In this paper, we provide an effective approach for battery screening. First,
Recently, due to their wide temperature range, high energy density, low self-discharge rate, and long cycle lifetime, lithium-ion batteries have been widely used in a variety of industrial sectors such as transportation, electronics, portable mobile devices, and aerospace [1,2,3,4].Prognostics and health management (PHM) of batteries refers to the activity of
To address the issue of the lengthy acquisition time for the IC curve and to improve the efficiency of retired battery echelon utilization, this paper proposes an IC curve prediction method based on CNN, which realizes the
In this paper, we provide an effective approach for battery screening. First, we apply interpolation on DVCs and give a method to transform them into slope sequences. Then, we use density-based spatial clustering of applications with noise (DBSCAN) for denoising and treat the remaining data as input to the K-means algorithm for screening
Cell Screening with multi-source time series data for lithium-ion battery (LIB) grouping is a challenging task in the production of LIB pack. Currently, most of these cell screening methods adopt a plain data fusion strategy that does not consider the relationship between different sources in the multi-source time series data.
First, the method assumes that the retired batteries are preliminarily screened and data filtered
This paper proposes a method of retired lithium-ion battery screening based on support vector machine(SVM) with a multi-class kernel function. First, 10 new NCR18650B batteries were used to carry
In this paper, a flexible screening scheme for two mainstream types of retired batteries based on random forest (RF) algorithm and new feature is proposed. Firstly, retired battery modules are disassembled into battery cells. Subsequently, the incremental capacity (IC) curves of all batteries are gained by differentiating the capacity-voltage
Accurate and efficient screening of retired lithium-ion batteries from electric vehicles is crucial to guarantee reliable secondary applications such as in energy storage, electric bicycles, and smart grids. However, conventional electrochemical screening methods typically involve a charge/discharge process and usu 2020 RSC Advances HOT Article
Herein, an edge-fast screening method for retired batteries based on a Light Gradient Boosting Machine (LightGBM) is proposed. At the same time, a retired battery sorting architecture based on cloud-edge collaboration is presented, which offloads sorting tasks from the cloud to the edge side, and deploys a machine learning environment to
Considering the safety of electric vehicles, lithium-ion batteries must be retired and replaced with new ones when their capacity has decayed to 70%–80 % of the rated capacity [5].The remaining capacity of these retired batteries is sufficient for other electric energy systems, such as electric bicycles, scenic tourist electric vehicles, smart grids, communication base
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In this paper, a flexible screening scheme for two mainstream types of retired
Accurate and efficient screening of retired lithium-ion batteries from electric vehicles is crucial to guarantee reliable secondary applications such as in energy storage, electric bicycles, and smart grids. However, conventional
The naive bayes classifier (NBC) takes the peak coordinates of the IC curve as input, which obtains different battery capacity types. The screening accuracy can reach 96.9%, which indicates the proposed screening method can achieve the consistent screening of retired batteries.
To address the issue of the lengthy acquisition time for the IC curve and to improve the efficiency of retired battery echelon utilization, this paper proposes an IC curve prediction method based on CNN, which realizes the prediction of the complete IC curve and the V-Q curve by using the local charging voltage segments starting from any time po...
For consistency screening of lithium-ion batteries, this paper makes three
In this paper, we focus on improving the screening efficiency for retired batteries, namely speed and accuracy, and propose an efficient screening method based on support vector machine. Twelve retired LiFePO4 battery modules are dissembled into 240 cells as training and testing samples, and their capacity and resistance are analyzed. The
First, the method assumes that the retired batteries are preliminarily screened and data filtered under the premise of historical data to reduce the impact of data fluctuations. Then, according to the application scenario of the battery, a screening method with feature weight is designed to improve the matching degree between the battery and
Fast and accurate screening of retired lithium-ion batteries is critical to an
Herein, an edge-fast screening method for retired batteries based on a Light
For consistency screening of lithium-ion batteries, this paper makes three improvements based on the traditional FCM algorithm: first, the principal component analysis of battery characteristic parameters, namely dimension reduction, can be used when the battery characteristic parameters are too many; Second, aiming at the problem that FCM
Screening of retired lithium-ion batteries using incremental capacity charging curve-based residual capacity estimation method for facilitating sustainable circular lithium-ion battery system J. Manuf. Sci. Eng., 144 ( 2 ) ( 2022 ), p.
Owing to the inconsistent decay among cells during their applications, the battery uniformity is low, which seriously restricts the economy and efficiency of the cascade utilization of large-scale retired lithium batteries. Moreover, due to the complex chemical reaction inside the battery, the degradation process cannot be accurately described. It is particularly critical to select
Screening of Retired Lithium-Ion Batteries Using Incremental Capacity Charging Curve-Based Residual Capacity Estimation Method for Facilitating Sustainable Circular Lithium-Ion Battery System Honglei Li, Honglei Li School of Mechanical Engineering, Dalian University of Technology, Dalian, Liaoning Province 116024, China. Email: 939611809@qq . Search
Fast and accurate screening of retired lithium-ion batteries is critical to an efficient and reliable second use with improved performance consistency, contributing to the sustainability of renewable energy sources. However, time-consuming testing, representative criteria extraction, and large module-to-module inconsistencies at the end of
In this paper, we focus on improving the screening efficiency for retired batteries, namely speed and accuracy, and propose an efficient screening method based on support vector machine. Twelve retired LiFePO4 battery modules are dissembled into 240
The naive bayes classifier (NBC) takes the peak coordinates of the IC curve as input, which
Lithium-ion batteries (LIBs), the main pillar of energy storage technology for electric vehicles (EVs), suffer from performance degradation during usage and storage in terms of capacity and power [1].Typically, they reach their end-of-life when their remaining capacity reaches 80% of the nominal capacity [2] or their internal resistance reaches 200% of that of
Lithium batteries from consumer electronics contain anode and cathode material (Figure 1) and, as shown in Figure 2 (Chen et al., 2019), some of the main materials used to manufacture LIBs are lithium, graphite and cobalt in which their production is dominated by a few countries.More than 70% of the lithium used in batteries is from Australia and Chile whereas
The other type of screening method for retired batteries focuses on the efficiency. Such methods not only need to screen retired batteries with good consistency, but also optimize the screening process and shorten the screening time. A facile screening approach was proposed for commercial 18650 lithium-ion cells (He et al., 2017).
Conclusions Aiming at accelerating the progress of retired lithium-ion batteries for the second use, a fast and accurate screening approach based on pack-level testing is proposed for evaluating and classifying module-level aging. The main conclusions are summarized.
There is no doubt that AI-based screening techniques for retired batteries have attracted wide attention. The workflow can be divided into three stages. Step 1: the raw charging/discharging data of the retired batteries is pre-processed. Step 2: the capacity features that reflect the internal states are extracted.
These characteristics of the proposed framework facilitate fast, accurate, stable screening and accelerate the progress of second use, contributing to the sustainability of renewable energy and the reliability of battery storage systems.
Average overall performance improvements of 18.94%, 4.83% and 34.41% over benchmarks. Fast and accurate screening of retired lithium-ion batteries is critical to an efficient and reliable second use with improved performance consistency, contributing to the sustainability of renewable energy sources.
As shown in Fig. 11 (a), it is evident that the screening time of the presented method is much shorter than that of the traditional FCD approach. Specifically, the FCD method needs about 1200 h to screen five-ton retired batteries, while the proposed method only requires approximately 200 h. The sorting efficiency is increased by 6 times.
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