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Li-ion batteries are important in modern technology, especially for drones, due to their high energy density, long cycle life, and lightweight properties. Predicting their performance is crucial for enhancing drone flight safety, optimizing operations, and reducing costs. This involves using advanced techniques like machine learning (e.g., Convolutional Neural Network-CNNs, Recurrent Neural Network-RNNs), statistical modeling (e.g., Kalman Filtering), and explainable AI (e.g., SHAP, LIME, PDP) to forecast battery behavior, extend battery life, and improve drone efficiency. The study aims to develop a CNN-RNN-based ensemble model, enhanced with explainable AI, to predict key battery metrics during drone flights. The model's predictions will aid in enhancing battery performance via continuous, data-driven monitoring, improve drone safety, optimize operations, and reduce greenhouse gas emissions through advanced recycling methods. In the present study, comparisons are made for the behaviors of two different drone Li-ion batteries, numbered 92 and 129. The ensemble model in Drone 92 showed the best performance with MAE (0.00032), RMSE (0.00067), and R2 (0.98665) scores. Similarly, the ensemble model in Drone 129 showed the best performance with MAE (0.00030), RMSE (0.00044), and R2 (0.98094) performance metrics. Similar performance results are obtained in the two predictions. However, drone 129 has a minimally lower error rate. When the Partial Dependence Plots results, which are one of the explainable AI (XAI) techniques, are interpreted with the decision tree algorithm, the effect of the Current (A) value on the model estimations in both drone flights is quite evident. When the current value is around −4, the model is more sensitive and shows more changes. This study will establish benchmarks for future research and foster advancements in drone and battery technologies through extensive testing. [ABSTRACT FROM AUTHOR]

This paper presents experimental investigations into a hybrid energy storage system comprising directly parallel connected lead-acid and lithium batteries. This is achieved by the charge and discharge cycling of five hybrid battery configurations at rates of 0.2–1C, with a 10–50% depth of discharge (DoD) at 24 V and one at 48 V. The resulting data include the overall round-trip efficiency, transient currents, energy transfers between the strings, and the amount of energy discharged by each string across all systems. The general observation is that the round-trip efficiency drops from a maximum of around 94–95% in the first stages of the charge/discharge process, when only the Li-ion strings are active, to around 82–90% when the lead-acid strings reach a DoD of up to 50%. The most important parameters in the round-trip efficiency function are the ratio between the Li-ion and lead-acid energy available and the charge/discharge current. The energy transfer between the strings, caused by the transient currents, is negligible in the first stages of the discharge and then grows, with the DoD peaking at around 60% DoD. Finally, during the first stage of discharge, when only the Li-ion strings are active, the amount of energy discharged varies with the discharge C rate, decreasing to almost half at between 0.2 and 1C. [ABSTRACT FROM AUTHOR]

Lithium iron phosphate (LFP) has become a focal point of extensive research and observation, particularly as a cathode for lithium-ion batteries. It has extensive uses in electric vehicles, stationary power storage systems, and portable electronic devices. To further enhance the performance, one crucial area of focus is optimizing the cathode materials. This optimization involves improving key parameters such as conductivity, rate capability, and energy density. In this context, researchers have explored various cathode materials in combination with different conducting polymers, including poly(aniline), poly(thiophene), poly(pyrrole), poly(acetylene), and more. These conducting polymers are integrated into the cathode to boost the overall electrochemical behavior of LFP batteries. The objective is to assess how these electrochemical properties of conducting polymers influence the overall performance of LFP batteries. This research aims to provide a complete evaluation of conducting polymer-based cathode materials and to establish a solid foundation for selecting suitable polymers that support effectively as a cathode material. Such investigations are pivotal for advancing the development of these batteries with improved capabilities, ultimately leading to more efficient and reliable energy storage solutions intended for a varied choice of applications. [ABSTRACT FROM AUTHOR]

The safety concerns associated with current lithium-ion batteries are a significant drawback. A short-circuit within the battery's internal components, such as those caused by a car accident, can lead to ignition or even explosion. To address this issue, a polymer shear thickening electrolyte, free from flammable solvents, has been developed. It comprises a star-shaped oligomer derived from a trimethylolpropane (TMP) core and polyether chains, along with the inclusion of 20 wt.% nanosilica. Notably, the star-shaped oligomer serves a dual function as both the solvent for the lithium salt and the continuous phase of the shear thickening fluid. The obtained electrolytes exhibit an ionic conductivity of the order of 10−6 S cm−1 at 20 °C and 10−4 S cm−1 at 80 °C, with a high Li+ transference number (t+ = 0.79). A nearly thirtyfold increase in viscosity to a value of 1187 Pa s at 25 °C and a critical shear rate of 2 s−1 were achieved. During impact, this electrolyte could enhance cell safety by preventing electrode short-circuiting. [ABSTRACT FROM AUTHOR]

The development of water‐based composite cathode slurries for Li‐ion batteries is hindered by adverse interactions between the battery active material and water. Insights into interactions between H2O and LiNi0.5Mn1.5O4 (LNMO) are studied using a thin‐film model system. The reactivity of this active material with H2O is evaluated at different lithiation states, showing that protonation of the active material in aqueous environments is electrochemically driven, rather than a chemical Li+/H+ ion exchange reaction. The electrochemically driven mechanism describing the protonation of LNMO is only present when the material is in its partially delithiated form. These findings suggest it is possible to cast this material from an aqueous precursor when lithiated, as long as there are no traces of it when the electrochemical delithiation (cell charging) is carried out. [ABSTRACT FROM AUTHOR]

Lithium bis(trifluoromethanesulfonyl)imide (LiFSI) is a promising alternative salt for Li‐ion batteries. Unlike the conventional LiPF6, it is not prone to HF formation, and thus resistant to moisture. However, for cell voltages relevant for high energy cathodes (>4.2 V), the aluminium current collector will corrode in electrolytes based on this salt, and mitigation strategies are needed. Here, the use of Lithium tetrafluoroborate (LiBF4) and Lithium difluoro(oxalato)borate (LiDFOB) salts as additives is investigated, in order to enable the use of LiFSI‐based electrolytes. The performance of the electrolytes is evaluated separately for high content silicon anodes, (NMC442) cathodes and the aluminium current collector by electrochemical methods and post mortem analysis by SEM imaging and X‐ray photoelectron spectroscopy (XPS). Electrolytes with LiDFOB as additive showed the best performance for all components, and were therefore selected for cycling in full cells, composed of silicon anodes and NMC442. Results show that LiFSI‐based electrolytes with LiDFOB additive has an electrochemical performance similar to conventional electrolytes, and is thus a competitive, alternative electrolyte with a low fluorine content. Furthermore, it is verified that the good SEI forming properties of LiFSI based electrolytes known from cycling in half cells, is also preserved during cycling in full cells [ABSTRACT FROM AUTHOR]

Battery Management System (BMS) functions to monitor individual cell in a battery pack and its crucial task is to maintain stability throughout the battery pack. The BMS is responsible for maintaining the safety of the battery as well as not to harm the user or environment. The parameters that are to be monitored in a battery are Voltage, Current and Temperature. With the collected data, BMS carefully monitors the charging–discharging behavior of the battery particularly in the Lithium-ion (Li-ion) batteries in which charging and discharging behavior are completely different. This paper proposes a real-time IOT connected deep learning algorithm for estimation of State-of-Charge (SoC) of Li-ion batteries. This paper provides unique objectives and congruence between model-based conventional methods and state-of-the-art deep learning algorithm, specifically Feed Forward Neural Network (FNN) which is nonRecurrent. This paper also highlights the advantages of Internet-of-Things (IoT) connected deep learning algorithm for estimation of State-of-Charge of Li-ion batteries in Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs). The major advantage of the proposed method is that the Artificial Intelligence (AI)-based techniques aim to bring the estimation error less than 2% at a low cost and time without the model of the battery, at par with conventional method of Extended Kalman Filter (EKF) which is the best ever practical estimation theory. Another advantage of the proposed method is that in an abnormal condition (i.e., Unsafe Temperature) the IF This Then That (IFTTT) IoT mobile application interfaced with BMS through ThingSpeak cloud, sends a notification alert to the battery expert or to the user prior to an emergency. Finally, the real-time data of the battery parameters are collected through ThingSpeak cloud platform for future research and analysis. [ABSTRACT FROM AUTHOR]

Many significant parameters show the performance of batteries used in important fields such as biomedical systems, energy storage units, electric vehicle technologies, and advanced space studies. Two essential indicators among these parameters are open-circuit voltage and state of health. In this study, it is tried to estimate open-circuit voltage and state of health with high accuracy by applying optimization methods on the Thevenin electrical equivalent circuit model of batteries. The parameter values obtained by examining the discharge tests of the Li-ion battery cell with 2A constant current during the 150 charge/discharge cycle time at 25 °C are transferred to the electrical equivalent circuit model. Curve-fitting method, artificial bee colony, particle swarm optimization, dragonfly algorithm, and genetic algorithm have been studied in the prediction operation of open-circuit voltage and state of health which is defined based on state of charge, number of cycles, rated current capacity, and time. Comparisons are made considering the absolute error values, the smallest value of the sum of the squares of the errors, the response speed of the methods, and the correct estimation ability. Ultimately, it is aimed to obtain the most suitable method. [ABSTRACT FROM AUTHOR]

Li-ion batteries are integral to various applications, ranging from electric vehicles to mobile devices, because of their high energy density and user friendliness. The assessment of the Li-ion state of heath stands as a crucial research domain, aiming to innovate safer and more effective battery management systems that can predict and promptly report any operational discrepancies. To achieve this, an array of machine learning (ML) and artificial intelligence (AI) methodologies have been employed to analyze data from Li-ion batteries, facilitating the estimation of critical parameters like state of charge (SoC) and state of health (SoH). The continuous enhancement of ML and AI algorithm efficiency remains a pivotal focus of scholarly inquiry. Our study distinguishes itself by separately evaluating traditional machine learning frameworks and advanced deep learning paradigms to determine their respective efficacy in predictive modeling. We dissected the performances of an assortment of models, spanning from conventional ML techniques to sophisticated, hybrid deep learning constructs. Our investigation provides a granular analysis of each model's utility, promoting an informed and strategic integration of ML and AI in Li-ion battery state of health prognostics. Specifically, a utilization of machine learning algorithms such as Random Forests (RFs) and eXtreme Gradient Boosting (XGBoost), alongside regression models like Elastic Net and foundational neural network approaches including Multilayer Perceptron (MLP) were studied. Furthermore, our research investigated the enhancement of time series analysis using intricate models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) and their outcomes with those of hybrid models, including a RNN-long short-term memory (LSTM), CNN-LSTM, CNN-Gated Recurrent Unit (GRU) and RNN-GRU. Comparative evaluations reveal that the RNN-LSTM configuration achieved a Mean Squared Error (MSE) of 0.043, R-Squared of 0.758, Root Mean Square Error (RMSE) of 0.208, and Mean Absolute Error (MAE) of 0.124, whereas the CNN-LSTM framework reported an MSE of 0.039, R-Squared of 0.782, RMSE of 0.197, and MAE of 0.122, underscoring the potential of deep learning-based hybrid models in advancing the accuracy of battery state of health assessments. [ABSTRACT FROM AUTHOR]

A cellulose acetate (CA) membrane as an acetylation product of bacterial cellulose (BC) has been successfully fabricated by electrospinning (e-spin). CA was utilized to achieve the optimal polymer electrolytes' (SPEs) requirements of high ionic conductivity and good thermal and electrochemical stability. E-spin has received a great deal of attention in order to enhance the surface of membranes that is both flexible and strong, which will be effective in obtaining a low interface resistance for a better contact polymer electrolyte/electrode. The e-spin of CA SPEs forms a three-dimensional network of interconnected porosity. Polar ester groups in CA e-spin not only present the homogeny porosity of the SPEs but also played an important role in facilitating the dissociation of lithium bis(trifluoromethanesulfonyl)imide (LiTFSI). According to XPS, peaks of TFSI binding energy CA that are higher than BC are thought to represent the interaction between the polymer chain and the TFSI cation. TFSI− was found to have accumulated around C=O, and Li+ could diffuse quickly. This is supported by the fact that the ionic conductivity (2.68 × 10−3 Scm−1) of CA is better than BC (8.86 × 10−4 Scm−1). The change in dielectric permittivity (ε) as a function of frequency in CA SPEs shows more decay at higher frequencies, indicating better electrode polarization due to CA SPE/electrode contact than BC. The analysis of the modulus (M) shows that CA SPEs in this study are more ionic conductors than BC. CA SPEs also outperformed BC in terms of stability window performance (4.5 V). The discharge capacities of 40 mA h−1 for CA SPEs exhibit an oxidation peak at 4.25 V and a reduction peak at 2.9 V, which are comparatively higher than the charge–discharge of 20 mA h−1 for BC SPEs, which display an oxidation peak at 3.5 V and a reduction peak at 2.8 V. [ABSTRACT FROM AUTHOR]

The significant role of Li-ion batteries (LIBs) in electric vehicles (EVs) emphasizes their advantages in terms of energy density, being lightweight, and being environmentally sustainable. Despite their obstacles, such as costs, safety concerns, and recycling challenges, LIBs are crucial in terms of the popularity of EVs. The accurate prediction and management of LIBs in EVs are essential, and machine learning-based methods have been explored in order to estimate parameters such as the state of charge (SoC), the state of health (SoH), and the state of power (SoP). Various machine learning techniques, including support vector machines, decision trees, and deep learning, have been employed for predicting LIB states. This study proposes a methodology for comparative analysis, focusing on classical and deep learning approaches, and discusses enhancements to the LSTM (long short-term memory) and Bi-LSTM (bidirectional long short-term memory) methods. Evaluation metrics such as MSE, MAE, RMSE, and R-squared are applied to assess the proposed methods' performances. The study aims to contribute to technological advancements in the electric vehicle industry by predicting the performance of LIBs. The structure of the rest of the study is outlined, covering materials and methods, LIB data preparation, analysis, the proposal of machine learning models, evaluations, and concluding remarks, with recommendations for future studies. [ABSTRACT FROM AUTHOR]

In this study, the performances of a pouch Li-ion battery (LIB) with respect to temperature, pressure and discharge-rate variation are measured. A sensitivity study has been conducted with three temperatures (5 °C, 25 °C, 45 °C), four pressures (0.2 MPa, 0.5 MPa, 0.8 MPa, 1.2 MPa) and three electrical discharge rates (0.5 C, 1.5 C, 3.0 C). Electrochemical processes and overall efficiency are significantly affected by temperature and pressure, influencing capacity and charge–discharge rates. In previous studies, temperature and pressure were not controlled simultaneously due to technological limitations. A novel test bench was developed to investigate these influences by controlling the surface temperature and mechanical pressure on a pouch LIB during electrical charging and discharging. This test rig permits an accurate assessment of mechanical, thermal and electrical parameters, while decoupling thermal and mechanical influences during electrical operation. The results of the study confirm what has been found in the literature: an increase in pressure leads to a decrease in performance, while an increase in temperature leads to an increase in performance. However, the extent to which the pressure impacts performance is determined by the temperature and the applied electrical discharge rate. At 5 °C and 0.5 C, an increase in pressure from 0.2 MPa to 1.2 MPa results in a 5.84% decrease in discharged capacity. At 45 °C the discharge capacity decreases by 2.17%. Regarding the impact of the temperature, at discharge rate of 0.5 C, with an applied pressure of 0.2 MPa, an increase in temperature from 25 °C to 45 °C results in an increase of 4.27% in discharged capacity. The impact on performance varies significantly at different C-rates. Under the same pressure (0.2 MPa) and temperature variation (from 25 °C to 45 °C), increasing the electrical discharge rate to 1.5 C results in a 43.04% increase in discharged capacity. The interplay between temperature, pressure and C-rate has a significant, non-linear impact on performance. This suggests that the characterisation of an LIB would require the active control of both temperature and pressure during electrical operation. [ABSTRACT FROM AUTHOR]

Over the past decade, Unmanned Aerial Vehicles (UAVs) have begun to be increasingly used due to their untapped potential. Li-ion batteries are the most used to power electrically operated UAVs for their advantages, such as high energy density and the high number of operating cycles. Therefore, it is necessary to estimate the Remaining Useful Life (RUL) and the prediction of the Li-ion batteries' capacity to prevent the UAVs' loss of autonomy, which can cause accidents or material losses. In this paper, the authors propose a method of prediction of the RUL for Li-ion batteries using a data-driven approach. To maximize the performance of the process, the performance of three machine learning models, Support Vector Machine for Regression (SVMR), Multiple Linear Regression (MLR), and Random Forest (RF), were compared to estimate the RUL of Li-ion batteries. The method can be implemented within UAVs' Predictive Maintenance (PdM) systems. [ABSTRACT FROM AUTHOR]

Li‐molecule pair is a widely used model for the simulation of reduction in Li‐ion batteries. We demonstrate that this model provides incorrect results for some solvents. When an electron is added to the Li‐molecule pair, it may go to the lithium‐ion and neutralize it. Instead, we suggest placing this additional electron on the molecule using constrained density functional theory (CDFT). This approach resembles electron behaviour in the condensed phase and reproduces the physics of the reduction. We demonstrate that suggested in this work approach provides improved agreement with experimental data. Suggested CDFT‐based method is fast, reliable and may be used in computational screening of solvents. We demonstrate the practical application of the method by benchmarking it on a set of 30 molecules from the electrolyte solvent database. [ABSTRACT FROM AUTHOR]

With the developing technologies in the aviation, the transition to more electrical systems is increasing day by day. For this reason, research on the development of batteries has accelerated. Nowadays, Lithium ion (Li-ion) batteries are more widely preferred due to their energy-to-weight ratio and advantages such as having a lower self-discharge rate when not working compared to other battery technologies. Batteries convert the stored chemical energy into electrical energy and heat is released as a result of the chemical reactions. The heat released negatively affects the battery's lifespan, charging/discharging time and battery output voltage. The battery must be modeled correctly to see these negative effects and intervene in time. In this way, negative situations that may occur in the battery can be intervened at the right time without any incident. In this study, the unmanned aerial vehicle (UAV) is powered by Li-ion batteries. It is simulated in Matlab/Simulink environment using the electrical equivalent circuit. A detailed model is created, taking into account temperature, state of charge (SoC), cell dynamics and operating functions. To estimate state of health (SoH) of the battery, resistance values must be known. Resistance and capacity values in the equivalent circuit of the Li-ion battery are obtained with the help of the simulation model. So, the SoH of the Li-ion batteries can be accurately predicted with the results obtained. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries have revolutionized the portable and stationary energy industry and are finding widespread application in sectors such as automotive, consumer electronics, renewable energy, and many others. However, their efficiency and longevity are closely tied to accurately measuring their SOC and state of health (SOH). The need for precise algorithms to estimate SOC and SOH has become increasingly critical in light of the widespread adoption of lithium-ion batteries in industrial and automotive applications. While the benefits of lithium-ion batteries are undeniable, the challenges related to their efficient and safe management cannot be overlooked. Accurate estimation of SOC and SOH is crucial for ensuring optimal battery management, maximizing battery lifespan, optimizing performance, and preventing sudden failures. Consequently, research and development of reliable algorithms for estimating SOC and SOH have become an area of growing interest for the scientific and industrial community. This review article aims to provide an in-depth analysis of the state-of-the-art in SOC and SOH estimation algorithms for lithium-ion batteries. The most recent and promising theoretical and practical techniques used to address the challenges of accurate SOC and SOH estimation will be examined and evaluated. Additionally, critical evaluation of different approaches will be highlighted: emphasizing the advantages, limitations, and potential areas for improvement. The goal is to provide a clear view of the current landscape and to identify possible future directions for research and development in this crucial field for technological innovation. [ABSTRACT FROM AUTHOR]

The synthesis and electrochemical insertion of lithium into the Wadsley–Roth NaNb13O33 phase is studied. Lithium intercalation to form LixNaNb13O33 reaches a value of up to x~15, between 3.0 and 1.0 V vs. Li+/Li at a slow cycling rate, a capacity of 233 mAh g−1. Within this voltage window, two sharp peaks and one broad peak are observed in the differential capacity plots of lithium intercalation suggesting multiple two‐phase regions. High Li‐ion conductivity and rate capability was demonstrated. The lithium diffusion constant is about an order of magnitude greater than TiNb2O7. The average voltage is about 1.6 V and its high‐rate capability makes NaNb13O33 potentially useful as an anode in a fast‐charge Li‐ion battery application. [ABSTRACT FROM AUTHOR]

State of charge (SOC) estimation is an important part of a battery management system (BMS). As for small portable devices powered by lithium-ion batteries, no current sensor will be configured in BMS, which presents a challenge to traditional current-based SOC estimation algorithms. In this work, an electrochemical model is developed for lithium batteries, and three methods, including the incremental seeking method, dichotomous method, and extended Kalman filter algorithm (EKF), are separately developed to establish the framework of current and SOC estimation simultaneously. The results show that the EKF algorithm performs better than the other two methods in terms of estimation accuracy and convergence speed. In addition, the estimation error of the EKF algorithm is within ±2%, which demonstrates its feasibility. [ABSTRACT FROM AUTHOR]

The Li-ion battery is one of the key components in electric car development due to its performance in terms of energy density, power density and cyclability. However, this technology is likely to present safety problems with the appearance of cell thermal runaway, which can cause a car fire in the case of propagation in the battery pack. Today, standards describing safety compliance tests, which are a prerequisite for marketing Li-ion cells, are carried out on fresh cells only. It is therefore important to carry out research into the impact of cell aging on battery safety behavior in order to ensure security throughout the life of the battery, from manufacturing to recycling. In this article, the impact of Li-ion cell aging on safety is studied. Three commercial 18,650 cells with high-power and high-energy designs were aged using a Battery Electric Vehicle (BEV) aging profile in accordance with the International Electrotechnical Commission standard IEC 62-660. Several thermal (Accelerating Rate Calorimetry—ARC) and standardized safety (short-circuit, overcharge) tests were performed on fresh and aged cells. This study highlights the impact of aging on safety by comparing the safety behavior of fresh and aged cells with their aging conditions and the degradation mechanisms involved. [ABSTRACT FROM AUTHOR]

Energy storage technologies can act as flexibility sources for supporting the energy transition, enabling the decarbonisation of the grid service provision and the active engagement of the customers (both prosumers and consumers), opening for them new business opportunities. Within storage technologies, Lithium-ion (Li-ion) batteries represent an interesting solution for dealing with the majority of these services. In this context, this study addresses an evaluation of economic, environmental and geopolitical risks with reference to the critical raw materials used in the manufacturing of Lithium Iron Phosphate (LFP) Li-ion batteries. The assessment entailes grid and prosumer services that these batteries can provide. The exploited economic indicator is the Levelised Cost of Storage, whereas six environmental indicators are used for environmental impact estimation. Cycle stages accounted for in the analysis are the manufacturing and use phases. Finally, the evaluation of the impact of critical raw materials is performed by deploying a Supply Risk indicator, which is instead assessed considering every single material and the overall risk for the battery. High-risk materials are represented by Graphite and Phosphorous. Results denote that, for each service, the number of cycles and the discharge duration are pivotal to make the investment economically and environmentally sustainable. The reduction in the Net Import Reliance, as well as the increase in the Recycling Rate, could sensibly reduce the risk associated with battery raw materials. [ABSTRACT FROM AUTHOR]

Lithium Nickel Manganese Cobalt Oxides (NMC) are one of the most used cathode materials in lithium-ion batteries, and they will become more relevant in the following years due to their potential in electric vehicles. Unfortunately, this material experiences microcracking during the battery operation due to the volume variations, which is detrimental to the battery performance and limits the lifetime of the electrodes. Thus, understanding mechanical degradation is fundamental for the development of advanced batteries with improved capacity and limited degradation. In this work, we propose a chemo-mechanical model, including a stochastic phase-field fracture approach, to design structurally stable NMC electrodes. We include the degradation in the mechanical and chemical contributions. The heterogeneous NMC microstructure is considered by representing the material's tensile strength with a Weibull distribution function, which allows to represent complex and non-deterministic crack patterns. We use our model to provide a comprehensive analysis of mechanical degradation in NMC111 electrodes, including the effect of particle size, C-rate, and depth of charge and discharge. Then, we analyze the influence of the electrode composition (namely, Ni content) on the structural integrity. We use this information to provide design guides for functionally-graded electrodes with high capacity and limited degradation.   • Bigger particles, and higher C-rates and depth of delithiation promote material cracking. • Ni-richer NMC electrodes withstand larger C-rates. • Functionally-graded with composition gradient designs reduce mechanical degradation. [ABSTRACT FROM AUTHOR]

Lithium-rich layered oxides (LRLO) are a wide class of innovative active materials used in positive electrodes in lithium-ion (LIB) and lithium–metal secondary batteries (LMB). LRLOs are over-stoichiometric layered oxides rich in lithium and manganese with a general formula Li1+xTM1−xO2, where TM is a blend of transition metals comprising Mn (main constituent), Ni, Co, Fe and others. Due to their very variable composition and extended defectivity, their structural identity is still debated among researchers, being likely an unresolved hybrid between a monoclinic (mC24) and a hexagonal lattice (hR12). Once casted in composite positive electrode films and assembled in LIBs or LMBs, LRLOs can deliver reversible specific capacities above 220–240 mAhg−1, and thus they exceed any other available intercalation cathode material for LIBs, with mean working potential above 3.3–3.4 V vs Li for hundreds of cycles in liquid aprotic commercial electrodes. In this review, we critically outline the recent advancements in the fundamental understanding of the physical–chemical properties of LRLO as well as the most exciting innovations in their battery performance. We focus in particular on the elusive structural identity of these phases, on the complexity of the reaction mechanism in batteries, as well as on practical strategies to minimize or remove cobalt from the lattice while preserving its outstanding performance upon cycling. [ABSTRACT FROM AUTHOR]

Typically, in accelerated life testing analysis, only probability distributions possessing shape parameters are used to fit the experimental data, and many distributions with no shape parameters have been excluded, including the fundamental ones like the normal distribution, even when they are good fitters to the data. This work shows that the coefficient of variation is a replacement for the shape parameter and allows using normal distributions in this context. The work focuses on the Arrhenius-normal model as a life-stress relationship for lithium-ion (Li-ion) batteries and precisely derives the estimating equations of its accelerating parameters. Real and simulated lives of Li-ion batteries are used to validate our results. [ABSTRACT FROM AUTHOR]

Apart from being emission-free, electric vehicles enjoy benefits such as low maintenance and operating costs, noise-free, easy to drive, and the convenience of charging at home. All these benefits are directly dependent on the performance of the battery used in the vehicle. In this paper, one-dimensional modeling of Li-ion and NiMH batteries was developed, and their performances were studied. The performance characteristics of the batteries, such as the charging and discharging characteristics, the constituent losses of over-potential voltage, and the electrolyte concentration profile at various stages of charge and discharge cycles, were also studied. It is found that the electrolyte concentration profiles of Li-ion batteries show a drooping behavior at the start of the discharge cycle and a rising behavior at the end of discharge because of the concentration polarization due to the low diffusion coefficient. The electrolyte concentration profiles of NiMH batteries show rising behavior throughout the discharge cycle without any deviations. The reason behind this even behavior throughout the discharge cycle is attributed to the reduced concentration polarization due to electrolyte transport limitations. It is found that the losses associated with the NiMH battery are larger and almost constant throughout the battery's operation. Whereas for the Li-ion batteries, the losses are less variable. The electrolyte concentration directly affects the overpotential losses incurred during the charging and discharging phases. [ABSTRACT FROM AUTHOR]

The prediction of thermal behavior is essential for an efficient initial design of thermal management systems which equip energy sources based on electrochemical cells. In this study, the surface temperature of various cylindrical types of Li-ion batteries is monitored at multiple points during discharge. Three different battery chemistries and two sizes (18650 and 21700) are considered in this study, allowing the comparison of the influence these parameters have on the temperature rise considering different discharge rates (1C, 2C and 3C). Based on repeated experimental measurements, a simple equation that describes the thermal behavior of batteries is proposed and further used to create 3D thermal maps for each analyzed battery (generally error is below 1 °C but never exceeds 3 °C). The practical utility of such an equation is that it can drastically reduce the time spent with experimental measurements required to characterize the thermal behavior of cylindrical Li-ion batteries, necessary for the initial design process of energy sources' thermal management system. [ABSTRACT FROM AUTHOR]

Due to the long life of lithium ion cells, it is difficult to measure their low capacity degradation from cycle to cycle. In order to accelerate the measurements, cells are often exposed to extreme stress conditions, which usually means elevated temperatures and high charging currents. This raises doubts as to whether the results obtained in this way are representative for real world applications. This work shows that, with the help of very precise capacity measurements, it is possible to determine cell aging in a few days even under normal operating conditions from cycle to cycle. To verify this, a self-built measurement system is used. After demonstrating the capabilities of the system, two different cycling schemes are used simultaneously to determine the various causes of aging—namely cycle aging, calendrical aging and self-discharge due to leakage currents. [ABSTRACT FROM AUTHOR]

Laser-induced breakdown spectroscopy (LIBS) is a valuable tool for the solid-state elemental analysis of battery materials. Key advantages include a high sensitivity for light elements (lithium included), complex emission patterns unique to individual elements through the full periodic table, and record speed analysis reaching 1300 full spectra per second (1.3 kHz acquisition rate). This study investigates deep learning methods as an alternative tool to accurately recognize different compositions of similar battery materials regardless of their physical properties or manufacturer. Such applications are of interest for the real-time digitalization of battery components and identification in automated manufacturing and recycling plant designs. [ABSTRACT FROM AUTHOR]

The paper is focused on the analysis of changes in the internal impedance of the selected battery cell. In addition, the paper describes basic information about these batteries, their advantages/disadvantages and possible uses. Battery data was obtained using our own testing device, which performed long-term cyclical tests on batteries to decrease SOH. Thanks to these tests, we obtained batteries with different SOH used for further measurement and evaluation. The internal impedance of selected batteries was measured with an accurate measuring device. Based on these measured data, the analysis described in this paper was created. [ABSTRACT FROM AUTHOR]

Batteries made of charge carriers from Earth‐crust abundant materials (e.g., Na, K, and Mg) have received extensive attention as an alternative to Li‐ion batteries for grid storage. However, a lack of understanding of the behavior of these larger ions in the electrode materials hinders the development of electrode structures suitable for these large ions. In this study, we investigate the impact of alkali ions (Li, Na, and K) on the redox chemistry and mechanical deformations of iron phosphate composite cathodes by using electrochemical techniques and in situ digital image correlation. Na‐ion and Li‐ion intercalation demonstrate a nearly linear correlation between electrochemical strains and the state of charge and discharge. The strain development shows nonlinear dependance on the state of charge and discharge for K ions. Strain rate calculations show that K ion intercalation results in a progressive increase in the strain rate for all cycles. Li and Na intercalation induce nearly constant strain rates with the exception of the first discharge cycle of Na intercalation. When the same amount of ions are inserted into the electrode, the electrode shows the lowest strain generation upon Li intercalation compared to larger alkali ions. Na and K ions induce similar volumetric changes in the electrode when the state of charge and discharge is around 30%. Although the electrode experiences larger absolute strain generation at the end of the discharge cycles upon Na intercalation, strain rates were found to be greater for K ions. Potential‐dependent behaviors also demonstrate more sluggish redox reactions during K intercalation, compared to Li and Na. Our quantitative analysis suggests that the strain rate, rather than the absolute value of strain, is the critical factor in amorphization of the crystalline electrode. [ABSTRACT FROM AUTHOR]

Battery technology has been used for satellites since the first satellite sputnik-1 was launched in 1957. The majority of larger satellite’s (geostationary or communication satellites) lives range from 7 to 15 years. During the lifetime of satellites, the batteries used must complete 1000 to 33000 cycles without any problems or likelihood of maintenance. There are three battery technologies, Li-ion, Ni-H2 and Ni-Cd, that are well proven for Geostationary satellite applications. Energy density, lifetime, weight, ampere-hour capacity, depth of discharge, ruggedness and recharge-ability, battery management, thermal management, and self-discharge are main parameters that should be considered when comparing electrical and thermal performance of these three battery technologies. The purpose of this study is to compare the thermal control system for these three batteries for three-axis stabilized geostationary satellites. In particular, the thermal dissipation was compared, which is the temperature range required for battery operation. Thermal analysis was performed for Li-ion batteries using ThermXL software, and showed a temperature results variation ranging between 10.9°C and 32.7°C. The temperature during the battery module was not greater that its qualification temperature results. [ABSTRACT FROM AUTHOR]

Different battery thermal management (BTM) for a 18650 cylindrical Li-ion battery pack were simulated based on the lumped model. Phase change material (PCM) was introduced for its benefits: high latent heat and uniform melting temperature. However, the heat absorbed into the PCM was not dissipated to the environment. Thus, an additional cooling mechanism was required to reach satisfying temperature limits (45 °C for lifespan and 50 °C for safety). These include natural convection cooling, forced convection cooling, finned-natural convection cooling, PCM cooling, and PCM cooling coupled with liquid cooling channels. The temperature of the battery pack using natural convection, finned-natural convection, and PCM cooling reached 62.1 °C, 55 °C, and 50 °C, which were equal or over (50 °C), while it decreased to 49 °C using forced convection, which was over (45 °C). However, PCM coupled with liquid cooling channels satisfied the requirements, and the temperature was reduced to 42 °C using this system. [ABSTRACT FROM AUTHOR]

This article surveys the mathematical principles essential for understanding the thermal management of Li-ion batteries, the current technological state of the art, and the solution. Since the thermal management of electric drive vehicles has environmental, economic, and safety impacts, this review focuses on the efficient methods of battery thermal management (BTM) that were proposed to overcome the major challenges in the electric vehicle industry. The first section examines the perspective of battery-driven vehicles, the principles of Li-ion batteries with a thermal runaway, and their implication for battery safety. The second section discusses mathematical approaches for effective BTM modeling, including the thermal-fluidic network model, lumped capacitance model, spatial resolution lumped capacitance model, equivalent circuit model, impedance-based model, and data-driven model. The third section presents the current state-of-the-art technologies, including air-based, liquid-based, PCM-based, in situ BTM methods, and heat pipe and thermoelectric module-based methods. The conclusion section summarizes the findings from existing research and the possible future directions to achieve and employ better thermal management techniques. [ABSTRACT FROM AUTHOR]

Electrical models of battery cells are used in simulations to represent batteries' behavior in various fields of research and development involving battery cells and systems. Electrical equivalent circuit models, either linear or nonlinear, are commonly used for this purpose and are presented in this article. Various commercially available cylindrical, state-of-the-art lithium-ion battery cells, both protected and unprotected, are considered. Their impedance properties, according to four different equivalent circuit models, are measured using electrochemical impedance spectroscopies. Furthermore, the pricing, impedance, specific energy, and C-rate of the chosen battery cells are compared. For example, it is shown that the energy density of modern 18650 cells can vary from a typical value of 200 to about 260 Wh kg−1, whereas the cell price can deviate by a factor of about 3 to 5. Therefore, as a result, this study presents a concise but comprehensive battery parameter library that should aid battery system designers or power electronic engineers in conducting battery simulations and in selecting appropriate battery cells based on application-specific requirements. In addition, the accuracies and computational efforts of the four equivalent circuit models are compared. [ABSTRACT FROM AUTHOR]

In anticipation of the hybrid utilisation of the radio frequency (RF) wireless transceiver technology embedded in future smart Li-ion battery cells to deliver hybrid links based on power line communication (PLC) and wireless connections, herein we present an empirical high-frequency investigation of the direct current (DC) bus. The focus is to determine, via statistical tools including correlation coefficient (CC), root mean squared error (RMSE) and feature selective validation (FSV) method, the impedance and signal change impact on a possible communication link when fully charged cells are present or completely missing from the bus. Moreover, to establish if technological differences may be accounted for during the empirical experiments, Li-ion cells from two different manufacturers were selected and connected via three subsequent capacitive couplings of 1 µF, 1 nF and 1 pF. According to a methodical comparison by employing CC, RMSE, and FSV over the measured impedance and signal attenuation, this study has shown that the physical DC network is the dominant impedance at high frequencies and that the signal attenuation on the DC line supports communication in the investigated spectrum. The reported findings are critical for in situ hybrid PLC and wireless communication implementation of BMS for Li-ion systems supported through only one RF transceiver. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An experimental study is performed to investigate the performance (thermally and electrically) of a small-scale li-ion module (3s2p) using passive thermal management strategy of phase change material (PCM)/graphite matrix. The PCM/graphite matrix was obtained by impregnating the graphite matrix (bulk density: 75 g L-1) with phase change material (paraffin/organic, RT-35). The performance tests of a li-ion module are conducted at 1C and 1.6C discharge rates for graphite matrix composite with phase change (phase change composite or PCM/graphite matrix) and also air cooling, comparatively. To illustrate the performance of the PCM/graphite matrix, transient temperature variations, thermal imaging, discharge capacity, and energy capacity are achieved comprehensively. The results illustrate that graphite matrix composite with phase change has a significant contribution to melting heat transfer, operating temperature, utilized capacity, and energy capacity compared to air cooling. Effective thermal conductivity of PCM/graphite matrix is increased 35 times by comparison with pure paraffin. Operating temperature and temperature gradient throughout the li-ion surface decrease by 22 % and 43 % compared to the air cooling, respectively, for high discharge rate. Operating time and energy capacity is increased 33 % and 28% compared to natural air cooling, respectively, for high discharge rate. It is also disclosed that dominant heat transfer mechanism is conduction depending on micro/nano-size porous structure of graphite matrix. [ABSTRACT FROM AUTHOR]

In this paper, we explore how effectively renewable generation can be used to meet a country's electricity demands. We consider a range of different generation mixes and capacities, as well as the use of energy storage. First, we introduce a new open-source model that uses hourly wind speed and solar irradiance data to estimate the output of a renewable electricity generator at a specific location. Then, we construct a case study of the Great Britain (GB) electricity system as an example using historic hourly demand and weather data. Three specific sources of renewable generation are considered: offshore wind, onshore wind, and solar PV. Li-ion batteries are considered as the form of electricity storage. We demonstrate that the ability of a renewables-based electricity system to meet expected demand profiles can be increased by optimising the ratio of onshore wind, offshore wind and solar PV. Additionally, we show how including Li-ion battery storage can reduce overall generation needs, therefore lowering system costs. For the GB system, we explore how the residual load that would need to be met with other forms of flexibility, such as dispatchable generation sources or demand-side response, varies for different ratios of renewable generation and storage. [ABSTRACT FROM AUTHOR]

The Li-ion batteries used in electric vehicles widely employ cooling plates with mini channels for thermal management purpose. Researches on battery thermal management systems of electric vehicles are largely focused on improving the cooling efficiency hence obtaining temperature uniformity across the cells. In the present work, a streamline configuration has been incorporated in cooling plate design to enhance the performance of mini-channel cooling plate. These kinds of configurations are usually found in submarine and aerospace applications. By adopt- ing streamline design the flow resistance is found to reduce. Five plates with different number of channels (3, 4, 5, 6, 7) were computationally studied at a coolant (water and ethylene glycol mixture) flow rate of 0.002 kg/s. The maximum cell temperature is decreased by 7.8% for 7-channel configuration. Temperature uniformity is also found to improve using this design. The results reveal that this design is able to maintain the temperature of Li-ion battery in safe operating range. Hence this could be a better solution for thermal management of Li-ion batteries in future. [ABSTRACT FROM AUTHOR]

Copyright of SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi is the property of Journal of Engineering Sciences & Design and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The most relevant results from the IDR/UPM Institute research on solar cells/panels and Li-ion batteries performance are reviewed in this article. The aim of this article is to present the possibilities of the mathematical procedures developed for space applications, with a view to extend their use to other industrial sectors. The research at the IDR/UPM Institute has been driven by selecting simple tools and procedures to model the behavior of solar cell/panel, since modeling these photovoltaic devices is normally carried out by 1-diode/2-resistor equivalent circuit models, which might represent an unaffordable mathematical challenge for many professionals and technicians in the renewable energy field. Concerning Li-ion batteries, the large experience accumulated during the maintenance of the UPMSat-2 battery resulted in the successful development of new mathematical models to study the performance of batteries. The models developed are based on the amount of discharged energy, measured in W·h, instead of other more traditional parameters such as the state of charge, measured in A·h. [ABSTRACT FROM AUTHOR]

Deep learning has gained much traction in application to state-of-charge (SOC) estimation for Li-ion batteries in electric vehicle applications. However, with the vast selection of architectures and hyperparameter combinations, it remains challenging to design an accurate and robust SOC estimation model with a sufficiently low computation cost. Therefore, this study provides a comparative evaluation among commonly used deep learning models from the recurrent, convolutional, and feedforward architecture benchmarked on an openly available Li-ion battery dataset. To evaluate model robustness and generalization capability, we train and test models on different drive cycles at various temperatures and compute the root mean squared error (RMSE) and mean absolute error metric. To evaluate model computation costs, we run models in real-time and record the model size, floating-point operations per second (FLOPS), and run-time duration per datapoint. This study proposes a two-hidden layer stacked gated recurrent unit model trained with a one-cycle policy learning rate scheduler. The proposed model achieves a minimum RMSE of 0.52% on the train dataset and 0.65% on the test dataset while maintaining a relatively low computation cost. Executing the proposed model in real-time takes up approximately 1 MB in disk space, 300K FLOPS, and 0.03 ms run-time per datapoint. This makes the proposed model feasible to be executed on lightweight battery management system processors. [ABSTRACT FROM AUTHOR]

Copyright of Safety & Fire Technology (2657-8808) is the property of Centrum Naukowo-Badawcze Ochrony Przeciwpozarowej and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This review provides a personal account of career‐long efforts, interwoven with the contributions of others, to exploit the [B2]O4 framework of an A[B2]O4 spinel structure for lithium‐ion battery applications, where A = Li, and B is one or more metal cations. The narrative starts at the Council for Scientific and Industrial Research (CSIR) in South Africa in the mid‐1970s when, in response to the Middle East oil crisis, worldwide efforts were turned to develop high‐temperature sodium and lithium batteries to power electric vehicles. In 1981, results in CSIR's program on a high‐temperature lithium battery led to a collaborative project on room‐temperature lithium cells with Prof. John B. Goodenough at Oxford University, where studies of Fe3O4, Mn3O4, and LiMn2O4 cathodes led to the recognition of the broad utility of the [B2]O4 spinel framework as an anode, cathode, or solid electrolyte for rechargeable lithium cells. This finding has had a profound and lasting impact, influencing further ideas and discoveries at the CSIR, Argonne National Laboratory, and elsewhere. The narrative emphasizes the compositional versatility of the spinel structure and the opportunities to tailor the electrochemical potential and stability of a Li‐ion cell. [ABSTRACT FROM AUTHOR]

The battery system is one of the key components of electric vehicles (EV) which has brought groundbreaking technologies. Since modern EVs have mostly Li-ion batteries, they need to be monitored and controlled to achieve safe and high-performance operation. Particularly, the battery management system (BMS) uses complex processing systems that perform measurements, estimation of the battery states, and protection of the system. State of charge (SOC) estimation is a major part of these processes which defines remaining capacity in the battery until the next charging operation as a proportion to the total battery capacity. Since SOC is not a parameter that can be measured, the fundamental challenge is an accurate estimation. There are different SOC estimation methods in the literature that promises high accuracy such as model-based estimations, adaptive filter based estimations, and a combination of these systems. Recently, artificial intelligence (AI) and particularly machine learning (ML) based systems are included in the battery state estimation both as a part of adaptive systems and standalone. Data-driven methods are promising approaches to battery state estimation which provide high accuracy. The purpose of this study is to present a novel and highly accurate way of SOC estimation of the Li-ion battery (LIB) cell with a considerably low parameterization and modeling effort. Therefore, a new approach is proposed to estimate SOC with reduced modeling and without performing parametrization. Based on discharge test data, XGBoost is used to estimate SOC under dynamic operating conditions and the estimation is reached 98.81% coefficient of determination. As a novel approach, exponential smoothing is performed in combination with XGBoost SOC estimation to improve the estimation performance of the model. The estimation accuracy is improved as approximately 0.62%. [ABSTRACT FROM AUTHOR]

One of the main problems of LiNi0.80Co0.15Al0.05O2 (NCA) materials is the side reactions occurring during battery cycling. These side reactions result in capacity fading, which in turn decreases the life span of the battery. Conventionally, for nickel‐rich cathodes, an inorganic layer is used as a protective layer. Applying these layers may require additional complicated steps. To overcome this problem, we focused on coating the NCA electrode with a polymeric ion conductive layer that can easily be applied. For this purpose, using an ion‐conductive polymer is a novel approach. We used Nafion as the protective layer on NCA electrodes. While the Nafion layer can protect the surface against the side reactions, it does not prevent the Li+ ion flow. Following this purpose, we synthesized NCA by a modified co‐precipitation method followed by sintering at 750°C. After the preparation of the NCA cathodes, we coated the surfaces with a more straightforward drip coating. The Nafion‐coated electrodes delivered a superior electrochemical performance with 100% of discharge capacity retention even after 100 cycles at 0.1C. The electrochemical impedance spectroscopy revealed that the Nafion coating could effectively prevent passive layer formation, which causes capacity fading. [ABSTRACT FROM AUTHOR]

Satellites have a tangible impact on our daily lives; they provide us with many services like communication, global positioning, etc. Satellite batteries are expected to deliver the power demand at any time during the period of an eclipse, or when the power received from the onboard solar panels is not sufficient. The focus of this research is to develop an energy management mathematical model that reduces the state of health degradation of a battery in a low earth orbit (LEO) satellite. This improves the battery lifetime; thus, increasing the length of time a LEO satellite can stay in service. The developed model for a LEO satellite is solved separately for meeting three different objectives. In addition to the model, a heuristic approach is developed, and the results are compared to those obtained from the above-mentioned model. In this endeavor, data are collected for an existing LEO satellite, Nayif-1, in order to analyze the current battery behavior in space and to compare it with the developed model and heuristics. Sensitivity analysis is conducted to observe the effects of altering different parameters of the model. The results presented in this research show that minimizing the sum of products of the battery state switches and the battery current yields the best results by enhancing the lifetime of the battery by 8 days and providing 122 more cycles than that observed in the data from Nayif-1, assuming that the DOD of the battery remains constant throughout all orbits. [ABSTRACT FROM AUTHOR]

This article describes a symmetric modified multilevel ladder (SMML) converter that is capable of directly converting down from Li-ion battery voltage ranges to system-on-chip (SoC)-compatible voltage ranges while using low-voltage power MOSFETs in the power stage. Compared to conventional flying-capacitor multilevel converters, the SMML converter features reduced conduction losses, naturally balanced flying capacitors, and internal generation of all the supplies required for drivers and level shifters. A prototype shows that the converter achieves a peak efficiency of 90% and a peak power density of 0.52 W/mm2 while providing up to 16.7 $\times $ conversion ratios at still acceptable efficiencies. [ABSTRACT FROM AUTHOR]

Information derived from microscopic images of Li-ion cells is the base for research on the function, the safety, and the degradation of Li-ion batteries. This research was carried out to acquire information required to understand the mechanical properties of Li-ion cells. Parameters such as layer thicknesses, material compositions, and surface properties play important roles in the analysis and the further development of Li-ion batteries. In this work, relevant parameters were derived using microscopic imaging and analysis techniques. The quality and the usability of the measured data, however, are tightly connected to the sample generation, the preparation methods used, and the measurement device selected. Differences in specimen post-processing methods and measurement setups contribute to variability in the measured results. In this paper, the complete sample preparation procedure and analytical methodology are described, variations in the measured dataset are highlighted, and the study findings are discussed in detail. The presented results were obtained from an analysis conducted on a state-of-the-art Li-ion pouch cell applied in an electric vehicle that is currently commercially available. [ABSTRACT FROM AUTHOR]

Rechargeable lithium‐ion batteries with good electrochemical properties require nanostructured electrode materials, which are usually prepared through complex synthesis processes. Herein, a new facile method is reported for the synthesis of high‐performance electrode materials with a crater‐like morphology through repulsion between positive charges. The produced electrode material does not possess a nanostructure. However, it is capable of rapidly transferring lithium ions and electrons owing to the large contact area with electrolyte and the high concentration sp2‐hybridized carbon coating. LiFePO4 and LiNi1/3Co1/3Mn1/3O2 electrodes prepared by this process achieved high discharge capacities of 165.7 and 199.9 mAh g−1 at 0.1 C, with excellent rate capability of 127.5 and 162.6 mAh g−1 at 10 C, respectively. Although the crater‐type materials might decrease the electrode tap density, they facilitate better electrochemical properties such as high capacity, high power, and fast charging. Furthermore, this new method can be applied trough a sol–gel process for the synthesis of electrode materials to improve their electrochemical characteristics. [ABSTRACT FROM AUTHOR]

Energy policy of EU promotes electro-mobility, especially the use of bicycles and electric bicycles. PEDELEC is a form of an electric bicycle that does not require a registry plate, and with a battery power 250 W electric motor helps the driver to overcome the altitude differences and influence of the wind. Batteries for electric bicycles are based on Li-Ion technology. Battery packs are made of standard cells that have a lifecycle of 1000 cycles. In realistic exploration conditions, the lifecycle is four years and up to a third of the declared cycle of use. The reason for this is the climatic conditions which lead to increased temperatures in usage and charging temperatures, which significantly reduce their life cycle. Paper on a practical example shows the dynamics of PEDELEC usage, energy needs for specific route characteristics, as well as changes in the thermal pattern of the battery and its condition after 4 years of use. The analysis was performed with Non-Destructive Testing (NDT) using infrared thermography, which confirmed the inhomogeneity of thermal patterns and the moment of final battery failure. [ABSTRACT FROM AUTHOR]