Search Results (32)

In the wake of global energy transition and the “dual-carbon” goal, the rapid growth of electric vehicles has posed challenges for large-scale lithium-ion battery decommissioning. Retired batteries exhibit dual attributes of strategic resources (cobalt/lithium concentrations several times higher than natural ores) and environmental risks (heavy metal pollution, electrolyte toxicity). This paper systematically reviews pyrometallurgical and hydrometallurgical recovery technologies, identifying bottlenecks: high energy/lithium loss in pyrometallurgy, and corrosion/cost/solvent regeneration issues in hydrometallurgy. To address these, an integrated recycling process is proposed: low-temperature physical separation (liquid nitrogen embrittlement grinding + froth flotation) for cathode–anode separation, mild roasting to convert lithium into water-soluble compounds for efficient metal oxide separation, stepwise alkaline precipitation for high-purity lithium salts, and co-precipitation synthesis of spherical hydroxide precursors followed by segmented sintering to regenerate LiNi_1/3Co_1/3Mn_1/3O₂ cathodes with morphology/electrochemical performance comparable to virgin materials. This low-temperature, precision-controlled methodology effectively addresses the energy-intensive, pollutive, and inefficient limitations inherent in conventional recycling processes. By offering an engineered solution for sustainable large-scale recycling and high-value regeneration of spent ternary lithium ion batteries (LIBs), this approach proves pivotal in advancing circular economy development within the renewable energy sector. Full article

Lithium-ion batteries (LIBs) are widely deployed in electric vehicles due to their high energy density and long cycle life. Even after retirement, typically at around 80% of their rated capacity, LIBs can still be repurposed for second-life applications such as residential energy storage systems (ESSs). However, effectively grouping these heterogeneous cells is crucial to optimizing performance of the module. Retired LIBs can be effectively repurposed for numerous second-life applications such as ESSs, and other power backups. In this paper, we compare four clustering approaches including random grouping, equal-number Support Vector Clustering, K-means, and an equal-number Gaussian Mixture Model (GMM) to organize 60 retired cells into 48 V modules consisting of 15-cell groups. We verify the performance of each method via simulations of a 15S2P configuration, focusing on the standard deviation of final charge voltage, average charge throughput, delta capacity, and coulombic efficiency. Based on the evaluation metrics analyzed after regrouping the battery cells and simulating them for second-life ESS applications, the GMM-based clustering method demonstrates better performance. Full article

With the large-scale application of lithium-ion batteries in the field of new energy, many retired lithium batteries not only cause environmental pollution problems but also lead to serious waste of resources. Repairing failed lithium batteries and regenerating new materials has become a crucial path to break through this dilemma. Based on the research on the failure mechanism of ternary cathode materials, this paper systematically combs through the multiple factors leading to their failure, extensively summarizes the influence of heat treatment process parameters on the performance of recycled materials, and explores the synergistic effect between heat treatment technology and other processes. Studies have shown that the failure of ternary cathode materials is mainly attributed to factors such as cation mixing disorder, the generation of microcracks, phase structure transformation, and the accumulation of by-products. Among them, cation mixing disorder damages the crystal structure of the material, microcracks accelerate the pulverization of the active substance, phase structure transformation leads to lattice distortion, and the generation of by-products will hinder ion transport. The revelation of these failure mechanisms lays a theoretical foundation for the efficient recycling of waste materials. In terms of recycling technology, this paper focuses on the application of heat treatment technology. On the one hand, through synergy with element doping and surface coating technologies, heat treatment can effectively improve the crystal structure and surface properties of the material. On the other hand, when combined with processes such as the molten salt method, coprecipitation method, and hydrothermal method, heat treatment can further optimize the microstructure and electrochemical properties of the material. Specifically, heat treatment plays multiple key roles in the recycling process of ternary cathode materials: repairing crystal structure defects, enhancing the electrochemical performance of the material, removing impurities, and promoting the uniform distribution of elements. It is a core link to achieving the efficient reuse of waste ternary cathode materials. Full article

With the rapid expansion of transportation electrification worldwide, the demand for electric vehicles (EVs) has increased dramatically, creating new and sustainable growth opportunities for the global economy. However, as the most expensive component of EVs, lithium-ion batteries pose significant sustainability challenges due to raw material consumption and supply chain constrains, as well as the complexities of end-of-life battery disposal and recycling. To address these concerns, many countries are actively establishing regulations to promote sustainable pathways for battery reuse and recycling. Despite these efforts, existing battery recycling regulations remain often inefficient and vary significantly across different countries in legal enforcement, producer responsibility, waste classification, recycling targets, design standards, public engagement, and financial incentives, particularly given the complexities of the global supply chain and resource distribution within the battery industry. Understanding these regulatory differences and establishing a unified framework are therefore crucial to ensuring sustainable and efficient battery recycling. This review provides a comprehensive analysis of the necessity of establishing robust regulations for sustainable development of battery recycling industry. The evolution and refinement of battery recycling regulations are deeply reviewed to identifying persistent gaps and challenges in key countries. Furthermore, we discuss the challenges associated with regulatory enforcement and propose strategies for developing a more cohesive legislative framework to ensure the effective utilization of retired batteries. Full article

With the increasing adoption of lithium-ion batteries in energy storage systems, accurately monitoring the State of Health (SoH) of retired batteries has become a pivotal technology for ensuring their safe utilization and maximizing their economic value. In response to this need, this paper presents a highly efficient estimation model based on the multi-input metabolic gated recurrent unit (MM-GRU). The model leverages constant-current charging time, charging current area, and the 1800 s voltage drop as input features and dynamically updates these features through a metabolic mechanism. It requires only four cycles of historical data to reliably predict the SoH of subsequent cycles. Experimental validation conducted on retired Samsung and Panasonic battery cells and packs under constant-current and dynamic operating conditions demonstrates that the MM-GRU model effectively tracks SoH degradation trajectories, achieving a root mean square error of less than 1.2% and a mean absolute error of less than 1%. Compared to traditional machine learning algorithms such as SVM, BPNN, and GRU, the MM-GRU model delivers superior estimation accuracy and generalization performance. The findings suggest that the MM-GRU model not only significantly enhances the breadth and precision of SoH monitoring for retired batteries but also offers robust technical support for their safe deployment and asset optimization in energy storage systems. Full article

A closed-loop regeneration process for spent LiCoO₂ has been successfully designed with prior synthesis of LiNi_xCo_yMn_zO₂, by the authors. This research applies the methodology to lithium-ion battery anodes, using spent graphite from a decommissioned battery in a leaching process with 1.5 mol∙L⁻¹ malic acid and 3% H₂O₂ alongside LiCoO₂. The filtered graphite was separated, annealed in an argon atmosphere, and the filtrate was used to synthesize NCM cathode material. Characterization involved X-ray diffraction, EDX, and SEM techniques. The regenerated graphite (RG) showed a specific discharge capacity of 340.4 mAh/g at a 0.1C rate in the first cycle, dropping to 338.4 mAh/g after 55 cycles, with a Coulombic efficiency of 99.9%. CV and EIS methods provided further material assessment. In a related study, the SNCM111 synthesized from the leaching solution showed a specific discharge capacity of 131.68 mAh/g initially, decreasing to 115.71 mAh/g after 22 cycles. Full article

This study introduces a sophisticated methodology that integrates 3D assessment technology for the reorganization and recycling of retired lithium-ion battery packs, aiming to mitigate environmental challenges and enhance sustainability in the electric vehicle sector. By deploying a kernel extreme learning machine (KELM), variational mode decomposition (VMD), and an advanced sparrow search algorithm (SSA), the research achieves a marked increase in the precision of battery classification and performance forecasting. Implementing a three-dimensional dynamic evaluation model, the study optimizes battery pack grouping strategies, culminating in superior secondary utilization rates, extended operational lifespans, and minimized ecological footprints. The research demonstrates that balanced weight distribution strategies, which maximize energy density to 61.37571 Wh/L and cycle counts up to 947 cycles, are pivotal for the efficient reorganization of battery packs, substantiating the economic feasibility and environmental sustainability of recycling initiatives. Future endeavors will extend this research to investigate the influence of diverse battery materials and morphologies on reorganization efficacy, with the aim of broadening the application horizons to include real-world scenarios, thereby refining battery performance and lifespan predictions and propelling forward the frontiers of recycling technology and policy development. Full article

The utilization of lithium-ion batteries (LIBs) is increasing sharply with the increasing use of mobile phones, laptops, tablets, and electric vehicles worldwide. Technologies are required for the recycling and recovery of spent LIBs. In the context of the circular economy, it is urgent to search for new methods to recycle waste graphite that comes from the retired electrode of LIBs. The conversion of waste graphite into other products, such as new electrodes, in the field of energy devices is attractive because it reduces resource waste and processing costs, as well as preventing environmental pollution. In this paper, new electrode materials were prepared using waste anode graphite originating from a spent mobile phone battery with an xBT·0.1C₁₂H₂₂O₁₁·(0.9-x)(NH₄)₂HPO₄ composition, where x = 0–50 weight% BT from the anodic active mass of the spent phone battery (labeled as BT), using the melt quenching method. Analysis of the diffractograms shows the graphite crystalline phase with a hexagonal structure in all prepared samples. The particle sizes decrease by adding a higher BT amount in the composites. The average band gap is 1.32 eV (±0.3 eV). A higher disorder degree in the host network is the main factor responsible for lower band gap values. The prepared composites were tested as electrodes in an LIB or a fuel cell, achieving an excellent electrochemical performance. The voltammetric studies indicate that doping with 50% BT is the most suitable for applications as electrodes in LIBs and fuel cells. Full article

In order to solve the issue of low efficiency in retired battery clustering, a method for quickly obtaining a charging curve and Incremental Capacity (IC) curve based on Convolutional Neural Networks (CNN) is proposed. By training a CNN model, the method enables accurate prediction of complete IC curves and V-Q curves from local charging curves starting at any beginning. The prediction accuracy was validated using the Oxford battery degradation dataset, and transfer learning was conducted by fine-tuning the model trained on LCO batteries for use with LFP batteries, which reduced the RMSE of the estimation and validated the generalizability of the model. Peak parameters were extracted from both the original and predicted IC curves for clustering, and the t-test was applied to eliminate outliers, which significantly reduced the time required to obtain clustering features and improved clustering efficiency. Full article

In this paper, we propose a battery management algorithm to maximize the lifetime of a parallel-series connected battery pack with heterogeneous states of health in a battery energy storage system. The growth of retired lithium-ion batteries from electric vehicles increases the applications for battery energy storage systems, which typically group multiple individual batteries with heterogeneous states of health in parallel and series to achieve the required voltage and capacity. However, previous work has primarily focused on either parallel or series connections of batteries due to the complexity of managing diverse battery states, such as state of charge and state of health. To address the scheduling in parallel-series connections, we propose a cooperative multi-agent deep Q network framework that leverages multi-agent deep reinforcement learning to observe multiple states within the battery energy storage system and optimize the scheduling of cells and modules in a parallel-series connected battery pack. Our approach not only balances the states of health across the cells and modules but also enhances the overall lifetime of the battery pack. Through simulation, we demonstrate that our algorithm extends the battery pack’s lifetime by up to 16.27% compared to previous work and exhibits robustness in adapting to various power demand conditions. Full article

Accurately estimating the state of health (SOH) of lithium-ion batteries ensures the proper operation of the battery management system (BMS) and promotes the second-life utilization of retired batteries. The challenges of existing lithium-ion battery SOH prediction techniques primarily stem from the different battery aging mechanisms and limited model training data. We propose a novel transferable SOH prediction method based on a neural network optimized by Harris hawk optimization (HHO) to address this challenge. The battery charging data analysis involves selecting health features highly correlated with SOH. The Spearman correlation coefficient assesses the correlation between features and SOH. We first combined the long short-term memory (LSTM) and fully connected (FC) layers to form the base model (LSTM-FC) and then retrained the model using a fine-tuning strategy that freezes the LSTM hidden layers. Additionally, the HHO algorithm optimizes the number of epochs and units in the FC and LSTM hidden layers. The proposed method demonstrates estimation effectiveness using multiple aging data from the NASA, CALCE, and XJTU databases. The experimental results demonstrate that the proposed method can accurately estimate SOH with high precision using low amounts of sample data. The RMSE is less than 0.4%, and the MAE is less than 0.3%. Full article

Faced with the increasing volume of retired lithium-ion batteries (LIBs), recycling and reusing the spent graphite (SG) is of great significance for resource sustainability. Here, a facile method for transforming the SG into a carbon framework as well as loading Fe₂O₃ to form a composite anode with a sandwich structure is proposed. Taking advantage of the fact that the layer spacing of the spent graphite naturally expands, impurities and intercalants are eliminated through microwave thermal shock to produce microwave-puffed graphite (MPG) with a distinct three-dimensional structure. Based on the mechanism of microwave-induced gasification intercalation, a Fe₂O₃-MPG intercalation compound (Fe₂O₃-MPGIC) anode material was constructed by introducing iron precursors between the framework layers and subsequently converting them into Fe₂O₃ through annealing. The Fe₂O₃-MPGIC anode exhibits a high reversible capacity of 1000.6 mAh g⁻¹ at 200 mA g⁻¹ after 100 cycles and a good cycling stability of 504.4 mAh g⁻¹ at 2000 mA g⁻¹ after 500 cycles. This work can provide a reference for the feasible recycling of SG and development of high-performance anode materials for LIBs. Full article

Retired electric vehicle (EV) batteries are reused in second-life energy storage applications. However, the overall performance of repurposed energy storage systems (ESSs) is limited by the variability in the individual batteries used. Therefore, battery grading is required for the optimal performance of ESSs. Electrochemical impedance spectroscopy (EIS)-based evaluation of battery aging is a promising way to grade lithium-ion batteries. However, it is not practical to measure the impedance of mass-retired batteries due to their high complexity and slowness. In this paper, a broadband multi-sine binary signal (MSBS) perturbation integrated with a multichannel EIS system is presented to measure the impedance spectra for the high-speed aging evaluation of lithium-ion batteries or modules. The measurement speed is multiple times higher than that of the conventional EIS. The broadband MSBS is validated with a reference sinusoidal sweep perturbation, and the corresponding root-mean-square error (RMSE) analysis is performed. Moreover, the accuracy of the presented multichannel EIS system is validated by impedance spectra measurements of Samsung INR18650-29E batteries and compared with those measured using a commercial EIS instrument. A chi-squared error under 0.641% is obtained for all channels. The non-linearity of batteries has a significant impact on the quality of impedance spectra. Therefore, Kronig–Kramer (KK) transform validation is also performed. Full article

With the global surge in electric vehicle (EV) deployment, driven by enhanced environmental regulations and efforts to reduce transportation-related greenhouse gas emissions, managing the life cycle of Li-ion batteries becomes more critical than ever. A crucial step for battery reuse or recycling is the precise estimation of static capacity at retirement. Traditional methods are time-consuming, often taking several hours. To address this issue, a machine learning-based approach is introduced to estimate the static capacity of retired batteries rapidly and accurately. Partial discharge data at a 1 C rate over durations of 6, 3, and 1 min were analyzed using a machine learning algorithm that effectively handles temporally evolving data. The estimation performance of the methodology was evaluated using the mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). The results showed reliable and fairly accurate estimation performance, even with data from shorter partial discharge durations. For the one-minute discharge data, the maximum RMSE was 2.525%, the minimum was 1.239%, and the average error was 1.661%. These findings indicate the successful implementation of rapidly assessing the static capacity of EV batteries with minimal error, potentially revitalizing the retired battery recycling industry. Full article

Over the last 50 years since Whittingham created the world’s first lithium-ion battery (LIB) in 1970, LIBs have continued to develop and have become mainstream for electric vehicle (EV) batteries. However, when an LIB for an EV reaches 80% of its state of health (SOH), although it still retains about 80% of its capacity, it is no longer suitable for use in general EVs and must be retired. This is problematic because not only is a retired LIB still viable for use and not totally obsolete, if not properly disposed of, a retired LIB may cause environmental pollution on top of being a waste of resources. Therefore, the reuse of retired LIBs from EVs is increasingly important. This paper uses circular economy (CE) and extended producer responsibility (EPR) as a theoretical basis to deal with the disposal of retired LIBs from EVs in Taiwan from legal, technical, and economic perspectives, and hopes to provide suggestions for the reuse of retired LIBs from EVs in Taiwan. Full article