scholarly journals Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1309
Author(s):  
Akash Samanta ◽  
Sumana Chowdhuri ◽  
Sheldon S. Williamson
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Battery Management System ◽  
Battery Management ◽  
Knowledge Based ◽  
Future Challenges

Fault detection/diagnosis has become a crucial function of the battery management system (BMS) due to the increasing application of lithium-ion batteries (LIBs) in highly sophisticated and high-power applications to ensure the safe and reliable operation of the system. The application of Machine Learning (ML) in the BMS of LIB has long been adopted for efficient, reliable, accurate prediction of several important states of LIB such as state of charge, state of health and remaining useful life. Inspired by some of the promising features of ML-based techniques over the conventional LIB fault detection/diagnosis methods such as model-based, knowledge-based and signal processing-based techniques, ML-based data-driven methods have been a prime research focus in the last few years. This paper provides a comprehensive review exclusively on the state-of-the-art ML-based data-driven fault detection/diagnosis techniques to provide a ready reference and direction to the research community aiming towards developing an accurate, reliable, adaptive and easy to implement fault diagnosis strategy for the LIB system. Current issues of existing strategies and future challenges of LIB fault diagnosis are also explained for better understanding and guidance.

Fast Fault Diagnosis of a Lithium-Ion Battery for Hybrid Electric Aircraft

2018 ◽  
Author(s):  
Seyed Reza Hashemi ◽  
Ashkan Nazari ◽  
Roja Esmaeeli ◽  
Haniph Aliniagerdroudbari ◽  
Muapper Alhadri ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Lithium Ion ◽  
Battery Management System ◽  
Battery Management ◽  
Sensor Faults ◽  
Battery Cell ◽  
Operational Variables ◽  
Electric Aircraft ◽  
Hybrid Electric ◽  
And Control

A well-designed battery management system along with a set of voltage and current sensors is required to properly measure and control the battery cell operational variables for Hybrid Electric Aircrafts (HEAs). Some critical functions of the battery including State-Of-Charge (SOC) and State-Of-Health (SOH) estimations, over-current, and over-/under-voltage protections are mainly related to current and voltage sensor measurements. Therefore, in case of battery faults occur in HEA, designing a reliable and robust diagnostic procedure is essential. In this study, for Li-ion batteries, a new and fast fault diagnosis technique via collecting data is proposed. Finally, the effectiveness of the proposed diagnostic method is validated, and the results show how overcharge, over-discharge and sensor faults can be accurately detected.

scholarly journals Structural Effects of Anomalous Current Densities on Manganese Hexacyanoferrate for Li-Ion Batteries

2020 ◽  
Vol 10 (21) ◽  
pp. 7573
Author(s):  
Angelo Mullaliu ◽  
Stéphanie Belin ◽  
Lorenzo Stievano ◽  
Marco Giorgetti ◽  
Stefano Passerini
Keyword(s):  
Coulombic Efficiency ◽  
Lithium Ion ◽  
Electrochemical Process ◽  
Remaining Useful Life ◽  
Multivariate Curve Resolution ◽  
Battery Management System ◽  
Battery Management ◽  
Structural Effects ◽  
Current Densities ◽  
Li Ion

A battery management system (BMS) plays a pivotal role in providing optimal performance of lithium-ion batteries (LIBs). However, the eventual malfunction of the BMS may lead to safety hazards or reduce the remaining useful life of LIBs. Manganese hexacyanoferrate (MnHCF) was employed as the positive electrode material in a Li-ion half-cell and subjected to five cycles at high current densities (10 A gMnHCF−1) and to discharge at 0.1 A gMnHCF−1, instead of classical charge/discharge cycling with initial positive polarization at 0.01 A gMnHCF−1, to simulate a current sensor malfunctioning and to evaluate the electrochemical and structural effects on MnHCF. The operando set of spectra at the Mn and Fe K-edges was further analyzed through multivariate curve resolution analysis with an alternating least squares algorithm (MCR–ALS) and extended X-ray absorption fine structure (EXAFS) spectroscopy to investigate the structural modifications arising during cycling after the applied electrochemical protocol. The coulombic efficiency in the first cycle was dramatically affected; however, the local structural environment around each photo absorber recovered during charging. The identification of an additional spectral contribution in the electrochemical process was achieved through MCR-ALS analysis, and the Mn-local asymmetry was thoroughly explored via EXAFS analysis.

A Fast Diagnosis Methodology for Typical Faults of a Lithium-Ion Battery in Electric and Hybrid Electric Aircraft

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Seyed Reza Hashemi ◽  
Roja Esmaeeli ◽  
Ashkan Nazari ◽  
Haniph Aliniagerdroudbari ◽  
Muapper Alhadri ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Battery Pack ◽  
Battery Management System ◽  
Battery Management ◽  
Operational Variables ◽  
Electric Aircraft ◽  
In Series ◽  
Hybrid Electric

Abstract In electric and hybrid-electric aircraft, the battery systems are usually composed of up to thousands of battery cells connected in series or parallel to provide the voltage and power/energy requirements. The inconsistent cells could affect the battery pack and its performance or even endanger electric and hybrid-electric aircraft security; thus, the early fault diagnosis of the battery system is essential. A well-designed battery management system along with a set of reliable voltage and current sensors is required to properly measure and control the cells operational variables in a large battery pack. In this study, based on the battery working mechanism, a new, fast, and robust fault diagnostic scheme is proposed for a lithium-ion battery (LIB) pack that can be employed for applications such as electric and hybrid-electric aircraft. In this method, some faults such as the overcharge, overdischarge occurring in LIB packs can be detected and isolated, based on some predefined factors gained from the battery models in healthy, overcharge, and overdischarge conditions. Finally, the effectiveness of the proposed fast fault diagnosis scheme is experimentally validated with LIBs under a typical flight cycle.

scholarly journals A Data-Driven Predictive Prognostic Model for Lithium-Ion Batteries based on a Deep Learning Algorithm

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 660 ◽  
Author(s):  
Phattara Khumprom ◽  
Nita Yodo
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Learning Algorithms ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Support Vector

Prognostic and health management (PHM) can ensure that a lithium-ion battery is working safely and reliably. The main approach of PHM evaluation of the battery is to determine the State of Health (SoH) and the Remaining Useful Life (RUL) of the battery. The advancements of computational tools and big data algorithms have led to a new era of data-driven predictive analysis approaches, using machine learning algorithms. This paper presents the preliminary development of the data-driven prognostic, using a Deep Neural Networks (DNN) approach to predict the SoH and the RUL of the lithium-ion battery. The effectiveness of the proposed approach was implemented in a case study with a battery dataset obtained from the National Aeronautics and Space Administration (NASA) Ames Prognostics Center of Excellence (PCoE) database. The proposed DNN algorithm was compared against other machine learning algorithms, namely, Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Artificial Neural Networks (ANN), and Linear Regression (LR). The experimental results reveal that the performance of the DNN algorithm could either match or outweigh other machine learning algorithms. Further, the presented results could serve as a benchmark of SoH and RUL prediction using machine learning approaches specifically for lithium-ion batteries application.

scholarly journals Application of Digital Twin in Smart Battery Management Systems

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Wenwen Wang ◽  
Jun Wang ◽  
Jinpeng Tian ◽  
Jiahuan Lu ◽  
Rui Xiong
Keyword(s):  
State Estimation ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Battery Management System ◽  
Battery Management ◽  
Battery Modeling ◽  
Dynamic Thermal Management ◽  
Digital Twin ◽  
New Energy ◽  
And Control

AbstractLithium-ion batteries have always been a focus of research on new energy vehicles, however, their internal reactions are complex, and problems such as battery aging and safety have not been fully understood. In view of the research and preliminary application of the digital twin in complex systems such as aerospace, we will have the opportunity to use the digital twin to solve the bottleneck of current battery research. Firstly, this paper arranges the development history, basic concepts and key technologies of the digital twin, and summarizes current research methods and challenges in battery modeling, state estimation, remaining useful life prediction, battery safety and control. Furthermore, based on digital twin we describe the solutions for battery digital modeling, real-time state estimation, dynamic charging control, dynamic thermal management, and dynamic equalization control in the intelligent battery management system. We also give development opportunities for digital twin in the battery field. Finally we summarize the development trends and challenges of smart battery management.

scholarly journals Future Challenges in State of Charge Estimation for Lithium-Ion Batteries

2020 ◽  
Vol 10 (1) ◽  
pp. 215-223
Keyword(s):  
Energy Storage ◽  
Electric Vehicles ◽  
Storage System ◽  
Lithium Ion ◽  
Energy Storage System ◽  
Online Control ◽  
State Of Charge ◽  
Battery Management System ◽  
Battery Management ◽  
Future Challenges

Energy storage system is an Emerging technology in past few decades. The Energy storage system is an important technology for Electric Vehicles, Hybrid Electric Vehicles (EV) and (HVE) and Micro grid system. The Battery Management System (BMS) is need to be control and monitor the various parameter of the battery such as SOC , SOH, C-Rate, E-Rate ,Temperature , RVL , EOL and so on. However, the (SOC) State of Charge is an important estimation for the online control and BMS monitoring. The SOC is the challenging task when online control and BMS monitoring. This various technique or methods available to estimate the SOC and alsoits represents the Elaboration for various methods of SOC estimation and its drawback. Past five years, where the tendency of the Estimation technique has been oriented towards a mixture of probabilistic techniques and some Artificial Intelligence.

Sign in / Sign up

Export Citation Format

Share Document