Simultaneous and rapid estimation of state of health and state of charge for lithium-ion battery based on response characteristics of load surges

Lin, Qiongbin; Li, Huasen; Chai, Qinqin; Cai, Fenghuang; Zhan, Yin

doi:10.1016/j.est.2022.105495

Cited by 14 publications

(4 citation statements)

References 55 publications

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“…Thirdly, based on the measured OCV2, the SOC at the corresponding state (SOC2) is again looked up through the SOC-OCV relationship. By equating the difference of SOC1 and SOC2 with ∆Q using the definition of SOC, which is the current amount of energy stored in the battery (typically denoted by Q) divided by the battery storage capacity (typically denoted by Qmax) and a little bit of algebra, the battery capacity can be estimated faster than the conventional technique [8]. It certainly has the advantage of faster measurement of battery storage capacity compared to the full discharge method.…”

Section: A Brief Review Of Battery Storage Capacity Evaluation Methodsmentioning

confidence: 99%

“…In order to estimate the SOH, it is important to identify features that might reflect and quantify battery storage capacity as battery capacity degrades. On top of that, the features that will be used to quantify battery capacity fade, have to be selected with values that can be easily obtained during battery operation, such as voltage, current, and temperature [7,8]. In this section, a description of the feature extraction used in the current research is provided.…”

Section: Transient Response Analysis (Tra) Of Voltagementioning

confidence: 99%

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Rapid Estimation of Battery Storage Capacity through Multiple Linear Regression

Jung

Choi

2023

Batteries

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Due to global warming issues, the rapid growth of electric vehicle sales is fully expected to result in a dramatic increase in returned batteries after the first use. Naturally, industries have shown great interest in establishing business models for retired battery reuse and recycling. However, they still have many challenges, such as high costs from the logistics of returned batteries and evaluating returned battery quality. One of the most important characteristics of a returned battery is the battery storage capacity. Conventionally, the battery’s energy capacity is measured through the low current full charging and discharging process. While this traditional measurement procedure gives a reliable estimate of battery storage capacity, the time required for a reliable estimate is unacceptably long to support profitable business models. In this paper, we propose a new algorithm to estimate battery storage capacity that can dramatically reduce the time for estimation through the partial discharging process. To demonstrate the applicability of the proposed algorithm, cylindrical and prismatic cells were used in the experiments. Initially, five indicators were selected from the voltage response curves that can identify battery storage capacity. Then, the five indicators were applied to principal component analysis (PCA) to extract dominant factors. The extracted factors were applied to a multiple linear regression model to produce a reliable estimation of battery storage capacity.

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Section: A Brief Review Of Battery Storage Capacity Evaluation Methodsmentioning

confidence: 99%

Section: Transient Response Analysis (Tra) Of Voltagementioning

confidence: 99%

Rapid Estimation of Battery Storage Capacity through Multiple Linear Regression

Jung

Choi

2023

Batteries

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“…It is often referred to as an associative NN, where only a small sub dressed by the input vector determines instantaneous output. Compared with bac agation neural networks (BPNNs) and deep learning algorithms, FCMNN has vantages of faster learning speed, better generalization and less computation res which is increasingly being employed for classification and forecast [33,36,37]. Th The energy of each level of detail coefficient is obtained and used as an effective classification feature.…”

Section: Battery Soh Estimator Based On Fcmnnmentioning

confidence: 99%

Non-Invasive Method-Based Estimation of Battery State-of-Health with Dynamical Response Characteristics of Load Surges

Fan,

Lin,

Huang

2024

Energies

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Battery state-of-health (SOH) estimation is an effective approach to evaluate battery reliability and reduce maintenance costs for battery-based backup power supply systems. This paper proposes a novel SOH estimation method for batteries, which only uses the response characteristics of load surges and is, therefore, non-destructive to the estimated battery and its system. The discrete wavelet transform (DWT) method based on multi-resolution analysis (MRA) is used for wavelet energy features extraction, and the fuzzy cerebellar model neural network (FCMNN) is introduced to design the battery SOH estimator. The response voltage signals to load surges are used in the training and detection process of the FCMNN. Compared to conventional methods, the proposed method only exploits characteristics of online response signals to the inrush currents rather than injecting interference signals into the battery. The effectiveness of the proposed method is validated by detailed simulation analysis and experiments.

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“…The state of health (SOH) is commonly used as an estimation index to evaluate the battery-aging degree, as shown in Figure 1, which is easily affected by characteristic parameters of the battery, including operation temperature, depth of discharge (DOD), discharge current [71], state of charge (SOC), and cycling depth, as well as charge throughput [72]. The battery can be used for a certain period that its internal characteristics mainly manifest as capacity attenuation [73]. The battery capacity can present the current maximum capacity of the battery, which is different from the initial capacity, in order to analyze the its performance; namely Incremental Capacity Analysis (ICA) [74][75][76], Electrochemical Impedance Spectroscopy (EIS) [77][78][79], X-ray computed tomography (CT) [80], and other technologies based on the physical and chemical properties of the battery can be used to obtain the parameters required by evaluating the battery-aging degree.…”

Section: Related Policies and Technical Standards For Echelon Utiliza...mentioning

confidence: 99%

A Review on Dynamic Recycling of Electric Vehicle Battery: Disassembly and Echelon Utilization

2023

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With the growing requirements of retired electric vehicles (EVs), the recycling of EV batteries is being paid more and more attention to regarding its disassembly and echelon utilization to reach highly efficient resource utilization and environmental protection. In order to make full use of the retired EV batteries, we here discuss various possible application methods of echelon utilization, including hierarchical analysis methods based on various battery evaluation index. In addition, retired EV battery disassembly is also reviewed through the entire EV battery recycling based on human–robot collaboration methods. In order to improve the efficiency and reduce the cost of EV recycling, it is necessary to find a suitable recycling mode and disassembly process. This paper discusses the future possibility of echelon utilization and disassembly in retired EV battery recycling from disassembly optimization and human–robot collaboration, facing uncertain disassembly and echelon utilization.

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Simultaneous and rapid estimation of state of health and state of charge for lithium-ion battery based on response characteristics of load surges

Cited by 14 publications

References 55 publications

Rapid Estimation of Battery Storage Capacity through Multiple Linear Regression

Rapid Estimation of Battery Storage Capacity through Multiple Linear Regression

Non-Invasive Method-Based Estimation of Battery State-of-Health with Dynamical Response Characteristics of Load Surges

A Review on Dynamic Recycling of Electric Vehicle Battery: Disassembly and Echelon Utilization

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