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The rapidly growing deployment of lithium-ion batteries in electric vehicles is associated with a great waste of natural resource and environmental pollution caused by manufacturing and disposal. Repurposing the retired lithium-ion batteries can extend their useful life, creating environmental and economic benefits. However, the residual capacity of retired lithium-ion batteries is unknown and can be drastically different owing to various working history and calendar life. In this study, we used the incremental capacity (IC) curve to estimate the residual capacity of waste power batteries. First, through experimental means, the parameters of the battery and the IC charging curve are measured. Second, to achieve rapid capacity estimation, a battery capacity estimation method based on the adaptive genetic algorithm-back propagation neural network (AGA-BPNN) is proposed and compared with other classic machine learning methods. The proposed algorithm reduced the error of capacity estimation to 3%. Finally, through the analysis of the IC curve, a method for identifying aging mechanism of large-scale decommissioned batteries is obtained. This research provides effective support for the capacity-based classification of large-scale decommissioned power batteries. [ABSTRACT FROM AUTHOR]

Rapid and accurate sorting consistent cells is indispensable for the cascade utilization of retired batteries. However, the conventional full charge-discharge capacity testing methods are time-consuming and low efficient, which cannot be employed to screen large-scale retired batteries. Moreover, most existing artificial intelligence techniques sort retired batteries at cloud servers, which leads to tremendous burdens of transmission. Therefore, a fast and intelligent screening approach based on a Light Gradient Boosting Machine is proposed, which only utilizes partial charge/discharge curves, significantly reducing the test time. Meanwhile, a cloud-edge collaborative framework for separating large-scale retired batteries is developed. Specifically, the model is trained in the cloud and screens retired batteries in the edge plane. The experimental results show that the data transmission time of the cloud-edge collaborative framework is reduced by about 3 orders of magnitude than the cloud computing plane. Besides, the method achieves a 97 % classification accuracy rate. More importantly, the screening efficiency of the proposed approach is approximately 6 times higher than that of the conventional full charge-discharge testing methods. Additionally, the voltage consistency of the new module regrouped is significantly improved compared with the original module. The mean of the standard deviation is reduced by approximately 3 times. • Rapid and accurate sorting consistent cells is indispensable for cascade utilization. • A fast screening approach based on Light Gradient Boosting Machine is proposed. • The method only utilizes partial charge/discharge curves, reducing the test time. • A cloud-edge collaborative framework for separating large-scale retired batteries is developed. [ABSTRACT FROM AUTHOR]

Summary: With the commercialization of the electric vehicles, the large‐scale lithium‐ion cells as the power of electric vehicles are to be retired. The second‐use of retired cells is of great significance to improve the battery economy. A fast classification and regroup evaluation method of the retired lithium‐ion cells are proposed in this paper to improve the classification efficiency of retired lithium‐ion cells and adapt to the regroup under different conditions. The lithium‐ion cells after being balanced in parallel are charged in series with a constant current. A support vector regression (SVR) model with the parameters optimized by the particle swarm optimization (PSO) algorithm is built for the fast capacity estimation and the error will not exceed 0.3%. Different cells regrouped means different performance. In order to improve the consistency of retired cells and satisfy different using conditions, a Weighted‐K‐means algorithm is proposed in this paper to regroup the cells with the known capacity and internal resistance. The classification method is evaluated by the voltage consistency of cells using different working conditions, which indicates capacity occupied a large proportion can meet the requirement of energy condition meanwhile keep a good consistency. But the resistance will dominate in algorithm under conditions which have requirement for instantaneous power. [ABSTRACT FROM AUTHOR]

Summary: With the commercialization of the electric vehicles, the large‐scale lithium‐ion cells as the power of electric vehicles are to be retired. The second‐use of retired cells is of great significance to improve the battery economy. A fast classification and regroup evaluation method of the retired lithium‐ion cells are proposed in this paper to improve the classification efficiency of retired lithium‐ion cells and adapt to the regroup under different conditions. The lithium‐ion cells after being balanced in parallel are charged in series with a constant current. A support vector regression (SVR) model with the parameters optimized by the particle swarm optimization (PSO) algorithm is built for the fast capacity estimation and the error will not exceed 0.3%. Different cells regrouped means different performance. In order to improve the consistency of retired cells and satisfy different using conditions, a Weighted‐K‐means algorithm is proposed in this paper to regroup the cells with the known capacity and internal resistance. The classification method is evaluated by the voltage consistency of cells using different working conditions, which indicates capacity occupied a large proportion can meet the requirement of energy condition meanwhile keep a good consistency. But the resistance will dominate in algorithm under conditions which have requirement for instantaneous power. [ABSTRACT FROM AUTHOR]

With the rapid development of electric vehicles, the second usage of retired batteries becomes a key issue. The accuracy of existing screening methods for retired batteries is highly dependent on the feature selection from charging or discharging curves. This paper proposes a novel method of screening retired batteries, in which the constant current (CC) charging curves are converted into images by Gramian angular difference fields (GADF) and classified with a ConvNeXt network. Firstly, the CC charging voltage data is reasonably reduced by piecewise aggregation approximation. Secondly, the CC voltage curves are encoded into images by GADF to make small differences more distinguishable. Then, a ConvNeXt network is used for screening the retired batteries because of its excellent performance on accuracy and scalability. Finally, validation experiments are carried out on 143 retired high-power lithium-ion batteries, and the results show that the proposed screening method has a classification detection accuracy of 93.71%. [ABSTRACT FROM AUTHOR]

As electric vehicles (EVs) are developing at a rapid pace, the foreseeable "scrap tide" of EV batteries poses a severe challenge to ecological protection. This article investigates a dual-recycle channel closed-loop supply chain and provides regulatory solutions to retired EV batteries' recycling. Specifically, we construct four supervision scenarios: S1 no policy intervention, S2 reward-penalty scheme, S3 deposit-refund scheme, and S4 dual scheme combining S2 and S3. Based on the Stackelberg game and empirical data, all scenarios' recycling performance is evaluated and compared with a view to "society, economy, and environment". The results revealed: (1) Compared with S1, the recycling rate and carbon reduction rate in S2∼S4 increase by 2.6049%/0.0092%, 4.0379%/0.0285%, and 6.6660%/0.0379%, respectively; (2) The difference between S2 and S3 in recycling performance depends on regulatory intensities, yet the latter places greater burdens on consumers and firms. The S4 presents optimal environmental performance but at the expense of socioeconomic development; (3) As regulatory intensity increases, social welfare rises driven by environmental benefits, then falls due to overburdened supply chain profits, consumer surplus, and policy expenditures; (4) Carbon trading prices and EVs' potential market sizes affect regulatory schemes' operations. Our results contribute to policy-making and managerial practices for EV battery recycling. [Display omitted] • Construct a dual-recycle channel system for electric vehicle battery recycling. • Evaluate four supervision scenarios' social-economic-environmental performance. • The impact of the carbon trading mechanism on supply chain operations is examined. • Regulatory schemes improve recycling rates, carbon reduction and social welfare. • Electric vehicle potential market sizes affect regulatory schemes' operations. [ABSTRACT FROM AUTHOR]

• A reward-penalty mechanism is proposed to promote the LIBs' recycling by using the Stackelberg game theory. • Six collection modes are considered and compared, including the collection activities undertaken by different stakeholders. • A partnership between the manufacturer and retailer can achieve the highest actual collection rate and the total welfare. With the rapid adoption of Electric Vehicles (EVs), numerous lithium-ion batteries (LIBs) are reaching retirement age, leading to increasing concerns about the sustainable industrial development. To promote the recycling of LIBs, a reward-penalty mechanism is proposed in this work and analyzed by using the Stackelberg game theory. Six collection modes are considered and compared, including the collection activities undertaken by different stakeholders, i.e. the EV manufacturer, the EV retailer, the third-party enterprise, and the pairwise partners of them. The results show that: (i) a partnership between the manufacturer and retailer can achieve the highest actual collection rate and the total welfare; (ii) although increasing the reward-penalty intensity can contribute to higher collection rates, the total welfare can still fall, due to the negative influences of policy expenditure and implementation cost; (iii) the impact of raising the collection rate target is similar to the reward-penalty intensity, which can first promote and then restrain the growth of total welfare, and the drop is mainly due to the negative influences of corporate profits and implementation cost. [ABSTRACT FROM AUTHOR]

The power from lithium-ion batteries can be retired from electric vehicles (EVs) and can be used for energy storage applications when the residual capacity is up to 70% of their initial capacity. The retired batteries have characteristics of serious inconsistency. In order to solve this problem, a layered bidirectional active equalization topology is proposed in this paper. Specifically, a bridge-type equalization topology based on an inductor is adopted in the bottom layer, and the distributed equalization topological structure based on the bidirectional BUCK-BOOST circuit is adopted in the top layer. State of charge (SOC) is used as the equalization target variable, and the bottom layer equalization algorithm based on a "partition" idea and route optimization is proposed. The static equalization experiments and charge equalization experiments are performed by the 12 retired batteries selected from an electric sanitation vehicle. The results show that the proposed equalization method can reduce the SOC difference between retired batteries and can effectively improve the inconsistency of the retired battery pack with a faster equalization speed. [ABSTRACT FROM AUTHOR]

The battery management system (BMS) is the key development for energy storage systems, and battery balancing is an important subsystem of the BMS. However, with rapid development of supercapacitors, future energy storage cells are not constrained by one type, while different types of cells may form a source package (SP). Furthermore, the introduction of second-life batteries from retired electric vehicles promotes the demand of effective balancing systems for SPs with hybrid cells, as well as the requirement that balancing should be extended to any preset ratio rather than 1:1. This paper proposes a novel tapped inductor balancing circuit that allows any ratio of voltage balancing for hybrid energy storage cells. The analysis of the circuit, simulation and experiment results are presented to demonstrate its effectiveness in handling hybrid source balancing. [ABSTRACT FROM AUTHOR]