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However, precise estimation of battery capacity is a challenging task, especially under complex and varying operation conditions. To tackle this problem, we propose an automatic feature extraction technique that utilizes random fragmented charging data to achieve precise capacity estimation across diverse operational scenarios.
This study proposes a novel estimation framework using deep residual shrinkage network (DRSN) and uncertainty evaluation to estimate the lithium-ion battery capacity directly; model inputs are only random fragment charging data.
The first type of feature extracted from the charging data denotes the calculated mean voltage when the battery charges at the start voltage V1 and stops charging at the end voltage V2. As can be seen from Fig. 2, the charging voltage rises slower with the battery degrades.
This study employs a TCN model 37, incorporating recent advances in convolutional architectures from other fields to improve the model, offering a more precise and robust method for predicting battery SOH. This model is designed to use battery charge-discharge data as indicators for predicting the SOH of lithium-ion batteries.
Considering the above issues, random fragment voltage-capacity data obtained during constant current charging is used to estimate the capacity of lithium-ion batteries in this study first.
The experimental results on the MIT and Oxford datasets demonstrate the performance of our proposed model on the battery capacity estimation task. Secondly, a new CNN-based module is proposed for the feature extractor, which combines the concepts of attention mechanism and residual structure.
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A novel data-driven method for mining battery open-circuit voltage characterization. ... comparison of long short-term memory and a temporal convolutional …
Accurate state of charge (SOC) estimation is critical for the effective management of lithium-ion batteries in electric vehicles (EVs). However, traditional SOC …
Nonetheless, several challenges are associated with extracting HIs using measured data such as voltage, current, and temperature. Certain HIs, such as internal …
Lithium-ion battery. Open-circuit voltage. ... relationship between noise effect on the input data and VF extraction precision; (c) a typical example of noise effect on input data; …
To solve this problem, this paper proposes an SOH online estimation method based on random charging curves. Firstly, rough localization of the random charging curve is accomplished to …
This study introduces a novel SOH estimation method combining Kolmogorov–Arnold Networks (KAN) and Long Short-Term Memory (LSTM) networks. The …
The methods for estimating battery capacity are mainly grouped into two categories, namely model-based methods and data-driven methods [[3], [4], [5]] model …
The Reached Voltage When Charging for the Same Duration.The second type of degradation feature denotes the reached voltage when the battery charges for the same …
These batteries have a rated capacity of 2.1 Ah and an operating voltage range of 3.2 V–4.2 V. The aging data were obtained through CC-CV charge/discharge cycles at 24 …
The average RMSE values for the real and imaginary parts between the original battery impedance data points and the fitted data of all battery samples are shown in Fig. 7, while …
With the rapid global growth in demand for renewable energy, the traditional energy structure is accelerating its transition to low-carbon, clean energy. Lithium-ion batteries, …
Data-driven battery state of health estimation based on random partial charging data IEEE Trans Power Electron, 37 ( 2022 ), pp. 5021 - 5031, 10.1109/TPEL.2021.3134701 View in Scopus …
In particular, the key contributions of this paper are summarized as follows: 1) Based on the observation of data performance and qualitative analysis of the aging mechanism …
Lithium-ion battery capacity estimation based on fragment charging data using deep residual shrinkage networks and uncertainty evaluation. ... (GPR) [7], and artificial neural …
The LSTM structure comprises three layers, including two LSTM layers and a fully connected layer. The CNN is used to extract key features from continuous charging data, …
Methods used to extract the aging characteristics of lithium-ion batteries involve the extraction of such data with trend characteristics by collecting the voltage, current, …
State of health estimation of lithium-ion battery based on constant current charging time feature extraction and internal resistance compensation. ... achieving battery …
Accurate battery capacity estimation is a key task in ensuring the safe and reliable operation of lithium-ion batteries and alleviating driver range anxiety. Most existing data …
In order to improve the accuracy of the SOH estimation model for lithium-ion batteries, this research adopts a feature extraction method based on charging curves. ... Data …
The model-based method utilizes the EKF algorithm and battery model to extract features, which are then utilized by a DCNN for subsequent battery capacity estimation. ... SOH prediction of …
A review of lithium-ion battery state of health and remaining useful life estimation methods based on bibliometric analysis ... Li et al. (2023) proposed a joint SOC-SOH-RUL …
The above correlations between HFs and battery discharge capacity are all above 0.8, indicating that the calculation of the area of the voltage curve during battery …
Training data feature extraction: the charging voltage, charging current, discharging voltage and discharging current of lithium-ion batteries in the aging experiments at …
The voltage and current of cell in a complete cycling with 1C charging rate is shown in Fig. 1 (a), which includes five processes, i.e.: (a) CC charging (the cell was firstly …
However, they require extracting data from a larger voltage range, making the application conditions more stringent and often difficult to use in real-world scenarios. ... State …
The battery management system (BMS) is an essential device to monitor and protect the battery health status, and the PHM as a critical part mainly includes state of health (SOH) estimation …
The feature analysis approaches focus on extracting the variables that are highly related to the battery capacity degradation from the voltage and capacity data in charging or …
Classification and extraction: Classifying lithium battery materials data from various sources and extracting data relevant to targeted attributes, particularly descriptors, …
This paper proposes a multi-scale data-driven framework for online SOC estimation of lithium-ion batteries, bringing the prior knowledge of battery modeling to data-driven state estimation. The …
Each stage''s charging time was 15 min until the battery voltage reached the 4.3 V charging cut-off voltage. The battery was fully charged and allowed to rest for 20 min, and …
State of health estimation for fast-charging lithium-ion battery based on incremental capacity analysis. J. Energy Storage (2022) ... the complementary ensemble …
To tackle this problem, we propose an automatic feature extraction technique that utilizes random fragmented charging data to achieve precise capacity estimation across diverse operational …
The published lithium-ion battery data set used in this study was obtained from the NASA Ames Prognostics Center of Excellence (PCoE). The carrier of the experiment was a …
To tackle this problem, we propose an automatic feature extraction technique that utilizes random fragmented charging data to achieve precise capacity estimation across diverse operational scenarios.
Current studies have divided the techniques for evaluating the SOH into two categories: model-based and data-driven methodologies [17,18].To perform model-based SOH …
In this paper, we extracted and summarized the effective degradation features from the CC charging voltage data for the battery SOH estimation based on the machine …
Although existing health feature extraction analysis such as incremental capacity IC curves, DTV and current/voltage response based on charge-discharge curves can reflect a …
The performance degradation data series have been used in the lifecycle study of lithium-ion batteries [7], focusing on the prognostics of the state of health (SOH), state of …
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