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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]

The influence of the composition, the thickness and the surface morphology of Li3V2(PO4)3 or Li4Ti5O12 based electrode composites with carbon nanomaterial and polyvinylidene fluoride on their electrochemical performance was examined. The thickness and the surface morphology of the electrodes were jointly controlled by rolling with different gaps and monitored using 3D laser microscopy and scanning electron microscopy. Increasing carbon nanomaterial content, the increase in the specific capacity of the electrode due to the non-Faradic component was observed up to the values of the specific capacity seemingly exceeding the theoretical capabilities of Li3V2(PO4)3 or Li4Ti5O12. When rolling the electrode with decreasing gap, we observed that Li3V2(PO4)3-based electrode composites improved their performance in terms of initial specific capacity and resistance to high current loads. As for Li4Ti5O12-based composites we observed the extremum. We concluded that not only the contact of Li4Ti5O12 or Li3V2(PO4)3 with electrolyte, but the three-phase contact of Li4Ti5O12 or Li3V2(PO4)3 with carbon nanomaterial particles and electrolyte as well was important for the electrochemical activity of electrode composites.

The increasing need for effective electric vehicle (EV) charging solutions in the context of transportation electrification has become a significant challenge. This system introduces an innovative algorithm, named Energy Distribution and Node Allocation using Evolutionary and Resourceful Optimization (ENDEAVOR), designed to elevate the efficiency of EV charging through the integration of a battery's digital twin. This cutting-edge algorithm offers precise estimations of EV charging time, seamlessly updating both the State of Charge (SOC) via the Unscented Kalman Filter (UKF) and the internal battery resistance using parameterization, while sending this information to the cloud. ENDEAVOR optimizes charging-node allocation and intelligently distributes energy among incoming EVs based on their specific charging requests, all within the context of renewable-energy-sourced charging stations. The incorporation of a digital twin for the battery confers several benefits, including highly accurate SOC and charging-time estimates that ultimately enhance the overall efficiency of the charging process. This algorithm further optimizes energy distribution, resulting in significantly improved charging-time predictions, reduced wait times for users, and an enhanced overall experience for the user. The day-to-day implications of these enhancements are remarkable, culminating in substantial annual energy savings of approximately 180 units. ENDEAVOR has the potential to revolutionize the landscape of EV charging. [ABSTRACT FROM AUTHOR]

As more renewable energy sources are integrated into the United Kingdom's power grid, flexibility services are becoming integral to ensuring energy security. This has encouraged the proliferation of Lithium-ion battery storage systems, with 85 GW in development. However, battery degradation impacts both system lifespan and the economic viability of large-scale projects. With rising commodity costs and supply chain issues, maximising the value of energy storage is critical. Traditional methods of mitigating battery ageing rely on static limits based on inflexible warranties, which do not fully account for the complexity of battery degradation. This study examined an alternative, degradation-aware current derating strategy to improve system performance. Using an optimisation model simulating UK energy trading, combined with an electro-thermal and semi-empirical battery model, we assessed the impact of this approach. Interviews with industry leaders validated the modelled parameters and the relevance of the alternative strategy. Results show the degradation-aware strategy can extend battery lifetime by 5–8 years and improve net present value and internal rate of return over a 15-year period compared with traditional methods. These findings highlight the economic benefits of flexible, degradation-aware operational strategies and suggest that more adaptive warranties could accelerate renewable energy integration and lower costs for storage operators. [ABSTRACT FROM AUTHOR]

This study considers the significance of drones in various civilian applications, emphasizing battery-operated drones and their advantages and limitations, and highlights the importance of energy consumption, battery capacity, and the state of health of batteries in ensuring efficient drone operation and endurance. It also describes a robust testing methodology used to determine battery SoH accurately, considering discharge rates and using machine learning algorithms for analysis. Machine learning techniques, including classical regression models and Ensemble Learning methods, were developed and calibrated using experimental UAV data to predict SoH accurately. Evaluation metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) assess model performance, highlighting the balance between model complexity and generalization. The results demonstrated improved SoH predictions with machine learning models, though complexities may lead to overfitting challenges. The transition from simpler regression models to intricate Ensemble Learning methods is meticulously described, including an assessment of each model's strengths and limitations. Among the Ensemble Learning methods, Bagging, GBR, XGBoost, LightGBM, and stacking were studied. The stacking technique demonstrated promising results: for Flight 92 an RMSE of 0.03% and an MAE of 1.64% were observed, while for Flight 129 the RMSE was 0.66% and the MAE stood at 1.46%. [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]

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.

The increasing importance of accurate battery state estimations in advanced Battery Management Systems (BMS) underscores the need for precise modelling of battery behaviour and characteristics. While equivalent circuit models are widely utilized for their low computational demands, they face challenges in maintaining precision and adaptability during dynamic conditions, posing a persistent concern for future advancements. This study focuses specifically on the battery hysteresis effect, a complicating factor in the modelling and estimation processes. Open circuit voltage (OCV) measurements and parameter identification for equivalent circuit models were conducted on prevalent Li-ion battery technologies, namely nickel-manganese–cobalt (NMC) and lithium-iron-phosphate (LFP). The experimental results indicate the hysteresis effect becomes more significant with lower temperatures. In this paper, a battery model covering the temperature influence on the hysteresis effect is proposed. The proposed model exhibits an average root mean square error of less than 13 mV. The model holds promise for application in modern battery management systems, offering an enhancement to state-of-charge estimation methodologies.

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]

Lithium-ion battery recycling includes discharging and processing exhausted batteries to recover valuable metals for reuse in new battery production. The improper disposal of e-waste draws attention to the possibility of reprocessing used lithium-ion batteries to make progress in recovering valuable metals. In this study, using biodegradable mixed organic acids, valuable metals were extracted from used batteries by a hydrometallurgical process under optimal conditions such as a stirring speed of 200 rpm, mixed acid concentration of ascorbic acid/citric acid (AA/CA) of 50:50 mM, temperature of 50 °C, time of 50 min, and slurry density of 20 g/L. Kinetic studies verified that the apparent activation energies, 43.6, 70.5, 49.8, 60.6, 45, and 6 kJ/mol, and surface chemical reactions controlled the leaching process for Li, Mn, Co, Ni, and Cu from cathode powder obtained from used LIBs. XRD and FT-IR confirmed the crystalline nature of the cathode powder. UV–visible spectra showed a Co(II) complex with λmax at 380 nm by reduction of the Co(III) complex. Lithium was recovered by LiF and as MnO2 using ammonium persulfate. Our efforts aimed to recover it through an economical and environmentally friendly approach. [ABSTRACT FROM AUTHOR]

The increasing importance of accurate battery state estimations in advanced Battery Management Systems (BMS) underscores the need for precise modelling of battery behaviour and characteristics. While equivalent circuit models are widely utilized for their low computational demands, they face challenges in maintaining precision and adaptability during dynamic conditions, posing a persistent concern for future advancements. This study focuses specifically on the battery hysteresis effect, a complicating factor in the modelling and estimation processes. Open circuit voltage (OCV) measurements and parameter identification for equivalent circuit models were conducted on prevalent Li-ion battery technologies, namely nickel-manganese–cobalt (NMC) and lithium-iron-phosphate (LFP). The experimental results indicate the hysteresis effect becomes more significant with lower temperatures. In this paper, a battery model covering the temperature influence on the hysteresis effect is proposed. The proposed model exhibits an average root mean square error of less than 13 mV. The model holds promise for application in modern battery management systems, offering an enhancement to state-of-charge estimation methodologies. [ABSTRACT FROM AUTHOR]

Nowadays, systems that use more electricity in aircraft are increasing due to harmful gas emissions. This increase has created the need to store electrical energy and accelerated the trend towards battery technologies. Since energy storage in batteries occurs as a result of chemical reactions, problems may occur that will damage the battery group or the entire system. These problems are caused by high current, voltage, and temperature, which affect the reaction rate during battery charging/discharging. A Battery Management System (BMS) is needed to prevent problems and to use the required electrical energy safely. In this study, it is aimed to meet the energy needs of the system in a controlled manner by disabling only the damaged cell in case of problems that may occur in the cells in the battery. For this purpose, a model of an Unmanned Aerial Vehicle (UAV) system been created by adding a BMS block to each cell to control the battery cells. The BMS model is realized based on the cell temperature, the State of Charge (SoC) value of the cell, and the output voltage values of the cell. The UAV system is modeled using MATLAB/Simulink environment. Thanks to the proposed BMS, in case of a problem that may occur in any cell in the battery, that cell will be disabled and the required energy will be met through the remaining cells. It is observed from the results obtained that when the cell parameters become normal, it continues to feed the system again. [ABSTRACT FROM AUTHOR]

We report on the use of an atmospheric pressure, open-air plasma treatment to remove Li2CO3 species from the surface of garnet-type tantalum-doped lithium lanthanum zirconium oxide (Li6.4La3Zr1.4Ta0.6O12, LLZTO) solid-state electrolyte pellets. The Li2CO3 layer, which we show forms on the surface of garnets within 3 min of exposure to ambient moisture and CO2, increases the interface (surface) resistance of LLZTO. The plasma treatment is carried out entirely in ambient and is enabled by use of a custom-built metal shroud that is placed around the plasma nozzle to prevent moisture and CO2 from reacting with the sample. After the plasma treatment, N2 compressed gas is flowed through the shroud to cool the sample and prevent atmospheric species from reacting with the LLZTO. We demonstrate that this approach is effective for removing the Li2CO3 from the surface of LLZTO. The surface chemistry is characterized with X-ray photoelectron spectroscopy to evaluate the effect of process parameters (plasma exposure time and shroud gas chemistry) on removal of the surface species. We also show that the open-air plasma treatment can significantly reduce the interface resistance. This platform demonstrates a path towards open-air processed solid-state batteries. [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]

Managing the capacity of lithium-ion batteries (LiBs) accurately, particularly in large-scale applications, enhances the cost-effectiveness of energy storage systems. Less frequent replacement or maintenance of LiBs results in cost savings in the long term. Therefore, in this study, AdaBoost, gradient boosting, XGBoost, LightGBM, CatBoost, and ensemble learning models were employed to predict the discharge capacity of LiBs. The prediction performances of each model were compared based on mean absolute error (MAE), mean squared error (MSE), and R-squared values. The research findings reveal that the LightGBM model exhibited the lowest MAE (0.103) and MSE (0.019) values and the highest R-squared (0.887) value, thus demonstrating the strongest correlation in predictions. Gradient boosting and XGBoost models showed similar performance levels but ranked just below LightGBM. The competitive performance of the ensemble model indicates that combining multiple models could lead to an overall performance improvement. Furthermore, the study incorporates an analysis of key features affecting model predictions using SHAP (Shapley additive explanations) values within the framework of explainable artificial intelligence (XAI). This analysis evaluates the impact of features such as temperature, cycle index, voltage, and current on predictions, revealing a significant effect of temperature on discharge capacity. The results of this study emphasize the potential of machine learning models in LiB management within the XAI framework and demonstrate how these technologies could play a strategic role in optimizing energy storage systems. [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]

The ability to manipulate rheological and electrical properties of colloidal carbon black gels makes them attractive in composites for energy applications such as batteries and fuel cells, where they conduct electricity and prevent sedimentation of 'granular' active components. While it is commonly assumed that granular fillers have a simple additive effect on the composite properties, new phenomena can emerge unexpectedly, with some composites exhibiting a unique rheological bi-stability between high-yield-stress and low-yield-stress states. Here we report such bi-stability in suspensions of non-Brownian graphite and colloidal carbon black in oil, a model system to mimic composite suspensions for energy applications. Steady shear below a critical stress elicits a transition to a persistent mechanically weak and poorly conducting state, which must be 'rejuvenated' using high-stress shear to recover a stronger, high-conductivity state. Our findings highlight the highly tunable nature of binary granular/gel composite suspensions and present new possibilities for optimising mixing and processing conditions for Li-ion battery slurries. [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]

Abstract 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.

Rechargeable batteries are essential components for modern energy systems and electric vehicles (EVs). Accurate estimation of State of Charge (SOC) plays a pivotal role in the reliable operation and efficiency of battery systems. While various methods have been developed to improve SOC estimation, there remains significant potential for further enhancement. This paper presents a hybrid SOC estimation technique specifically designed for EV battery management systems (BMS). The proposed method effectively mitigates the impact of cell deterioration, achieving high-precision SOC estimation. SOC serves as a critical parameter in BMS decision-making. This study integrates the Adaptive Extended Kalman Filter (AEKF) with a Li-ion cell model and the Coulomb Counting technique. Given the computational complexity inherent to AEKF and the susceptibility of the Coulomb Counting method to noise, their combination offers a novel approach characterized by improved accuracy and reduced complexity. The method was validated through extensive simulations in MATLAB-Simulink and experimental testing using a hardware test bench. The results were compared to those of the unscented Kalman filter-based SOC estimation, adaptive integral correction-based methods, and machine learning-based methods. The proposed adaptive strategy shows a 70% reduction in complexity compared to DEKF while achieving an SOC estimation accuracy of up to 1.02%.

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.

This study considers the significance of drones in various civilian applications, emphasizing battery-operated drones and their advantages and limitations, and highlights the importance of energy consumption, battery capacity, and the state of health of batteries in ensuring efficient drone operation and endurance. It also describes a robust testing methodology used to determine battery SoH accurately, considering discharge rates and using machine learning algorithms for analysis. Machine learning techniques, including classical regression models and Ensemble Learning methods, were developed and calibrated using experimental UAV data to predict SoH accurately. Evaluation metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) assess model performance, highlighting the balance between model complexity and generalization. The results demonstrated improved SoH predictions with machine learning models, though complexities may lead to overfitting challenges. The transition from simpler regression models to intricate Ensemble Learning methods is meticulously described, including an assessment of each model’s strengths and limitations. Among the Ensemble Learning methods, Bagging, GBR, XGBoost, LightGBM, and stacking were studied. The stacking technique demonstrated promising results: for Flight 92 an RMSE of 0.03% and an MAE of 1.64% were observed, while for Flight 129 the RMSE was 0.66% and the MAE stood at 1.46%.

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]

The work is aimed at studying the kinetics of silicon electrodeposition from different chloride melts in order to select both optimal melt compositions and their electrolysis parameters. The study was carried out in KCl, KCl-CsCl and LiCl–KCl-CsCl melts with the addition of K2SiF6 at different temperatures depending on the melting point of each system. The optimal deposition potentials and current densities for electrolysis were determined by voltammetry. The LiCl–KCl-CsCl based melts are characterized by the highest silicon electrodeposition rates. Moreover, lowering lithium chloride content increase electrodeposition rates at the same other conditions. In LiCl–KCl-CsCl melt containing lithium chloride, the electrodeposition rate is highest in an electrolyte with a reduced LiCl content due to the lowest decomposition rate of the additive. Galvanostatic and potentiostatic electrolysis was carried out in the melts with different composition that have the highest rate of electrodeposition. It was found that continuous deposits occur in the LiCl–KCl-CsCl melt, while in other melts silicon is deposited in the form of fibers and dendrites. Fibers with a diameter of up to 0.7 μm were obtained in LiCl-free melts, and films consisting mainly of spherical grains with a diameter of up to 1 μm were obtained in a melt with lithium chloride. [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]

We report on the use of an atmospheric pressure, open-air plasma treatment to remove Li2CO3 species from the surface of garnet-type tantalum-doped lithium lanthanum zirconium oxide (Li6.4La3Zr1.4Ta0.6O12, LLZTO) solid-state electrolyte pellets. The Li2CO3 layer, which we show forms on the surface of garnets within 3 min of exposure to ambient moisture and CO2, increases the interface (surface) resistance of LLZTO. The plasma treatment is carried out entirely in ambient and is enabled by use of a custom-built metal shroud that is placed around the plasma nozzle to prevent moisture and CO2 from reacting with the sample. After the plasma treatment, N2 compressed gas is flowed through the shroud to cool the sample and prevent atmospheric species from reacting with the LLZTO. We demonstrate that this approach is effective for removing the Li2CO3 from the surface of LLZTO. The surface chemistry is characterized with X-ray photoelectron spectroscopy to evaluate the effect of process parameters (plasma exposure time and shroud gas chemistry) on removal of the surface species. We also show that the open-air plasma treatment can significantly reduce the interface resistance. This platform demonstrates a path towards open-air processed solid-state batteries.

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]

The vast majority of Earth-orbiting satellites carry an electrical power subsystem (EPS) which main components are solar panels and secondary batteries. During eclipse periods, satellites are powered only by rechargeable batteries which have a large energy density but a limited power density. This fact limits the power capabilities of small satellites during eclipse periods. Due to the large power density of Supercapacitors (SCs), ground and on-orbit tests have been conducted to verify their applicability on satellite EPSs. At the moment, no studies are underway on the issue of sizing SCs for eclipse operations. Hybrid configuration could reduce the mass and volume of EPS or maintain those reference values but increasing peak power capabilities. This paper deals with this issue. On the one hand, novel analytical expressions of a variable capacitance SCs are derived, including time–voltage dependence in constant power applications. Also an equivalent formulation for a SCs bank (SCsB) is derived. On the other hand, a SCsB sizing procedure is presented, considering the energy and power requirements of a particular space mission as an input. Finally, a simple mission is analysed, the results showing an improvement on the hybrid EPS design with respect to the traditional, being the mass reduction of 36%. • Implementation of a simple but accurate supercapacitor (SC) model for spacecraft applications. • Innovative analytical expressions of the performance of a SC in constant power applications. • Novel determination of the electrical parameters of a SCs bank, treated as an equivalent SC model. • Estimation of key variables in the operation of hybrid storage systems for space applications. • Preliminary design of a SCs bank devoted to supply high power during eclipse times. [ABSTRACT FROM AUTHOR]

Solid polymer electrolytes were prepared using polymethyl methacrylate (PMMA), ethylene carbonate (EC), lithium trifluoroethane-sulfonate (LiTf), magnesium trifluoro-methanesulfonate (MgTf2). The ionic conductivity was measured at 30 °C as 6.17 × 10−6 and 1.83 × 10−4 S cm−1, respectively. The ionic mobility (μ), charge carrier diffusion coefficient (D) and the ion number density (n) of the samples have been calculated to understand the effect of these parameters on the ionic conductivity. The experimentally obtained values of Z r and Z i are compared with theoretical calculations. The PMMA-EC-MgTf2 sample exhibits higher dielectric constant compared to PMMA-EC-LiTf. The MgTf2 containing sample also exhibits a higher transference number. [ABSTRACT FROM AUTHOR]

The thermal performance of battery modules has a crucial role in the performance, safety, and lifetime of battery cells. Commonly, battery models are validated through experimental data to ensure the correctness of model behavior; however, the influences of experimental setups are often not considered in the laboratory environment, especially for prismatic cells such as lithium titanate oxide (LTO) battery cells used in electric vehicles. For this purpose, both experimental and numerical studies of the thermal performance of the battery module consisting of LTO cells was investigated using different cold plates used in electrical and hybrid vehicles. Three different discharging rates were applied to the battery module to obtain comparative results of the cooling performance. In the numerical simulations, heat generation models are typically used to observe the thermal behavior of the battery module; however, in the numerical study, dual potential multi-scale multi-domain (MSMD) battery models were used, with transient flow and heat transfer calculations performed. The numerical results were in good agreement with the experimental data. A new high-performance cold plate was developed for the thermal management of LTO battery cells. In comparison with the other two cold plate configurations, the proposed cold plate configuration dropped the maximum temperature up to 45% for the same operating conditions. [ABSTRACT FROM AUTHOR]

Halide perovskite-based energy storage devices have gained much attention due to their high electronic and ionic conductivity. However, poor performance and cyclic stability of hybrid halide perovskite supercapacitors have become the bottleneck for commercialization. Typical electrolytes used so far in halide perovskite-based supercapacitors are nonaqueous tetrabutylammonium tetrafluoroborate, or tetrabutylammonium perchlorate having large cations. We demonstrated that inorganic halide perovskite-based supercapacitors with Li-ion electrolytes are highly efficient with a specific energy density of ∼250 W h/kg. There are no structural changes in the two-dimensional tetragonal phase of the CsPb2Br5 lattice. However, the orthorhombic phases of the three-dimensional CsPbBr3 crystal structure disappear due to Li-ion intercalation/conversion. A quasi-reversibility is observed during the discharging cycles. We have also shown that introducing perovskite nanocrystals can stabilize the quasi-reversible orthorhombic to amorphous phase transition in CsPbBr3. This is mainly because of the nanocrystals' finite size (∼10 nm), where Li-ion intercalation results in a semicrystalline phase. Therefore, no such structural changes are observed in CsPbBr3 nanocrystals during charging or discharging cycles in Li-ion electrolytes. We have further fabricated solid-state supercapacitors by introducing quasi-solid-state gel electrolytes between symmetric electrodes having an energy density over 4.58 μW h/cm2. These devices are stable over 5000 GCD (galvanostatic charge–discharge) cycles with more than 89% capacity retention. [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 subject of study in this article is the voltage ranges, methods, and tools for prototyping independent sources of power supply and artificial lighting for home appliances with reuse of only widespread components. The goal is to improve the efficiency of the creation and use of independent power supply sources for home appliances. Task: to analyze the history of the development of voltage standards for households; analyze existing autonomous power sources and types of batteries; analyze different loads; find rational voltage ranges based on fundamental values; analyze charge control and balancing circuits for lithium-based batteries; propose the technique of prototyping independent sources of power supply based on reused lithium-ion (Li-ion) accumulators; provide an example of the practical application of the results of research. According to the tasks, the following results were obtained. The evolution of voltage standards of electrical supply networks is analyzed. Types of autonomous power supplies, including pure sine versions, are discussed. The analysis of batteries for autonomous power sources of different chemical compositions is performed. It is proposed to use the water analogy of current and area as an analogy of battery capacity for visual representation of electrical processes. Models of constant current consumption and constant power consumption are considered. It is proposed to reduce the internal resistance of the battery assembly by parallel connection of the reused lithium-ion accumulators. Correspondence of voltage ranges of sequential connection of lithium-ion cells to ensure compatibility with existing devices is investigated. Rational parameters of voltage ranges to ensure compatibility of lithium-ion and acid accumulators with the ability to charge directly from solar panels without a charge controller are found. Charge controllers, battery management systems (BMS), and battery balancing circuits are analyzed. A set of steps for reuse of lithium-ion accumulators for the creation of autonomous power sources is proposed. Conclusions. The main contribution of this research is the proposed method of creation of power supply and interior lighting based on the reuse of accumulators without additional components. The discovered and proposed magic numbers of 3, 5, 7, 9, 11 and 13 for series connection of lithium-ion cells allows to obtain the equivalent of standard voltage ranges of 12 V, 19 V, 27 V, 36 V, 42 V and 48 V. The proposed technique of adjusting the voltage of the passive balancer allows adding 4.5 % to the capacity of the battery assembly. The described solutions allow to build the completely scalable autonomous low-voltage electrical supply network with the ability to charge directly from solar panels without expensive charge controllers.

Temperature is known to have a significant impact on the performance and ageing of Li-ion batteries. Because of it, their temperature is monitored and controlled implementing a Battery Thermal Management System (BTMS) in which lumped-parameter thermal models are commonly used to estimate the cells’ thermal behavior, due to their low computation effort. In this regard, it is important to plan a proper calibration procedure able to accurately assess the cell’s thermal properties. While literature presents several methodologies with different levels of accuracy and complexity, there is a lack of a systematic approach. Furthermore, only a few works take into account the heat lost through the cell holder by conduction. In this work, a re-designed cell holder is introduced to minimize its thermal interaction with the cell during testing, then it provides a better understanding of thermal models calibration by comparing two simple, but effective, testing procedures. The specific heat capacity of a cylindrical cell is evaluated by carrying out two testing procedures in which heat flow direction is inverted. In the first procedure, heat is actively generated from the cell, while in the second procedure it is externally provided using a flexible polyimide heater. This has led to two different formulations of the energy balance equation that differ from the internal thermal dissipation modeling. At the end, a validation has been carried out implementing a current profile generated from the UDDS drive cycle. Both formulations have shown to have the similar temperature prediction with a RMSE of 0.484 °C.