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

Recent Advances in Model-Based Fault Diagnosis for Lithium-Ion Batteries: A Comprehensive Review

Lithium-ion batteries (LIBs) have found wide applications in a variety of fields such as electrified transportation, stationary storage and portable electronics devices. A battery management system (BMS) is critical to ensure the reliability, efficiency and longevity of LIBs. Recent research has witnessed the emergence of model-based fault diagnosis methods in advanced BMSs. This paper provides a comprehensive review on the model-based fault diagnosis methods for LIBs. First, the widely explored battery models in the existing literature are classified into physics-based electrochemical models and electrical equivalent circuit models. Second, a general state-space representation that describes electrical dynamics of a faulty battery is presented. The formulation of the state vectors and the identification of the parameter matrices are then elaborated. Third, the fault mechanisms of both battery faults (incl. overcharege/overdischarge faults, connection faults, short circuit faults) and sensor faults (incl. voltage sensor faults and current sensor faults) are discussed. Furthermore, different types of modeling uncertainties, such as modeling errors and measurement noises, aging effects, measurement outliers, are elaborated. An emphasis is then placed on the observer design (incl. online state observers and offline state observers). The algorithm implementation of typical state observers for battery fault diagnosis is also put forward. Finally, discussion and outlook are offered to envision some possible future research directions.Comment: Submitted to Renewable and Sustainable Energy Reviews on 09-Jan-202

A physics-based fractional-order equivalent circuit model for time and frequency-domain applications in lithium-ion batteries

This work was partially supported by the Regional Government of Andalusia under project P18-RT-3303 from Plan Andaluz de Investigación, Desarrollo e Innovación (PAIDI 2020), by the Spanish Ministry of Science and Innovation and by FEDER funds via Project MCI-20-PID2019-110955RB-I00, by the Principality of Asturias via project AYUD/2021/50994, and by the FPU-UGR-Banco Santander Program for Predoctoral Scholarships. Funding for open access charge: Universidad de Granada / CBUAEquivalent circuit models (ECMs) remain the most popular choice for online applications in lithium-ion batteries because of their simpler parameterization and lower computational requirements in comparison to electrochemical models. Nevertheless, standard ECMs lack physical insight and fail to accurately reproduce cell behavior under a wide range of operating conditions. For this reason, the development of physics-informed ECMs becomes essential so as to provide a better description of the physical processes while maintaining a reduced computational complexity. In this article, we propose a novel physics-based ECM derived directly from an electrochemical model, so that there is a clear correlation between circuit states and internal battery states, as well as circuit and physical parameters. The proposed model yields an RMS error below 1.46 mV for cell voltage, 0.28% for the surface concentration in the active material particles, 0.6% for the electrode-averaged electrolyte concentration and 0.32 mV for the charge-transfer overpotentials. Another key feature of this model is the relationship between circuit parameters and those identified in frequency-domain tests, which allows us to characterize and validate the model experimentally. We understand that the presented model constitutes an alternative to standard ECMs as well as electrochemical models as it combines advantageous characteristics from both of them.Regional Government of Andalusia P18-RT-3303Spanish Ministry of Science and InnovationFEDER: MCI-20-PID2019-110955RB-I00Principality of Asturias AYUD/2021/50994PU-UGR-Banco SantanderUniversidad de Granada / CBU

Comparative Study on SOC Estimation Techniques for Optimal Battery Sizing for Hybrid Vehicles

Automotive Industry is growing at a very fast rate. Hence problems pertaining to the increasing CO2 levels in the atmosphere and the ever increasing fuel rates also increase. Electric and Hybrid electric technology has become the latest milestone for the automotive industry. In Hybrid Electric Vehicles (HEV), the reliable range of operation is characterized by batteries and battery state of charge (SOC), that describes its remaining capacity, is an important factor for providing the control strategy for the battery management system (BMS) in plug-in hybrid electric vehicles (PHEV) and electric vehicles (EV). Accuracy in estimation of the SOC is necessary not only to protect the battery, prevent it’s over discharge, and improve the battery life but also to allow the application to make rational control strategies to save energy. However, the chemical energy of a battery which is a chemical energy storage source cannot be directly accessed and this issue makes the estimation of the SOC of a battery difficult. Hence, estimation of the SOC accurately becomes very complex and is difficult to implement, as there are parametric uncertainties and the battery models are limited. In fact, in practice several examples of models of the estimation of the SOC are found which have poor accuracy and reliability. Hence a comparative study done in this paper on the various methods will help choose the right method based on the requirements and the application. This paper also reviews a case study on modeling and simulation of one of the methods of SOC estimation and efforts have been put in obtaining the performance of Li-ion batteries by calculating the SOC using Coulomb counting method in MATLAB Simulink. DOI: 10.17762/ijritcc2321-8169.16044

Review on Lithium-Ion battery modeling for different applications

Battery modeling is one of the most important functions in a battery management system for different applications such as electrical vehicles, This article focuses on state of the art of lithium-ion battery modeling by exploring different existing modeling methods, such as Electrochemical models, Analytical models and the equivalent electrical circuit. First, the characteristics of the lithium-ion battery for different applications are reviewed ,we chose to study this type of battery because it offers satisfactory characteristics compared to other battery types, then the different modeling methods have been explored, finaly a conclusion with suggestion of other modeling type such as fractional order model have been proposed to improve efficiency and precision of battery management system

Review on Lithium-Ion battery modeling for different applications

Battery modeling is one of the most important functions in a battery management system for different applications such as electrical vehicles, This article focuses on state of the art of lithium-ion battery modeling by exploring different existing modeling methods, such as Electrochemical models, Analytical models and the equivalent electrical circuit. First, the characteristics of the lithium-ion battery for different applications are reviewed ,we chose to study this type of battery because it offers satisfactory characteristics compared to other battery types, then the different modeling methods have been explored, finaly a conclusion with suggestion of other modeling type such as fractional order model have been proposed to improve efficiency and precision of battery management system

Accuracy versus simplicity in online battery model identification

This paper presents a framework for battery modeling in online, real-time applications where accuracy is important but speed is the key. The framework allows users to select model structures with the smallest number of parameters that is consistent with the accuracy requirements of the target application. The tradeoff between accuracy and speed in a battery model identification process is explored using different model structures and parameter-fitting algorithms. Pareto optimal sets are obtained, allowing a designer to select an appropriate compromise between accuracy and speed. In order to get a clearer understanding of the battery model identification problem, “identification surfaces” are presented. As an outcome of the battery identification surfaces, a new analytical solution is derived for battery model identification using a closed-form formula to obtain a battery’s ohmic resistance and open circuit voltage from measurement data. This analytical solution is used as a benchmark for comparison of other fitting algorithms and it is also used in its own right in a practical scenario for state-of-charge estimation. A simulation study is performed to demonstrate the effectiveness of the proposed framework and the simulation results are verified by conducting experimental tests on a small NiMH battery pack

Estimation of Reduced Order Equivalent Circuit Model Parameters of Batteries from Noisy Current and Voltage Measurements

Identification of reduced order equivalent circuit battery model from current and voltage measurements allows modeling, classification and monitoring of batteries, and these tasks are very essential for battery management systems. This study presents a theoretical study to investigate performance of computer-aided identification of the reduced order equivalent circuit battery model from noisy current and voltage measurement data. The battery model is expressed in the form of fractional order differential equation and time domain numerical solution of this model is numerically calculated according to Grünwald-Letnikov definition of fractional-order derivative. Paper demonstrates an application of this numerical solution so that it can fit noisy current and voltage measurement data, and thus parameters of the equivalent circuit battery model can be estimated. Particle swarm optimization (PSO) method is used to solve this model fitting problem. Performance of the parameter estimation method is investigated for various noise levels of the synthetically generated current and voltage profiles

An accurate time constant parameter determination method for the varying condition equivalent circuit model of lithium batteries.

An accurate estimation of the state of charge for lithium battery depends on an accurate identification of the battery model parameters. In order to identify the polarization resistance and polarization capacitance in a Thevenin equivalent circuit model of lithium battery, the discharge and shelved states of a Thevenin circuit model were analyzed in this paper, together with the basic reasons for the difference in the resistance capacitance time constant and the accurate characterization of the resistance capacitance time constant in detail. The exact mathematical expression of the working characteristics of the circuit in two states were deduced thereafter. Moreover, based on the data of various working conditions, the parameters of the Thevenin circuit model through hybrid pulse power characterization experiment was identified, the simulation model was built, and a performance analysis was carried out. The experiments showed that the accuracy of the Thevenin circuit model can become 99.14% higher under dynamic test conditions and the new identification method that is based on the resistance capacitance time constant. This verifies that this method is highly accurate in the parameter identification of a lithium battery model

Power Electronics and Energy Management for Battery Storage Systems

The deployment of distributed renewable generation and e-mobility systems is creating a demand for improved dynamic performance, flexibility, and resilience in electrical grids. Various energy storages, such as stationary and electric vehicle batteries, together with power electronic interfaces, will play a key role in addressing these requests thanks to their enhanced functionality, fast response times, and configuration flexibility. For the large-scale implementation of this technology, the associated enabling developments are becoming of paramount importance. These include energy management algorithms; optimal sizing and coordinated control strategies of different storage technologies, including e-mobility storage; power electronic converters for interfacing renewables and battery systems, which allow for advanced interactions with the grid; and increase in round-trip efficiencies by means of advanced materials, components, and algorithms. This Special Issue contains the developments that have been published b researchers in the areas of power electronics, energy management and battery storage. A range of potential solutions to the existing barriers is presented, aiming to make the most out of these emerging technologies