A comparison of online electrochemical spectroscopy impedance estimation of batteries

This paper compares methods of undertaking on-line electrochemical impedance spectroscopy that have been published in literature. This work describes the different published methodologies and sorts these into categories. The paper looks at the theoretical analysis of the circuits and control techniques and follows up with simulation and/or experimental studies of these methods. This work focuses on battery systems

E-transportation: the role of embedded systems in electric energy transfer from grid to vehicle

Electric vehicles (EVs) are a promising solution to reduce the transportation dependency on oil, as well as the environmental concerns. Realization of E-transportation relies on providing electrical energy to the EVs in an effective way. Energy storage system (ESS) technologies, including batteries and ultra-capacitors, have been significantly improved in terms of stored energy and power. Beside technology advancements, a battery management system is necessary to enhance safety, reliability and efficiency of the battery. Moreover, charging infrastructure is crucial to transfer electrical energy from the grid to the EV in an effective and reliable way. Every aspect of E-transportation is permeated by the presence of an intelligent hardware platform, which is embedded in the vehicle components, provided with the proper interfaces to address the communication, control and sensing needs. This embedded system controls the power electronics devices, negotiates with the partners in multi-agent scenarios, and performs fundamental tasks such as power flow control and battery management. The aim of this paper is to give an overview of the open challenges in E-transportation and to show the fundamental role played by embedded systems. The conclusion is that transportation electrification cannot fully be realized without the inclusion of the recent advancements in embedded systems

A Study on Remaining Useful Life Prediction for Prognostic Applications

We consider the prediction algorithm and performance evaluation for prognostics and health management (PHM) problems, especially the prediction of remaining useful life (RUL) for the milling machine cutter and lithium ‐ ion battery. We modeled battery as a voltage source and internal resisters. By analyzing voltage change trend during discharge, we made the prediction of battery remain discharge time in one discharge cycle. By analyzing internal resistance change trend during multiple cycles, we were able to predict the battery remaining useful time during its life time. We showed that the battery rest profile is correlated with the RUL. Numerical results using the realistic battery aging data from NASA prognostics data repository yielded satisfactory performance for battery prognosis as measured by certain performance metrics. We built a battery test platform and simulated more usage pattern and verified the prediction algorithm. Prognostic performance metrics were used to compare different algorithms

Advances in Batteries, Battery Modeling, Battery Management System, Battery Thermal Management, SOC, SOH, and Charge/Discharge Characteristics in EV Applications

The second-generation hybrid and Electric Vehicles are currently leading the paradigm shift in the automobile industry, replacing conventional diesel and gasoline-powered vehicles. The Battery Management System is crucial in these electric vehicles and also essential for renewable energy storage systems. This review paper focuses on batteries and addresses concerns, difficulties, and solutions associated with them. It explores key technologies of Battery Management System, including battery modeling, state estimation, and battery charging. A thorough analysis of numerous battery models, including electric, thermal, and electro-thermal models, is provided in the article. Additionally, it surveys battery state estimations for a charge and health. Furthermore, the different battery charging approaches and optimization methods are discussed. The Battery Management System performs a wide range of tasks, including as monitoring voltage and current, estimating charge and discharge, equalizing and protecting the battery, managing temperature conditions, and managing battery data. It also looks at various cell balancing circuit types, current and voltage stressors, control reliability, power loss, efficiency, as well as their advantages and disadvantages. The paper also discusses research gaps in battery management systems.publishedVersio

Review of Power Converter Topologies for Electrochemical Impedance Spectroscopy of Lithium-Ion Batteries

Frequency domain impedance of Li-ion batteries contains valuable information about the state of charge (SOC) and state of health (SOH). Normally, electrochemical impedance spectroscopy (EIS) is performed during the relaxation of battery cells. However, performing EIS during the batteries operation has been achieved through switching power converters. This paper reviews the power converter topologies for both online and offline Electrochemical Impedance Spectroscopy (EIS) characterization of batteries. The information that can be extracted from EIS Nyquist plots are discussed. Comparative analysis between converter topologies is presented. Finally, challenges are identified and new converter topologies are proposed for further consideration in online/offline EIS characterization

Predicting the Batteries' State of Health in Wireless Sensor Networks Applications

[EN] The lifetime of wireless sensor networks deployments depends strongly on the nodes battery state of health (SoH). It is important to detect promptly those motes whose batteries are affected and degraded by ageing, environmental conditions, failures, etc. There are several parameters that can provide significant information of the battery SoH, such as the number of charge/discharge cycles, the internal resistance, voltage, drained current, temperature, etc. The combination of these parameters can be used to generate analytical models capable of predicting the battery SoH. The generation of these models needs a previous process to collect dense data traces with sampled values of the battery parameters during a large number of discharge cycles under different operating conditions. The collected data allow the development of mathematical models that can predict the battery SoH. These models are required to be simple because they must be executed in motes with low computational capabilities. The paper shows the complete process of acquiring the training data, the models generation and its experimental validation using rechargeable batteries connected to Telosb motes. The obtained results provide significant insight of the battery SoH at different temperatures and charge/discharge cycles.This work was supported in part by the Spanish MINECO under Grant BIA2016-76957-C3-1-R and in part by the I+D+i Program of the Generalitat Valenciana, Spain, under Grant AICO/2016/046.Lajara Vizcaino, JR.; Perez Solano, JJ.; Pelegrí Sebastiá, J. (2018). Predicting the Batteries' State of Health in Wireless Sensor Networks Applications. IEEE Transactions on Industrial Electronics. 65(11):8936-8945. https://doi.org/10.1109/TIE.2018.2808925S89368945651

Online impedance spectroscopy estimation of a dc–dc converter connected battery using a switched capacitor-based balancing circuit

This study investigates a novel method of undertaking online electrochemical impedance spectroscopy measurements to estimate battery impedance across the frequency range using a battery balancing circuit. A switched capacitor balancing system is used to generate an excitation signal of low-frequency of variable values from which battery voltage and current can be measured to estimate the impedance

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