scholarly journals Perspective on State-of-Health Determination in Lithium-Ion Batteries

2020 ◽  
Vol 17 (4) ◽  
Author(s):  
Matthieu Dubarry ◽  
George Baure ◽  
David Anseán
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Battery Management ◽  
State Of Health ◽  
Second Stage ◽  
Capacity Loss ◽  
Battery Management Systems ◽  
Essential Parameter ◽  
Battery Packs ◽  
Deployed Systems

Abstract State-of-health (SOH) is an essential parameter for the proper functioning of large battery packs. A wide array of methodologies has been proposed in the literature to track state of health, but they often lack the proper validation that needed to be universally adaptable to large deployed systems. This is likely induced by the lack of knowledge bridge between scientists, who understand batteries, and engineers, who understand controls. In this work, we will attempt to bridge this gap by providing definitions, concepts, and tools to apply necessary material science knowledge to advanced battery management systems (BMS). We will address SOH determination and prediction, as well as BMS implementation and validation using the mechanistic framework developed around electrochemical voltage spectroscopies. Particular focus will be set on the onset and the prediction of the second stage of accelerating capacity loss that is commonly observed in commercial lithium-ion batteries.

State-of-Health Online Estimation for Li-Ion Battery

2020 ◽  
Vol 9 (2) ◽  
pp. 185-196
Author(s):  
Liu Fang ◽  
◽  
Liu Xinyi ◽  
Su Weixing ◽  
Chen Hanning ◽  
...  
Keyword(s):  
Estimation Method ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Estimation Methods ◽  
Battery Management ◽  
State Of Health ◽  
Li Ion Battery ◽  
Improved Genetic Algorithm ◽  
Battery Management Systems ◽  
Li Ion

To realize a fast and high-precision online state-of-health (SOH) estimation of lithium-ion (Li-Ion) battery, this article proposes a novel SOH estimation method. This method consists of a new SOH model and parameters identification method based on an improved genetic algorithm (Improved-GA). The new SOH model combines the equivalent circuit model (ECM) and the data-driven model. The advantages lie in keeping the physical meaning of the ECM while improving its dynamic characteristics and accuracy. The improved-GA can effectively avoid falling into a local optimal problem and improve the convergence speed and search accuracy. So the advantages of the SOH estimation method proposed in this article are that it only relies on battery management systems (BMS) monitoring data and removes many assumptions in some other traditional ECM-based SOH estimation methods, so it is closer to the actual needs for electric vehicle (EV). By comparing with the traditional ECM-based SOH estimation method, the algorithm proposed in this article has higher accuracy, fewer identification parameters, and lower computational complexity.

High End Battery Management Systems for Renewable Energy and EV Applications

Green ◽  
2013 ◽  
Vol 3 (1) ◽  
Author(s):  
Max Jung ◽  
Simon Schwunk
Keyword(s):  
Lithium Ion ◽  
Life Time ◽  
High Energy ◽  
High Energy Density ◽  
Management Systems ◽  
Battery Management ◽  
Wind Generators ◽  
Battery Management Systems ◽  
Battery Systems ◽  
Battery Packs

AbstractUsing renewable energies means having to deal with a strongly stochastic behaviour, since for photovoltaics the sun has to shine or for wind generators the wind has to blow. For being able to supply the load any time, storage solutions are needed. Decreasing costs and better availabilities of new battery technologies like lithium-ion therefore result in a growing demand for more sophisticated battery systems in off-grid and grid connected applications. In e.g. off-grid applications, lead-acid battery systems are state of the art. Though, lithium-ion batteries become more popular because of their high energy density and long life time. Another application for electrochemical storage systems are electric vehicles. In all those cases the battery storages need to be managed. But the management of a battery system is not a trivial problem. The batteries must be monitored and controlled, there are challenges regarding safety, electrical isolation and energy efficiency. The article gives an introduction to different architectures of battery management systems (BMS). There are different approaches to design a BMS the article describes in the first part. In the second part, there is a more precise description of the electronic hardware and the software behind a BMS. To understand both function and importance of a BMS, the article introduces in the third part a few applications of BMS in bigger battery packs.

State Estimation for an Electrochemical Model of Multiple-Material Lithium-Ion Batteries

2016 ◽  
Author(s):  
Leobardo Camacho-Solorio ◽  
Miroslav Krstic ◽  
Reinhardt Klein ◽  
Anahita Mirtabatabaei ◽  
Scott J. Moura
Keyword(s):  
State Estimation ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Particle Model ◽  
Battery Management ◽  
Active Materials ◽  
Energy Characteristics ◽  
Battery Management Systems ◽  
Power And Energy ◽  
Electrochemical Model

This paper presents state estimation for a system of diffusion equations coupled in the boundary appearing in reduced electrochemical models of lithium-ion batteries with multiple active materials in single electrodes. The observer is synthesized from a single particle model and is based on the backstepping method for partial differential equations. The observer is suitable for state of charge estimation in battery management systems and is an extension of existing backstepping observers which were derived only for cells with electrodes of single active materials. Observer gains still can be computed analytically in terms of Bessel and modified Bessel functions. This extension is motivated by the trend in cell manufacturing to use multiple active materials to combine power and energy characteristics or reduce degradation.

Dynamic overcharge investigations of lithium ion batteries with different state of health

2021 ◽  
Vol 507 ◽  
pp. 230262
Author(s):  
Lei Feng ◽  
Lihua Jiang ◽  
Jialong Liu ◽  
Zhaoyu Wang ◽  
Zesen Wei ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
State Of Health

scholarly journals Determination of the Distribution of Relaxation Times by Means of Pulse Evaluation for Offline and Online Diagnosis of Lithium-Ion Batteries

Batteries ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 36
Author(s):  
Erik Goldammer ◽  
Julia Kowal
Keyword(s):  
Lithium Ion Batteries ◽  
Electrochemical Impedance ◽  
Sampling Rate ◽  
Relaxation Times ◽  
Low Frequency ◽  
Lithium Ion ◽  
Battery Management ◽  
Time Constants ◽  
Time Domain Data ◽  
Aging Study

The distribution of relaxation times (DRT) analysis of impedance spectra is a proven method to determine the number of occurring polarization processes in lithium-ion batteries (LIBs), their polarization contributions and characteristic time constants. Direct measurement of a spectrum by means of electrochemical impedance spectroscopy (EIS), however, suffers from a high expenditure of time for low-frequency impedances and a lack of general availability in most online applications. In this study, a method is presented to derive the DRT by evaluating the relaxation voltage after a current pulse. The method was experimentally validated using both EIS and the proposed pulse evaluation to determine the DRT of automotive pouch-cells and an aging study was carried out. The DRT derived from time domain data provided improved resolution of processes with large time constants and therefore enabled changes in low-frequency impedance and the correlated degradation mechanisms to be identified. One of the polarization contributions identified could be determined as an indicator for the potential risk of plating. The novel, general approach for batteries was tested with a sampling rate of 10 Hz and only requires relaxation periods. Therefore, the method is applicable in battery management systems and contributes to improving the reliability and safety of LIBs.

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