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.

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 System protection for lithium-ion batteries management system: a review

2019 ◽  
Vol 13 (3) ◽  
pp. 1184 ◽  
Author(s):  
L. Rimon ◽  
Khairul Safuan Muhammad ◽  
S.I. Sulaiman ◽  
AM Omar
Keyword(s):  
Management System ◽  
Short Circuit ◽  
Lithium Ion ◽  
Cell Voltage ◽  
Battery Management System ◽  
Battery Management ◽  
Battery Cell ◽  
Safety Issues ◽  
System Protection ◽  
Faulty Cell

<span>Robustness of a battery management system (BMS) is a crucial issue especially in critical application such as medical or military. Failure of BMS will lead to more serious safety issues such as overheating, overcharging, over discharging, cell unbalance or even fire and explosion. BMS consists of plenty sensitive electronic components and connected directly to battery cell terminal. Consequently, BMS exposed to high voltage potential across the BMS terminal if a faulty cell occurs in a pack of Li-ion battery. Thus, many protection techniques have been proposed since last three decades to protect the BMS from fault such as open cell voltage fault, faulty cell, internal short circuit etc. This paper presents a review of a BMS focuses on the protection technique proposed by previous researcher. The comparison has been carried out based on circuit topology and fault detection technique</span>

scholarly journals Comparative Study of Equivalent Circuit Models Performance in Four Common Lithium-Ion Batteries: LFP, NMC, LMO, NCA

Batteries ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 51
Author(s):  
Manh-Kien Tran ◽  
Andre DaCosta ◽  
Anosh Mevawalla ◽  
Satyam Panchal ◽  
Michael Fowler
Keyword(s):  
Equivalent Circuit ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Battery Management System ◽  
Battery Management ◽  
Li Ion Batteries ◽  
Li Ion Battery ◽  
Battery Modeling ◽  
Li Ion ◽  
And Control

Lithium-ion (Li-ion) batteries are an important component of energy storage systems used in various applications such as electric vehicles and portable electronics. There are many chemistries of Li-ion battery, but LFP, NMC, LMO, and NCA are four commonly used types. In order for the battery applications to operate safely and effectively, battery modeling is very important. The equivalent circuit model (ECM) is a battery model often used in the battery management system (BMS) to monitor and control Li-ion batteries. In this study, experiments were performed to investigate the performance of three different ECMs (1RC, 2RC, and 1RC with hysteresis) on four Li-ion battery chemistries (LFP, NMC, LMO, and NCA). The results indicated that all three models are usable for the four types of Li-ion chemistries, with low errors. It was also found that the ECMs tend to perform better in dynamic current profiles compared to non-dynamic ones. Overall, the best-performed model for LFP and NCA was the 1RC with hysteresis ECM, while the most suited model for NMC and LMO was the 1RC ECM. The results from this study showed that different ECMs would be suited for different Li-ion battery chemistries, which should be an important factor to be considered in real-world battery and BMS applications.

scholarly journals Ensemble Surrogate Models for Fast LIB Performance Predictions

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4115
Author(s):  
Marco Quartulli ◽  
Amaia Gil ◽  
Ane Miren Florez-Tapia ◽  
Pablo Cereijo ◽  
Elixabete Ayerbe ◽  
...  
Keyword(s):  
Surrogate Models ◽  
Battery Management System ◽  
Battery Management ◽  
Battery Cell ◽  
Battery Modeling ◽  
Wide Range ◽  
Learning Ensembles ◽  
The One ◽  
Battery Energy ◽  
And Control

Battery Cell design and control have been widely explored through modeling and simulation. On the one hand, Doyle’s pseudo-two-dimensional (P2D) model and Single Particle Models are among the most popular electrochemical models capable of predicting battery performance and therefore guiding cell characterization. On the other hand, empirical models obtained, for example, by Machine Learning (ML) methods represent a simpler and computationally more efficient complement to electrochemical models and have been widely used for Battery Management System (BMS) control purposes. This article proposes ML-based ensemble models to be used for the estimation of the performance of an LIB cell across a wide range of input material characteristics and parameters and evaluates 1. Deep Learning ensembles for simulation convergence classification and 2. structured regressors for battery energy and power predictions. The results represent an improvement on state-of-the-art LIB surrogate models and indicate that deep ensembles represent a promising direction for battery modeling and design.

scholarly journals Lithium-Ion Battery Cell-Balancing Algorithm for Battery Management System Based on Real-Time Outlier Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Changhao Piao ◽  
Zhaoguang Wang ◽  
Ju Cao ◽  
Wei Zhang ◽  
Sheng Lu
Keyword(s):  
Lithium Ion Battery ◽  
Outlier Detection ◽  
Management System ◽  
Lithium Ion ◽  
Battery Pack ◽  
Battery Management System ◽  
Battery Management ◽  
Battery Cell ◽  
Online Clustering ◽  
Cell Balancing

A novel cell-balancing algorithm which was used for cell balancing of battery management system (BMS) was proposed in this paper. Cell balancing algorithm is a key technology for lithium-ion battery pack in the electric vehicle field. The distance-based outlier detection algorithm adopted two characteristic parameters (voltage and state of charge) to calculate each cell’s abnormal value and then identified the unbalanced cells. The abnormal and normal type of battery cells were acquired by online clustering strategy and bleeding circuits (R= 33 ohm) were used to balance the abnormal cells. The simulation results showed that with the proposed balancing algorithm, the usable capacity of the battery pack increased by 0.614 Ah (9.5%) compared to that without balancing.

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.

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.

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