Implementation of SOH Estimator in Automotive BMSs Using Recursive Least-Squares

Sung, Woosuk; Lee, Jaejoon

doi:10.3390/electronics8111237

Cited by 10 publications

(6 citation statements)

References 16 publications

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“…Hence, the decision function is obtained by solving Equation (9), and the regression model can be written as…”

Section: Soh Estimation By Ls-svrmentioning

confidence: 99%

“…Among these issues, how to estimate battery health status rapidly is a critical and difficult issue for battery management systems (BMS). The state of health (SOH) indicates a change in the ability of the battery to store and release electrical energy compared to the new battery [7,8], which can directly reflect the aging of the battery [9,10]. Typically, after the actual capacity of the battery drops to 70% or 80% of its nominal capacity, the remaining useful life (RUL) of the battery reaches the end of life (EOL).…”

Section: Introductionmentioning

confidence: 99%

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State of Health Estimation for Lithium-Ion Batteries Based on the Constant Current–Constant Voltage Charging Curve

Xiao

Liu

2020

Electronics

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The state of health is an indicator of battery performance evaluation and service lifetime prediction, which is essential to ensure the reliability and safety of electric vehicles. Although a large number of capacity studies have emerged, there are few simple and effective methods suitable for engineering practice. Hence, a least square support vector regression model with polynomial kernel function is presented for battery capacity estimation. By the battery charging curve, the feature samples of battery health state are extracted. The grey relational analysis is employed for the feature selection, and the K-fold cross-validation is adopted to obtain hyper-parameters of the support vector regression estimation model. To validate this method, the support vector regression estimation model was trained and tested on the battery data sets provided by NASA Prognostics Center of Excellence. The experimental results show that the proposed method only needs some battery feature data, and can achieve high-precision capacity estimation, which indicates that the proposed method shows great efficiency and robustness.

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“…Hence, the decision function is obtained by solving Equation (9), and the regression model can be written as…”

Section: Soh Estimation By Ls-svrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

State of Health Estimation for Lithium-Ion Batteries Based on the Constant Current–Constant Voltage Charging Curve

Xiao

Liu

2020

Electronics

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“…Cell inconsistencies in a LIB pack are a common issue; thus an appropriate BMS is also indispensable for the safe and reliable operation of the LIB pack as well as every single cell of the battery pack [9,10]. A BMS can be designed to serve many functions including but not limited to data acquisition, estimation of the state of charge (SOC) [11,12] and state of health (SOH) [13,14], temperature measurement/estimation [15], cell balancing [16,17], fault detection/diagnosis [18,19] and thermal management [16]. Very recently researchers have started paying attention to the detection/diagnosis of faults after the occurrence of several accidents in e-transportation due to the failure of the LIB system [20,21].…”

Section: Introductionmentioning

confidence: 99%

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

2021

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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.

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“…However, the narrow safe operating area necessitates an effective battery management system (BMS) for almost all practical purposes [1][2][3]. Some of the common functionalities of a BMS includes state of charge (SOC) estimation [4,5] and state of health (SOH) estimation [6,7], temperature measurement/estimation [8], cell balancing [9], fault detection/diagnosis [10] and thermal management [11]. BMS consists of several hardware components such as sensors, microcontrollers and software to perform all these functionalities.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Wireless Battery Management System: Topology, Emerging Trends, and Challenges

Samanta

Williamson

2021

Electronics

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An effective battery management system (BMS) is indispensable for any lithium-ion battery (LIB) powered systems such as electric vehicles (EVs) and stationary grid-tied energy storage systems. Massive wire harness, scalability issue, physical failure of wiring, and high implementation cost and weight are some of the major issues in conventional wired-BMS. One of the promising solutions researchers have come up with is the wireless BMS (WBMS) architecture. Despite research and development on WBMS getting momentum more than a decade ago, it is still in a preliminary stage. Significant further upgradation is required towards developing an industry-ready WBMS, especially for high-power LIB packs. Therefore, an in-depth survey exclusively on WBMS architectures is presented in this article. The aim is to provide a summary of the existing developments as well as to present an informative guide to the research community for future developments by highlighting the issues, emerging trends, and challenges. In-depth analysis of the existing WBMS topologies will not only help the researchers to understand the existing challenges and future research scopes clearly but at the same time enthuse them to focus their research inclination in the domain of WBMS.

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Implementation of SOH Estimator in Automotive BMSs Using Recursive Least-Squares

Cited by 10 publications

References 16 publications

State of Health Estimation for Lithium-Ion Batteries Based on the Constant Current–Constant Voltage Charging Curve

State of Health Estimation for Lithium-Ion Batteries Based on the Constant Current–Constant Voltage Charging Curve

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

A Survey of Wireless Battery Management System: Topology, Emerging Trends, and Challenges

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