State-of-health estimation for lithium battery in electric vehicles based on improved unscented particle filter

Shi, Enwei; Xia, Fei; Peng, Daogang; Li, Liang; Wang, Xiaokang; Yu, Beili

doi:10.1063/1.5065477

Cited by 37 publications

(12 citation statements)

References 42 publications

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“…Tracking these aging characteristics, they estimated SOH and RUL with a high accuracy compared to an artificial neural network-based model. Likewise, references [67,68] developed a promising modified PF algorithm that avoids particle degradation. For example, Shi et al [68] demonstrated that improved unscented PF (IUPF) had better accuracy than unscented Kalman filter (UKF) and unscented particle filter (UPF) model prediction of ohmic internal resistance (R o ) and SOH.…”

Section: Gray-box Methods Are Hybrid Prognostics Between White and Blmentioning

confidence: 99%

A Circular Economy of Electrochemical Energy Storage Systems: Critical Review of SOH/RUL Estimation Methods for Second-Life Batteries

Montoya-Bedoya¹,

Sabogal-Moncada²,

Garcia-Tamayo³

et al. 2020

Green Energy and Environment

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Humanity is facing a gloomy scenario due to global warming, which is increasing at unprecedented rates. Energy generation with renewable sources and electric mobility (EM) are considered two of the main strategies to cut down emissions of greenhouse gasses. These paradigm shifts will only be possible with efficient energy storage systems such as Li-ion batteries (LIBs). However, among other factors, some raw materials used on LIB production, such as cobalt and lithium, have geopolitical and environmental issues. Thus, in a context of a circular economy, the reuse of LIBs from EM for other applications (i.e., second-life batteries, SLBs) could be a way to overcome this problem, considering that they reach their end of life (EoL) when they get to a state of health (SOH) of 70-80% and still have energy storage capabilities that could last several years. The aim of this chapter is to make a review of the estimation methods employed in the diagnosis of LIB, such as SOH and remaining useful life (RUL). The correct characterization of these variables is crucial for the reassembly of SLBs and to extend the LIBs operational lifetime.

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Section: Gray-box Methods Are Hybrid Prognostics Between White and Blmentioning

confidence: 99%

A Circular Economy of Electrochemical Energy Storage Systems: Critical Review of SOH/RUL Estimation Methods for Second-Life Batteries

Montoya-Bedoya¹,

Sabogal-Moncada²,

Garcia-Tamayo³

et al. 2020

Green Energy and Environment

View full text Add to dashboard Cite

show abstract

Section: Gray-box Methodsmentioning

confidence: 99%

“…Copper, silver, gold, carbon, aluminum, nickel, indium tin oxide, tin, graphene, graphene oxide, PEDOT:PSS, polyaniline, iron, graphite [39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58] Semiconductors Zinc oxide, silicon, zinc selenide, indium-gallium-zinc oxide, cadmium selenide, gallium arsenide, MALH [59][60][61][62][63][64] Resistors Aluminum oxide, hafnium dioxide, poly(4-vinylphenol), spin-on glass, parylene, solid electrolytes [65][66][67][68][69][70][71][72] Table 3.…”

Section: Conductorsmentioning

confidence: 99%

Green Energy and Environment

Yap¹,

Tan²

2020

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In the Faculty of Transdisciplinary Innovation, Eng Hwa Yap is the Acting Associate Dean: Teaching and Learning (Operations). He also leads the Bachelor of Technology and Innovation (BTi) as the Course Director. His research at the university focuses on curated multidisciplinary and mixed approaches of enquiry to understand and respond to complex problems surrounding technology, environmental sustainability, and future energy systems. In his research, Eng Hwa uses methods centering on systems thinking to investigate emerging and contemporary issues in technologies and their interaction with society, the environment, economics, and policy.Andrew Huey Ping Tan has extensive engineering experience in both Motorola Solutions and Agilent Technologies, where he specializes in ensuring high quality and timely manufacture of industrial products for downstream customers. By implementing systems thinking and knowledge, life-cycle assessments, and technical improvisations, he specializes in product line setup and process improvements for new product developments. Andrew completed his PhD at the University of Nottingham where he developed a systemic measurement for the Water-Energy-Food Security Nexus in Malaysia using a transdisciplinary and systems approach. He also graduated with MEng (Hons) in Mechanical Engineering with First Class Honors from the same university.

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“…Wassiliadis, N et al [14] proposed an approach tackling battery SOH estimation, which consists of two extended Kalman filters (EKFs), that synchronously estimate both the battery SOH and model parameters. Shi, E et al [15] proposed a method that uses improved unscented particle filter (IUPF) to estimate the ohm internal resistance, and then estimates the SOH based on the internal resistance. The testing results show that IUPF has certain advantages, with the SOH estimation error always less than 3%.…”

Section: Review Of Estimation Approachesmentioning

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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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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State-of-health estimation for lithium battery in electric vehicles based on improved unscented particle filter

Cited by 37 publications

References 42 publications

A Circular Economy of Electrochemical Energy Storage Systems: Critical Review of SOH/RUL Estimation Methods for Second-Life Batteries

A Circular Economy of Electrochemical Energy Storage Systems: Critical Review of SOH/RUL Estimation Methods for Second-Life Batteries

Green Energy and Environment

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

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