, is applied to improve the accuracy of Kalman filter-based SoC estimation. Busse’s adaptive Kalman filters are also applied for SoC estimation with online battery modelling to eliminate the complicated process of battery modelling. This study was conducted by using 2.4 V, 15 Ah LTO batteries. The batteries were tested with continuous current test and pulsed current test at several ambient temperatures (-25 ºC, 0 ºC, 25 ºC and 50 ºC) and charge/discharge currents (0.5 C, 1 C, 2 C). Additionally, modified dynamic stress tests at several temperatures (-15 ºC, 0 ºC, 15 ºC, 25 ºC, 35 ºC and 50 ºC) were also performed to test the battery under real EV environment. Results of the battery modelling showed that the developed transfer function-based battery model is accurate where the root-mean-square modelling error is less than 30 mV. The results also revealed that the Busse’s adaptive rule has effectively improved the Kalman filter-based SoC estimation for the case of offline battery model by giving a higher accuracy and shorter convergence time. Additionally, Busse’s adaptive Extended Kalman Filter gave a better accuracy in SoC estimation with online battery modelling. The proposed transfer function-based battery model provides a helpful solution for the battery charger design while the proposed Busse’s adaptive Kalman filter offers an accurate and robust SoC estimation for both offline and online battery models