scholarly journals Ultrasonic Health Monitoring of Lithium-Ion Batteries

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 751 ◽  
Author(s):  
Yi Wu ◽  
Youren Wang ◽  
Winco K. C. Yung ◽  
Michael Pecht
Keyword(s):  
Data Fusion ◽  
Experimental Studies ◽  
Lithium Ion ◽  
Battery Management ◽  
Early Failure ◽  
Battery Management Systems ◽  
Ultrasonic Sensing ◽  
Battery Degradation ◽  
Fusion Analysis ◽  
Fusion Approach

Because of the complex physiochemical nature of the lithium-ion battery, it is difficult to identify the internal changes that lead to battery degradation and failure. This study develops an ultrasonic sensing technique for monitoring the commercial lithium-ion pouch cells and demonstrates this technique through experimental studies. Data fusion analysis is implemented using the ultrasonic sensing data to construct a new battery health indicator, thus extending the capabilities of traditional battery management systems. The combination of the ultrasonic sensing and data fusion approach is validated and shown to be effective for degradation assessment as well as early failure indication.

scholarly journals Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3284
Author(s):  
Ingvild B. Espedal ◽  
Asanthi Jinasena ◽  
Odne S. Burheim ◽  
Jacob J. Lamb
Keyword(s):  
Electric Vehicles ◽  
Lithium Ion ◽  
Research Area ◽  
State Of Charge ◽  
Stable Operation ◽  
Battery Management ◽  
Soc Estimation ◽  
Current Trends ◽  
Battery Management Systems ◽  
Active Research

Energy storage systems (ESSs) are critically important for the future of electric vehicles. Despite this, the safety and management of ESSs require improvement. Battery management systems (BMSs) are vital components in ESS systems for Lithium-ion batteries (LIBs). One parameter that is included in the BMS is the state-of-charge (SoC) of the battery. SoC has become an active research area in recent years for battery electric vehicle (BEV) LIBs, yet there are some challenges: the LIB configuration is nonlinear, making it hard to model correctly; it is difficult to assess internal environments of a LIB (and this can be different in laboratory conditions compared to real-world conditions); and these discrepancies can lead to raising the instability of the LIB. Therefore, further advancement is required in order to have higher accuracy in SoC estimation in BEV LIBs. SoC estimation is a key BMS feature, and precise modeling and state estimation will improve stable operation. This review discusses current methods use in BEV LIB SoC modelling and estimation. The review culminates in a brief discussion of challenges in BEV LIB SoC prediction analysis.

scholarly journals Effects of cycling on lithium-ion battery hysteresis and overvoltage

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
V. J. Ovejas ◽  
A. Cuadras
Keyword(s):  
Mobile Applications ◽  
Open Circuit Voltage ◽  
Graphite Electrode ◽  
Lithium Ion ◽  
Open Circuit ◽  
Battery Management ◽  
Cell Efficiency ◽  
Battery Management Systems ◽  
Pure Phases ◽  
Determining Factor

Abstract Currently, lithium-ion batteries are widely used as energy storage systems for mobile applications. However, a better understanding of their nature is still required to improve battery management systems (BMS). Overvoltages and open-circuit voltage (OCV) hysteresis provide valuable information regarding battery performance, but estimations of these parameters are generally inaccurate, leading to errors in BMS. Studies on hysteresis are commonly avoided because the hysteresis depends on the state of charge and degradation level and requires time-consuming measurements. We have investigated hysteresis and overvoltages in Li(NiMnCo)O2/graphite and LiFePO4/graphite commercial cells. Here we report a direct relationship between an increase in OCV hysteresis and an increase in charge overvoltage when the cells are degraded by cycling. We find that the hysteresis is related to diffusion and increases with the formation of pure phases, being primarily related to the graphite electrode. These findings indicate that the graphite electrode is a determining factor for cell efficiency.

scholarly journals A Data-Driven Framework to Predict Lithium-Ion Battery Cell Imbalance for Real-Time Battery Management Systems

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8492
Author(s):  
Chao Li ◽  
Assimina A. Pelegri
Keyword(s):  
Real Time ◽  
Lithium Ion ◽  
Training Data ◽  
Data Driven ◽  
Management Systems ◽  
Battery Management ◽  
Battery Cell ◽  
Operational Conditions ◽  
Battery Management Systems ◽  
Battery Cells

Models that can predict battery cells’ thermal and electrical behaviors are necessary for real-time battery management systems to regulate the imbalance within battery cells. This work introduces a Gaussian Process Regression (GPR)-based data-driven framework that succeeds the Multi-Scale Multi-Dimensional (MSMD) modeling structure. The framework can make highly accurate predictions at the same level as full-order full-distribution simulations based on MSMD. A pseudo-2D model is used to generate training data and is combined with a process that shifts computation burdens from real-time battery management systems to lab data preparation. The testing results highlight the reliability of the GPR-based data-driven framework in terms of accuracy and stability under various operational conditions.

scholarly journals Robust Baseline-Free Damage Localization by Using Locally Perturbed Dynamic Equilibrium and Data Fusion Technique

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5964
Author(s):  
Shancheng Cao ◽  
Huajiang Ouyang ◽  
Chao Xu
Keyword(s):  
Data Fusion ◽  
Mode Shape ◽  
Structural Damage ◽  
Dynamic Equilibrium ◽  
Experimental Studies ◽  
Measurement Noise ◽  
Mode Shapes ◽  
Damage Localization ◽  
Shape Estimation ◽  
Fusion Approach

Mode shape-based structural damage identification methods have been widely investigated due to their good performances in damage localization. Nevertheless, the evaluation of mode shapes is severely affected by the measurement noise. Moreover, the conventional mode shape-based damage localization methods are normally proposed based on a certain mode and not effective for multi-damage localization. To tackle these problems, a novel damage localization approach is proposed based on locally perturbed dynamic equilibrium and data fusion approach. The main contributions cover three aspects. Firstly, a joint singular value decomposition technique is proposed to simultaneously decompose several power spectral density transmissibility matrices for robust mode shape estimation, which statistically deals better with the measurement noise than the traditional transmissibility-based methods. Secondly, with the identified mode shapes, an improved pseudo-excitation method is proposed to construct a baseline-free damage localization index by quantifying the locally damage perturbed dynamic equilibrium without the knowledge of material/structural properties. Thirdly, to circumvent the conflicting damage information in different modes and integrate it for robust damage localization, a data fusion scheme is developed, which performs better than the Bayesian fusion approach. Both numerical and experimental studies of cantilever beams with two cracks were conducted to validate the feasibility and effectiveness of the proposed damage localization method. It was found that the proposed method outperforms the traditional transmissibility-based methods in terms of localization accuracy and robustness.

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.

scholarly journals Comparative Analysis of Lithium-Ion Battery Resistance Estimation Techniques for Battery Management Systems

Energies ◽  
2018 ◽  
Vol 11 (6) ◽  
pp. 1490 ◽  
Author(s):  
Manoj Mathew ◽  
Stefan Janhunen ◽  
Mahir Rashid ◽  
Frank Long ◽  
Michael Fowler
Keyword(s):  
Comparative Analysis ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Management Systems ◽  
Battery Management ◽  
Estimation Techniques ◽  
Battery Management Systems

scholarly journals New Cell Balancing Charging System Research for Lithium-ion Batteries

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1393
Author(s):  
Chan-Yong Zun ◽  
Sang-Uk Park ◽  
Hyung-Soo Mok
Keyword(s):  
High Capacity ◽  
Lithium Ion ◽  
High Energy ◽  
High Energy Density ◽  
Battery Management ◽  
Battery Cell ◽  
Charging System ◽  
Charging Time ◽  
Battery Management Systems ◽  
Cell Balancing

With recent advancements in the electrical industry, the demand for high capacity and high energy density batteries has increased, subsequently increasing the demand for fast and reliable battery charging. A battery is an assembly of a plurality of cells, in which maintaining a balance between neighboring cells is crucial for stable charging. To this end, various methods have been applied to battery management systems. Representative methods for maintaining the balance in battery cells include a passive method of adjusting the balance using a resistor and an active method involving the exchange of energy between the cells. However, these methods are limited in terms of efficiency, lifespan, and charging time. Therefore, in this study, we propose a new charging method at the battery cell level and demonstrate its effectiveness through experiments.

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.

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