A Self-Cognizant Dynamic System Approach for Health Management: Lithium-Ion Battery Case Study

2014 ◽  
Author(s):  
Guangxing Bai ◽  
Pingfeng Wang
Keyword(s):  
Dynamic System ◽  
Lithium Ion Battery ◽  
Physical Model ◽  
System Approach ◽  
Health Management ◽  
Lithium Ion ◽  
Physical Models ◽  
Accurate Estimation ◽  
Ann Model ◽  
Energy Storage Devices

Safe and reliable operation of lithium-ion batteries as major energy storage devices is of vital importance, as unexpected battery failures could result in enormous economic and societal losses. Accurate estimation of the state-of-charge (SoC) and state-of-health (SoH) for an operating battery system, as a critical task for battery health management, greatly depends on the validity and generalizability of battery models. Due to the variability and uncertainties involved in battery design, manufacturing, and operation, developing a generally applicable battery physical model is a big challenge. To eliminate the dependency of SoC and SoH estimation on battery physical models, this paper presents a generic self-cognizant dynamic system approach for lithium-ion battery health management, which integrates an artificial neural network (ANN) with a dual extended Kalman filter (DEKF) algorithm. The ANN is trained offline to model the battery terminal voltages to be used by the DEKF. With the trained ANN, the DEKF algorithm is then employed online for SoC and SoH estimation, where voltage outputs from the trained ANN model are used in DEKF state-space equations to replace the battery physical model. Experimental results are used to demonstrate the effectiveness of the developed self-cognizant dynamic system approach for battery health management.

scholarly journals Effect of dynamic loads and vibrations on lithium-ion batteries

2021 ◽  
pp. 146134842110081
Author(s):  
Xia Hua ◽  
Alan Thomas
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Experimental Studies ◽  
Lithium Ion ◽  
Battery Pack ◽  
Dynamic Loads ◽  
Electrical Performance ◽  
Mechanical Degradation ◽  
Energy Storage Devices ◽  
Battery Cell

Lithium-ion batteries are being increasingly used as the main energy storage devices in modern mobile applications, including modern spacecrafts, satellites, and electric vehicles, in which consistent and severe vibrations exist. As the lithium-ion battery market share grows, so must our understanding of the effect of mechanical vibrations and shocks on the electrical performance and mechanical properties of such batteries. Only a few recent studies investigated the effect of vibrations on the degradation and fatigue of battery cell materials as well as the effect of vibrations on the battery pack structure. This review focused on the recent progress in determining the effect of dynamic loads and vibrations on lithium-ion batteries to advance the understanding of lithium-ion battery systems. Theoretical, computational, and experimental studies conducted in both academia and industry in the past few years are reviewed herein. Although the effect of dynamic loads and random vibrations on the mechanical behavior of battery pack structures has been investigated and the correlation between vibration and the battery cell electrical performance has been determined to support the development of more robust electrical systems, it is still necessary to clarify the mechanical degradation mechanisms that affect the electrical performance and safety of battery cells.

A flame-retardant polyimide interlayer with polysulfide lithium traps and fast redox conversion towards safety and high sulfur utilization Li-S batteries

Nanoscale ◽  
2022 ◽  
Author(s):  
Zhiyu Zhou ◽  
Zexiang Chen ◽  
Yang Zhao ◽  
Huifang Lv ◽  
Hualiang Wei ◽  
...  
Keyword(s):  
Energy Storage ◽  
Flame Retardant ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Energy Storage Devices ◽  
Storage Devices ◽  
Battery Technology ◽  
Redox Conversion ◽  
Lithium Sulfur ◽  
Made In

In recent years and following the progress made in lithium-ion battery technology, substantial efforts have been devoted to developing practical lithium-sulfur (Li–S) batteries for next-generation commercial energy storage devices. The...

scholarly journals Research on molecular energy storage

2021 ◽  
Vol 1 (3) ◽  
pp. 49-56
Author(s):  
S.M. Zuyev ◽  
◽  
R.A. Maleyev ◽  
YU.M. Shmatkov ◽  
M.YU. Khandzhalov ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Energy Storage ◽  
Lithium Ion Battery ◽  
Double Layer ◽  
Lithium Ion ◽  
Storage Device ◽  
Energy Storage Device ◽  
Energy Storage Devices ◽  
Storage Devices ◽  
Advantages And Disadvantages

This article provides a comparative analysis of various energy storage devices. A detailed review and analysis of molecular energy storage units is carried out, their main characteristics and parame-ters, as well as their application areas, are determined. The main types of molecular energy storage are determined: electric double layer capacitors, pseudo capacitors, hybrid capacitors. Comparison of the characteristics of various batteries is given. The parameters of various energy storage devices are presented. The analysis of molecular energy storage devices and accumulators is carried out. Ttheir advantages and disadvantages are revealed. It has been shown that molecular energy storage or double layer electrochemical capacitors are ideal energy storage systems due to their high specific energy, fast charging and long life compared to conventional capacitors. The article presents oscillograms of a lithium-ion battery with a voltage of 10.8 V at a pulsed load current of 2A of a laptop with and without a molecular energy storage device, as well as oscil-lograms of a laptop with DVD lithium-ion battery with a voltage of 10.8 V with a parallel shutdown of a molecular energy storage device with a capacity of 7 F and without it. The comparative analysis shows that when the molecular energy storage unit with a 7 F capacity is switched on and off, transient processes are significantly improved and there are no supply voltage dips. The dependenc-es of the operating time of a 3.6 V 600 mAh lithium-ion battery at a load of 2 A for powering mo-bile cellular devices with and without a molecular energy storage are given. It is shown that when the molecular energy storage device is switched on, the battery operation time increases by almost 20%.

scholarly journals An Overview of Lithium-Ion Battery Cathode Materials

2011 ◽  
Vol 1363 ◽  
Author(s):  
Yixu Wang ◽  
Hsiao-Ying Shadow Huang
Keyword(s):  
Energy Storage ◽  
Lithium Ion Battery ◽  
Cathode Materials ◽  
Lithium Iron Phosphate ◽  
Lithium Ion ◽  
Iron Phosphate ◽  
High Energy ◽  
Rechargeable Batteries ◽  
Environmental Benefits ◽  
Energy Storage Devices

ABSTRACTThe need for the development and deployment of reliable and efficient energy storage devices, such as lithium-ion rechargeable batteries, is becoming increasingly important due to the scarcity of petroleum. In this work, we provide an overview of commercially available cathode materials for Li-ion rechargeable batteries and focus on characteristics that give rise to optimal energy storage systems for future transportation modes. The study shows that the development of lithium-iron-phosphate (LiFePO4) batteries promises an alternative to conventional lithiumion batteries, with their potential for high energy capacity and power density, improved safety, and reduced cost. This work contributes to the fundamental knowledge of lithium-ion battery cathode materials and helps with the design of better rechargeable batteries, and thus leads to economic and environmental benefits.

scholarly journals Estimation of State of Charge for Lithium-Ion Battery Based on Finite Difference Extended Kalman Filter

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Ze Cheng ◽  
Jikao Lv ◽  
Yanli Liu ◽  
Zhihao Yan
Keyword(s):  
Kalman Filter ◽  
Finite Difference ◽  
Lithium Ion Battery ◽  
Extended Kalman Filter ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Nonlinear Dynamic System ◽  
State Of Charge ◽  
Energy Utilization ◽  
Accurate Estimation

An accurate estimation of the state of charge (SOC) of the battery is of great significance for safe and efficient energy utilization of electric vehicles. Given the nonlinear dynamic system of the lithium-ion battery, the parameters of the second-order RC equivalent circuit model were calibrated and optimized using a nonlinear least squares algorithm in the Simulink parameter estimation toolbox. A comparison was made between this finite difference extended Kalman filter (FDEKF) and the standard extended Kalman filter in the SOC estimation. The results show that the model can essentially predict the dynamic voltage behavior of the lithium-ion battery, and the FDEKF algorithm can maintain good accuracy in the estimation process and has strong robustness against modeling error.

scholarly journals SOC Estimation of Lithium-Ion Battery Based on Kalman Filter Algorithm for Energy Storage System in Microgrids

2018 ◽  
Vol 57 ◽  
pp. 02006 ◽  
Author(s):  
Dae-Won Chung ◽  
Seung-Hak Yang
Keyword(s):  
Kalman Filter ◽  
Energy Storage ◽  
Power Systems ◽  
Lithium Ion Battery ◽  
Storage System ◽  
Lithium Ion ◽  
Energy Storage System ◽  
Accurate Estimation ◽  
Soc Estimation ◽  
Internal States

State-of-charge (SOC) is one of the vital factors for the energy storage system (ESS) in the microgrid power systems to guarantee that a battery system is operating in a safe and reliable manner for the system. Many uncertainties and noises, such as nonlinearities in the internal states of a battery, sensor measurement accuracy and bias, temperature effects, calibration errors, and sensor failures, pose a challenge to the accurate estimation of SOC in most applications. This study makes two contributions to the existing literatures. First, a more accurate extended Kalman filter (EKF) algorithm is proposed to estimate the battery nonlinear dynamics. Due to its discrete form and ease of implementation, this straightforward approach could be more suitable for real applications on the ESS. Second, its order selection principle and parameter identification method are illustrated in detail. It can accurately demonstrate the characteristics of the lithium-ion battery to show the feasibility and effectiveness of the algorithm for the ESS.

scholarly journals A Lithium-ion Battery RUL Prediction Method Considering the Capacity Regeneration Phenomenon

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2247 ◽  
Author(s):  
Xiaoqiong Pang ◽  
Rui Huang ◽  
Jie Wen ◽  
Yuanhao Shi ◽  
Jianfang Jia ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Health Management ◽  
Prediction Method ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Multi Scale ◽  
Battery Capacity ◽  
Rest Time ◽  
Auto Regressive

Prediction of Remaining Useful Life (RUL) of lithium-ion batteries plays a significant role in battery health management. Battery capacity is often chosen as the Health Indicator (HI) in research on lithium-ion battery RUL prediction. In the rest time of batteries, capacity will produce a certain degree of regeneration phenomenon, which exists in the use of each battery. Therefore, considering the capacity regeneration phenomenon in RUL prediction of lithium-ion batteries is helpful to improve the prediction performance of the model. In this paper, a novel method fusing the wavelet decomposition technology (WDT) and the Nonlinear Auto Regressive neural network (NARNN) model for predicting the RUL of a lithium-ion battery is proposed. Firstly, the multi-scale WDT is used to separate the global degradation and local regeneration of a battery capacity series. Then, the RUL prediction framework based on the NARNN model is constructed for the extracted global degradation and local regeneration. Finally, the two parts of the prediction results are combined to obtain the final RUL prediction result. Experiments show that the proposed method can not only effectively capture the capacity regeneration phenomenon, but also has high prediction accuracy and is less affected by different prediction starting points.

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