Power Lithium-ion Batteries Research Articles

Intelligent solar light electric vehicle moving in hot regions under a hybrid power source strategy

Solar Light Electric Vehicles (ISLEV) have recently gained increasing attention as a promising solution for sustainable transportation. In hot regions, such as the BECHAR region in southwest Algeria, where temperatures can exceed 50 °C, the challenge of maintaining high performance standards while coping with challenging environmental conditions is critical. To address this challenge, this study proposes an intelligent hybrid power source strategy comprising SUN Power solar PV and lithium-ion battery supercapacitors. To optimize the vehicle's performance, a feedforward neural network FF-NN is employed, which regulates the power flow between the hybrid sources. The proposed system's efficiency and effectiveness are assessed through simulations, which reveal its robustness in coping with challenging environmental conditions while maintaining high performance standards. The results show that the proposed system holds con-siderable potential for sustainable and efficient transportation in hot regions and can have broad applications in diverse sectors. Future industrial vehicle designs must incorporate the choice of hybrid power management into their planning process to ensure the efficient use of energy and optimize the vehicle's performance.

Research progress of aerogel used in lithium-ion power batteries

In recent years, with the rapid development of clean energy vehicles (i.e., electric vehicles and hybrid electric vehicles) and electrochemical energy storage stations, safety accidents have significantly increased. However, the abuse conditions such as overheating, overcharging, mechanical abuse, short circuits, etc., can result in thermal runaway of lithium-ion batteries (LIBs). Severe thermal runaway can lead to battery fire and even explosion, thereby threatening the safety of personnel. The application of a few aerogels to the thermal insulation layer between the cells of the lithium-ion battery modules can strengthen the safety of batteries. Among many aerogels, oxide aerogels show excellent insulation and high-temperature resistance. Therefore, aerogels, especially oxide aerogels, have aroused widespread research interest. This paper introduces the mechanism of thermal runaway of LIBs and systematically reviews several important oxide aerogels and the derived composites, especially those based on SiO2, Al2O3, and ZrO2 aerogels. The mechanical strength and high-temperature resistance of those aerogels are discussed. The application of aerogel in LIBs is also investigated in detail, and the all-around effect is caused by the introduction of aerogel materials. In addition, the thermal protection safety strategy of aerogel thermal insulation layers is proposed. It is supposed that this review could enable readers to deepen their understanding of this field.

A Fault Diagnosis Method for Electric Vehicle Lithium Power Batteries Based on Dual Feature Extraction from the Time and Frequency Domains

Abstract This study focuses on the safety and reliability issues of lithium-ion batteries, proposing a fault diagnosis strategy that leverages dual feature extraction in the time-frequency domains. Additionally, by modifying the traditional autoencoder, the study proposes a feature-guided autoencoder as an unsupervised model for extracting features in the time domain. Initially, wavelet packet decomposition and its energy denoising treatment are employed to refine fault information within battery voltage signals. Subsequently, the reconstruction error outputted by the Feature-Guided Autoencoder is utilized as the time-domain fault feature, while the cosine similarity of the energy of signals in various frequency bands obtained after wavelet packet decomposition serves as the frequency-domain fault feature. Ultimately, this paper selects the Isolation Forest algorithm for two-dimensional outlier detection of time-frequency features. Experimental results demonstrate that the Feature-Guided Autoencoder proposed in this study not only enhances the sensitivity of time-domain fault features compared to traditional autoencoders and their variants but also optimizes issues related to training time and computational load. The effectiveness of the proposed dual feature fault diagnosis method in both the time and frequency domains is validated through data from two actual vehicles, showing superior early fault detection capability relative to single-feature fault diagnosis methods.

Exploration of physical recovery techniques and economic viability for retired lithium nickel cobalt manganese oxide-type lithium-ion power batteries

Exploration of physical recovery techniques and economic viability for retired lithium nickel cobalt manganese oxide-type lithium-ion power batteries

Simulation analysis of low-temperature heat generation of power batteries based on the ECM model

Abstract When the power battery operates in low temperatures, it encounters challenges with charging and discharging, leading to a decline in battery performance, which can prevent electric vehicles from starting and reduce their driving range. To address this, the battery must possess the capability to adapt to both extremely high and low temperatures. This article employs ANSYS software to investigate the heat generation in batteries due to their own charging and discharging processes at different discharge rates under -20°C. When the battery is heated at a 1 C discharge rate for 20 minutes, its temperature rises by approximately 1.1°C; at 2 C, the temperature rises by about 5°C; and at 3 C, it rises by about 11°C. The greater the discharge rate is, the more heat is generated, resulting in a higher temperature rise. Furthermore, prolonged heating at a certain discharge rate can elevate the temperature to a level that is optimal for lithium-iron-phosphate batteries, ultimately reaching their optimal operating range. This research contributes to enabling lithium-ion power batteries to function normally in low-temperature environments, without compromising their lifespan. It is imperative to heat the interior of lithium-ion power batteries to ensure they can start normally after prolonged overnight parking, through preheating.

Advancements and Current Developments in Integrated System Architectures of Lithium-Ion Batteries for Electric Mobility

Recognizing the challenges faced by power lithium-ion batteries (LIBs), the concept of integrated battery systems emerges as a promising avenue. This offers the potential for higher energy densities and assuaging concerns surrounding electric vehicle range anxiety. Moreover, mechanical design optimization, though previously overlooked, is gaining traction among researchers as a viable alternative to achieve enhanced energy and power densities. This review paper provides a comprehensive overview of recent research and progress in this domain, emphasizing the significance of battery architectures in enabling the widespread adoption of electric mobility. Beginning with an exploration of fundamental principles underlying LIB systems, the paper discusses various architectures involving different cell form factors, like pouch cells, cylindrical cells, and prismatic cells, along with their advantages and limitations. Furthermore, it reviews recent research trends, highlighting innovations aimed at enhancing battery performance, energy density, and safety through advanced battery system architecture. Through case studies and discussions on challenges and future directions, the paper underscores the critical role of advanced battery system architecture in driving the evolution of e-mobility and shaping the sustainable transportation landscape.

Thermal Runaway and Thermal Management of Lithium-Ion Power Batteries in New Energy Vehicles

Thermal Runaway and Thermal Management of Lithium-Ion Power Batteries in New Energy Vehicles

Nontarget Analysis of Legacy and Emerging PFAS in a Lithium-Ion Power Battery Recycling Park and Their Possible Toxicity Measured Using High-Throughput Phenotype Screening.

Driven by the global popularity of electric vehicles and the shortage of critical raw materials for batteries, the spent lithium-ion power battery (LIPB) recycling industry has exhibited explosive growth in both quantity and scale. However, relatively little information is known about the environmental risks posed by LIPB recycling, in particular with regards to perfluoroalkyl and polyfluoroalkyl substances (PFAS). In this work, suspect screening and nontarget analysis were carried out to characterize PFAS in soil, dust, water and sediment from a LIPB recycling area. Twenty-five PFAS from nine classes were identified at confidence level 3 or above, including 13 legacy and 12 emerging PFAS, as well as two ultrashort-chain PFAS. Based on the target analysis of 16 PFAS, at least nine were detected in each environmental sample, indicating their widespread presence in a LIPB recycling area. Perfluorodecanoic acid, perfluorooctanesulfonic acid and trifluoromethanesulfonamide showed significant differences in the four phenotypic parameters (growth, movement, survival and fecundity) of Caenorhabditis elegans and were the most toxic substances in all target PFAS at an exposure concentration of 200 μM. Our project provides first-hand information on the existence and environmental risk of PFAS, facilitating the formulation of regulations and green development of the LIPB recycling industry.

High ionic conductivity materials Li3YBr6 and Li3LaBr6 for solid-state batteries: first-principles calculations

High ionic conductivity solid-state electrolytes are essential for powerful solid-state lithium-ion batteries. With density functional theory andab initiomolecular dynamics simulations, we investigated the crystal structures of Li3YBr6and Li3LaBr6. The lowest energy configurations with uniform distribution of lithium ions were identified. Both materials have wide electrochemical stability windows (ESW): 2.64 V and 2.57 V, respectively. The experimental ESW for Li3YBr6is 2.50 V. Through extrapolating various temperature diffusion results, the conductivity of Li3YBr6was obtained at room temperature, approximately 3.9 mS cm-1, which is comparable to the experimental value 3.3 mS cm-1. Li3LaBr6has a higher conductivity, a 100% increase compared with Li3YBr6. The activation energies of Li3YBr6and Li3LaBr6through the Arrhenius plot are 0.26 eV and 0.24 eV, respectively, which is also close to the experimental value of 0.30 eV for Li3YBr6. This research explored high ionic conductivity halide materials and will contribute to developing solid-state lithium-ion batteries.

Study on the interface of high specific energy High stability

High nickel terterial oxide, with high specific capacity and low cost advantages, is the preferred positive electrode for high specific energy lithium-ion power batteries at present. However, the high nickel ternary cathode material is easy to have side reactions with the electrolyte in the high delithium state, which can not only lead to the dissolution of transition metal ions and the formation of the surface salt phase, but also cause the lattice oxygen loss and surface interface side reactions, resulting in rapid attenuation of electrode capacity. In this paper, the development history and structural characteristics of high nickel ternary cathode materials are introduced, and the surface interface problems leading to capacity attenuation and structural degradation are comprehensively analyzed. From this perspective, three effective strategies for improving electrochemical performance of high nickel ternary cathode materials are summarized. Finally, by describing the advantages and disadvantages of these layered cathode materials, the challenges faced by these layered cathode materials are discussed, and it is pointed out that in the future, composite modification strategies should be adopted to optimize their structures and improve their properties.

Prediction of the health state of lithium-ion power batteries using machine learning

Faced with the increasingly urgent issues of energy depletion and environmental pollution, various countries are actively supporting the new energy vehicle industry, making electric vehicle technology a focus of widespread attention among researchers. The Battery Management System (BMS) plays a crucial role in overseeing the power battery's operational parameters, with the estimation of the State of Health (SOH) being a pivotal function. Accurate estimation of SOH can improve the utilization efficiency and endurance of power batteries, extend their service life, and help users achieve the best balance between system safety and economic benefits. Machine learning methods provide a new solution to the SOH estimation problem. Without analyzing the complex aging mechanisms inside the battery, online SOH estimation can be completed by learning from historical aging data. In this study, the lithium battery dataset provided by NASA is used and trained through multiple linear regression models and support vector machine models to predict the SOH of lithium-ion batteries. The results demonstrate the accuracy and reliability of both methods in predicting SOH. Simultaneously, the prediction results of different feature values are compared to obtain the most accurate combination of feature values.

An Improved Collaborative Estimation Method for Determining The SOC and SOH of Lithium-Ion Power Batteries for Electric Vehicles

With the increase in the amount of actual operating data on electric vehicles, how to analyze and process useful information from existing battery charging and discharging data and apply it to subsequent state estimation is worthy of in-depth thinking and practice by researchers. This article proposes a collaborative estimation architecture for SOC and SOH based on the 1RC equivalent circuit model, recursive least squares, and adaptive extended Kalman filtering algorithms (AEKF), which combine offline data processing with online applications. By applying offline data processing, OCV–SOC polynomial fitting and average polarization resistance were determined, which reduced the time required for basic data measurement and improved the accuracy of model parameter identification, while a recursive estimation combining micro- and macro-time-scales of AEKF was used for the online real-time estimation of the SOC and actual available capacity of batteries, in order to eliminate interference from measurement and process noise. The results of the simulated and experimental data validation indicate that the proposed algorithm is applicable to the lithium-ion batteries studied in this paper, the average SOC deviation is less than 1.5%, the maximum deviation is less than 2.02%, and the SOH estimation deviation is less than 1% under different driving conditions in the multi-temperature range. This study lays the foundation for further utilizing offline data and improving SOC and SOH collaborative estimation algorithms.

Real time prediction algorithm for SOC of lithium ion power battery under high pulse rate

Real time prediction algorithm for SOC of lithium ion power battery under high pulse rate

SOC Estimation of Power Lithium Battery Based on RGC and Multi-innovation UKF Joint Algorithm

SOC Estimation of Power Lithium Battery Based on RGC and Multi-innovation UKF Joint Algorithm

An optimized multi-segment long short-term memory network strategy for power lithium-ion battery state of charge estimation adaptive wide temperatures

With the development of intelligentization and network connectivity of new energy vehicles, the estimation of power lithium-ion battery state of charge (SOC) using artificial intelligence methods is becoming a research hotspot. This paper proposes an optimized multi-segment long short-term memory (MSLSTM) network strategy for SOC estimation of powered lithium-ion batteries' adaptive wide temperatures. First, the multi-timescale electrochemical processes during the charging and discharging of power lithium-ion batteries are efficiently analyzed, and the analytically measurable external parameters are classified into subsets based on the analysis. Secondly, the idea of segment long short-term memory (SLSTM) estimation is proposed to enhance the data linkage between the SOC and the nonlinearly varying parameters and to improve the prediction accuracy. Finally, an optimized MSLSTM neural network is proposed for nonlinear regression prediction of SOC in subset intervals through a combination of segmented estimation idea and SLSTM neural network. The proposed algorithm is validated under a variety of temperatures and operating conditions, and the accuracy of the SOC estimation is improved by at least 66.770 % or more. It provides a solution idea for intelligent estimation of power lithium-ion battery SOC.

Effects of non-subsidized industrial policies on embedding position of power lithium-ion battery manufacturers in global value chain: Firm level evidence from China

Given the phase-out of subsidies, the Chinese government has issued a series of non-subsidized industrial policies to support the development of the power Lithium-ion Battery (PLiB) industry, recognized as pivotal for achieving carbon neutrality in the transportation department. However, existing literature provides limited evidence on the contribution of non-subsidized industrial policies, particularly to the PLiB industry. To bridge this gap, using data of 31 PLiB listed firms and 279 relevant non-subsidized industrial policies, this paper analyzes the effects of non-subsidized industrial policies on the global value chain (GVC) embedding position. The results reveal that non-subsidized industrial policies significantly raise the GVC embedding position of Chinese PLiB firms, and the positive effects are prone to increase over the research period. Further investigation shows that there is heterogeneity among PLiB firms, with the raising effects on the GVC embedding position being more significant in eastern and middle PLiB firms, private PLiB firms and midstream PLiB firms. The mechanism verification shows that non-subsidized industrial policies raise GVC embedding position of PLiB firms through technological innovation effect and scale economy effect, while competitiveness effect does not give full play. These findings provide important recommendations for better design of the industrial policies to improve the international competitiveness of PLiB industry and other strategic emerging industries.

Trust Improvement Consensus Model Considering Unreliability Degree of Opinions with Hesitant Fuzzy Sets

Trust is a crucial element in the consensus decision-making process, as it significantly impacts a group’s capacity to achieve consensus. Nevertheless, low trust by experts may result in detrimental decision-making behaviors that decrease group consensus degree. Meanwhile, it is important to note that experts’ opinions are not always reliable, and ignoring the unreliability degree of opinions may have an impact on the result of consensus decision-making. To deal with these issues, this paper proposes a trust improvement consensus model considering unreliability degree of opinions. First, considering that the discreteness of membership degree has a much smaller impact on the hesitancy degree in hesitant fuzzy elements than the number of membership degrees in extant research, an improved approach is suggested to quantify the unreliability degree of opinions. Second, a trust improvement method is proposed based on the score function for trust propagation path and the trust propagation operator considering unreliability degree of opinions. Third, a method for adjusting the opinions of experts is suggested, considering unreliability degree of opinions. Finally, the feasibility, effectiveness, and advantages of the proposed trust improvement consensus model are verified through a case study on the selection of cascade utilization alternatives for power lithium-ion batteries, as well as simulation and comparative analysis. The results show that: (1) The improved method exhibits a significant impact of the discreteness of membership degree on the hesitancy degree; (2) Trust improvement can facilitate group consensus, and the score function of trust propagation path can enhance trust degree among experts more effectively; (3) The unreliability degree of opinions not only affects the degree of trust improvement but also affects the ranking of alternatives. By taking into account the unreliability degree of opinions throughout the process of adjusting opinions, it is possible to promote group consensus and decrease the unreliability degree of opinions.

Multi-model feature aggregation for classification of laser welding images with vision transformer

The laser welding technique is quite common in various manufacturing lines, including those for lithium-ion power batteries, due to its remarkable productivity, effectiveness, and flexibility. However, it has been observed that the consistency of the welding quality is not always optimal. This study investigates the adoption of laser welding in the manufacturing of lithium-ion batteries, such as their anode, cathode and safety vent, where consistent weld quality is crucial for battery performance and safety. Therefore, evaluating the laser-welding product is indispensable for industrial production and the public domain. Several techniques have been utilized for laser welding evaluation, both destructive and non-destructive. However, these methods are ineffective and too cumbersome to be adopted in mass manufacturing. In contrast, a machine vision strategy has recently been adopted to distinguish between successful and unsuccessful laser welding items. This opened new perspectives for evaluating the quality of the laser welding product using digital image techniques. However, these methods cannot deliver outstanding performance across multiple laser-welding products and achieve optimal classification accuracy. Deep learning has evolved remarkably in recent years and gained popularity in detecting welding defects. This paper presents the observation of a Hybrid Vision Transformer (HViT) to classify the laser welding images corresponding to their feature patch images based on multi-model feature aggregation. The VGG-16 and MobileNet, which were pre-trained on ImageNet, were utilized as the core models to extract the rich features of the laser welding image. To integrate these features, the squeeze excitation module (SE) was utilized, and a multi-layer perception (MLP) approach with a label smoothing optimizer was used for classification. To determine the effectiveness of the proposed strategy, a comparative analysis was conducted with a multitude of alternative machine learning and deep learning approaches. The results indicate that the proposed strategy surpasses all other methods in terms of classification accuracy and evaluation metrics on the test dataset.

Eco-friendly closed-loop recycling of nickel, cobalt, manganese, and lithium from spent ternary lithium-ion battery cathodes

Recycling technology is essential for managing waste and addressing environmental issues related to scrapping power lithium batteries. A closed-loop recycling technique was proposed in this work for maximizing usage of lithium (Li), manganese (Mn), cobalt (Co), and nickel (Ni) resources in spent ternary lithium battery (SNCMB) cathodes. A green and sustainable leaching process utilizes citric acid (CA) and hydrogen peroxide (HP) to efficiently extract Ni, Co, Mn, and Li from SNCMB cathodes. Maximizing leaching rates for Ni (96.69 %), Co (95.38 %), Mn (97.64 %), and Li (98.72 %) under optimal conditions (80 ℃, 4 mol/L CA, 20 g/L solid to liquid (S/L) ratio, 2 vol% HP, 70 min). The Avrami equation models leaching kinetics, requiring a temperature of 80 ℃ to accelerate the process. Hydrogen-type strong acidic cation exchange resin (HR) effectively adsorbs Ni, Co, Mn, and Li ions from leachates through ion exchange under optimal conditions, reducing costs and eliminating the need for separating metals with similar chemical properties. This multi-stage adsorption recycling process renders leachates both economical and eco-friendly. The metal-enriched HR composite (HRC) from the ion-exchange process prepares ternary lithium battery (NCMB) cathodes to maximize metal usage. Microwave heating achieves excellent structure and electrochemical performance of regenerated ternary lithium battery (RNCM) cathodes, with 153.81 mAh/g discharge capacity at 0.1C rate and 90.92 % capacity retention after 50 cycles at 900 ℃ for 4 h. This innovative, efficient, and eco-friendly technique allows for closed-loop recycling of Ni, Co, Mn, and Li from SCNMB cathodes, providing new avenues for lithium battery recycling.

State of Health (SOH) Estimation of Lithium-Ion Batteries Based on ABC-BiGRU

As a core component of new energy vehicles, accurate estimation of the State of Health (SOH) of lithium-ion power batteries is essential. Correctly predicting battery SOH plays a crucial role in extending the lifespan of new energy vehicles, ensuring their safety, and promoting their sustainable development. Traditional physical or electrochemical models have low accuracy in measuring the SOH of lithium batteries and are not suitable for the complex driving conditions of real-world vehicles. This study utilized the black-box characteristics of deep learning models to explore the intrinsic correlations in the historical cycling data of lithium batteries, thereby eliminating the need to consider the internal chemical reactions of lithium batteries. Through Pearson correlation analysis, this study selects health indicators (HIs) from lithium battery cycling data that significantly impact SOH as input features. In the field of lithium batteries, this paper applies ABC-BiGRU for the first time to SOH prediction. Compared with other recursive neural network models, ABC-BiGRU demonstrates superior predictive performance, with maximum root mean square error and mean absolute error of only 0.016799317 and 0.012626847, respectively.