The role of interfacial effects on an ion-conducting poly(styrene-b-ethylene oxide) (PS-b-PEO or SEO) diblock copolymer doped with lithium bis(trifluoromethanesulfonyl) imide (LiTFSI) was investigated by electrochemical impedance spectroscopy (EIS). Coating the surface of commonly used stainless steel electrodes with a specific random copolymer brush increases the measured ionic conductivity by more than an order of magnitude compared to the uncoated electrodes. The increase in ionic conductivity is related to the interfacial structure of the block copolymer domain morphology on the electrode surface. We show that the impedance associated with the electrode–electrolyte interface can be detected using nonmetallic electrodes, allowing us to distinguish the ionic conductivity behaviors of the bulk electrolyte and the interfacial layers for both as-prepared and annealed samples.

This paper presents three approaches to estimating the battery parameters of the electrical equivalent circuit model (ECM) based on electrochemical impedance spectroscopy (EIS); these approaches are referred to as (a) least squares (LS), (b) exhaustive search (ES), and (c) nonlinear least squares (NLS). The ES approach is assisted by the LS method for the rough determination of the lower and upper bound of the ECM parameters, and the NLS approach is incorporated with the Monte Carlo run such that different initial guesses can be assigned to improve the goodness of EIS fitting. The proposed approaches are validated using both simulated and real EIS data. Compared to the LS approach, the ES and NLS approaches show better fitting accuracy at various noise levels, whereas in both the validation using simulated EIS data and actual EIS data collected from LG 18650 and Molicel 21700 batteries, the NLS approach shows better fitting accuracy than that of LS and ES approaches. In all cases, compared with the ES approach, the computational time of the NLS approach is significantly faster, and compared with the LS approach, the NLS approach shows a minimal difference in computational time and considerably better fitting performance.

Accurate prediction of lithium-ion batteries’ (LIBs) state-of-health (SOH) is crucial for the safety and maintenance of LIB-powered systems. This study addresses the variability in degradation trajectories by applying gated recurrent unit (GRU) networks alongside principal component analysis (PCA), Granger causality, and K-means clustering to analyze the relationships between operating conditions—such as temperature and load profiles—and battery performance degradation. This paper uses a publicly accessible dataset derived by aging three prismatic LIB cells under a realistic forklift operation profile. First, we identify the features that are relevant to driving variance, then we employ the winning algorithm of K-means clustering for the classification of operational states. Granger causality later investigates the inter-group relationships. Our GRU-BPP model achieves an RMSE value of 0.167 and an MAE of 0.129 for the reference performance testing (RPT) dataset and an RMSE of 0.032 with an MAE of 0.025 for the aging dataset, thus outperformed benchmark methods such as GRU, LME, and XGBoost. These results further enhance the predictiveness and robustness of this approach and yield a holistic solution to the conventional challenges in battery management and their remaining useful life (RUL) predictions.

Aqueous zinc-ion hybrid capacitors (ZIHCs) have emerged as a promising technology, showing superior energy and power densities, as well as enhanced safety, inexpensive and eco-friendly features. Although ZIHCs possess the advantages of both batteries and supercapacitors, their energy density is still unsatisfactory. Therefore, it is extremely crucial to develop reasonably matched electrode materials. Based on this challenge, a surge of studies has been conducted on the modification of carbon-based electrode materials. Herein, we first summarize the progress of the related research and elucidate the energy storage mechanism associated with carbon-based electrodes for ZIHCs. Then, we investigate the influence of the synthesis routes and modification strategies of the electrode materials on electrochemical stability. Finally, we summarize the current research challenges facing ZIHCs and predict potential future research pathways. In addition, we suggest key scientific questions to focus on and potential directions for further exploration.

The coupling of solar cells and Li-ion batteries is an efficient method of energy storage, but solar power suffers from the disadvantages of randomness, intermittency and fluctuation, which cause the low conversion efficiency from solar energy into electric energy. In this paper, a circuit model for the coupling system with PV cells and a charge controller for a Li-ion battery is presented in the MATLAB/Simulink environment. A new three-stage charging strategy is proposed to explore the changing performance of the Li-ion battery, comprising constant-current charging, maximum power point tracker (MPPT) charging and constant-voltage charging stages, among which the MPPT charging stage can achieve the fastest maximum power point (MPP) capture and, therefore, improve battery charging efficiency. Furthermore, the charge controller can improve the lifetime of the battery through the constant-current and constant-voltage charging scheme. The simulation results indicate that the three-stage charging strategy can achieve an improvement in the maximum power tracking efficiency of 99.9%, and the average charge controller efficiency can reach 96.25%, which is higher than that of commercial chargers. This work efficiently matches PV cells and Li-ion batteries to enhance solar energy storages, and provides a new optimization idea for hybrid PV/Li-ion systems.

Due to challenges in manufacturing composite cathodes with oxide solid electrolytes, new cell concepts are emerging in which the infiltration of solid-polymer electrolyte (SPE) into 3D cathode pore structures improves capacity retention and cycling stability. However, the performance limitation and the resulting practical relevance of such a hybrid concept have not yet been analyzed and discussed. This study investigates the impact of laser-ablated geometric structures on the performance of hybrid solid-state batteries (SSBs). A Doyle–Fuller–Newman modeling approach is developed and parameterized for structured hybrid SSBs that incorporate a PEO/LiTFSI SPE and an LLZO ceramic separator, as well as NMC-811 and Li-metal for the positive- and negative-electrode active materials. Comparison between structured and planar cell designs reveals significant rate capability improvements in structured designs due to reduced diffusion and interfacial charge transfer polarization. A sensitivity analysis of geometric structure parameters shows further potential for performance improvement in terms of specific capacity and energy density. However, current constriction effects in the LLZO separator can deteriorate the rate capability. A more general perspective is then taken by analyzing the impact of changing SPE parameters. An energy density of 128 Wh kg−1 at 1C, and 220 Wh kg−1 at 1C with improved SPE parameters is achieved in the best case, approaching the target of 250 Wh kg−1 , which is currently achieved for conventional Li-ion batteries.

This teaching aid aims to illustrate a range of the most common materials in end-of-life (EoL) lithium-ion batteries (LIBs) to demonstrate the usefulness of advanced organic petrography in the characterization of EoL LIB materials and to assess the efficiency of LIB recycling processes from the pre-processing stage up to the impurities of the metallurgical processes. Additionally, it may be useful for students, petrographers, and professionals in battery development and recycling.

This study introduces a novel Sequence-to-Sequence (Seq2Seq) deep learning model for predicting lithium-ion batteries’ remaining useful life. We address the challenge of extrapolating battery performance from high-rate to low-rate charging conditions, a significant limitation in previous studies. Experiments were also conducted on commercial cells using charge rates from 1C to 3C. Comparative analysis of fully connected neural networks, convolutional neural networks, and long short-term memory networks revealed their limitations in extrapolating to untrained conditions. Our Seq2Seq model overcomes these limitations, predicting charging profiles and discharge capacity for untrained, low-rate conditions using only high-rate charging data. The Seq2Seq model demonstrated superior performance with low error and high curve-fitting accuracy for 1C and 1.2C untrained data. Unlike traditional models, it predicts complete charging profiles (voltage, current, temperature) for subsequent cycles, offering a comprehensive view of battery degradation. This method significantly reduces battery life testing time while maintaining high prediction accuracy. The findings have important implications for lithium-ion battery development, potentially accelerating advancements in electric vehicle technology and energy storage.