Extend Battery Life Research Articles

Modular multilevel converter-based hybrid energy storage system for electric vehicles: Design, simulation, and performance evaluation

ABSTRACT Electric vehicles (EVs) are critical to reducing greenhouse gas emissions and advancing sustainable transportation. This study develops a Modular Multilevel Converter-based Hybrid Energy Storage System (HESS) integrating lithium-ion batteries (BT) and supercapacitors (SC) to enhance energy management and EV performance. A control strategy equalizes voltage across SC modules, achieving deviations within 1% of reference values, while optimizing energy transfer for peak demands. Simulations using PSIM software demonstrate a 12% improvement in energy efficiency and a 15% enhancement in dynamic response compared to battery-only systems. The optimized energy flow extends battery life by 20%, reducing overall ownership costs. Evaluations under the New European Driving Cycle (NEDC) validate the system’s performance, with recorded energy efficiencies of 87% for urban driving, 89% for suburban conditions, and 92% for highways. Supercapacitor modules respond to peak demands within 0.8 seconds, delivering a maximum current of 170 A during acceleration. This innovative approach ensures balanced energy distribution, reduces voltage fluctuations, and increases overall system robustness. Future work will focus on experimental validation under real-world conditions and integrating advanced SC materials to enhance performance. This work bridges a critical gap in energy storage systems for EVs, contributing to cleaner transportation solutions and aligning with global sustainability goals.

Interpreting CNN-RNN Hybrid Model-Based Ensemble Learning with Explainable Artificial Intelligence to Predict the Performance of Li-Ion Batteries in Drone Flights

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.

Advances in wearable biotechnology for cardiac monitoring

Wearable biotechnology is enhancing cardiac health monitoring by enabling continuous data collection for early detection and better management of cardiovascular diseases (CVDs). Recent advancements, such as multi-sensor integration, combine electrocardiography (ECG), heart rate variability, blood pressure, and oxygen saturation monitoring to provide a comprehensive cardiovascular assessment and improve diagnostic precision. Innovations in energy-harvesting technologies, like thermoelectric and piezoelectric materials, extend battery life, reducing the need for frequent recharging and increasing patient adherence. Despite these advances, challenges remain, particularly concerning data security, which requires robust encryption methods and secure data transmission protocols. Regulatory frameworks like the GDPR and HIPAA need to evolve to accommodate technological progress, ensuring new devices are safe and quickly brought to market. Enhancing interoperability with electronic health records (EHRs) is crucial to maximizing the clinical utility of wearable devices. Future research should focus on developing biochemical sensors to detect specific biomarkers in bodily fluids, refining AI algorithms for personalized diagnostics, and establishing standards for data sharing and integration. Collaboration among healthcare providers, regulatory bodies, and technology developers is essential to overcoming current barriers, expanding clinical applications, and ultimately improving patient outcomes and public health.

Covalently Anchoring an Ultrathin Conformal SiOx Coating on Polyolefin Separator for Enhanced Lithium Metal Battery Performance

AbstractSurface modification of separators with inorganic oxide ceramics such as SiOx, Al2O3, and TiO2 has emerged as a promising strategy to suppress lithium dendrite growth in lithium metal batteries, thereby enhancing safety and extending battery life. However, the binder‐dependent nature of these modifications often leads to increased separator thickness and a heightened risk of detachment during lithium plating, ultimately compromising both battery performance and energy density. In this study, a conformal, ultrathin (≈20 nm) SiOx coating with strong covalent bonding is grown on a porous separator through a liquid polymer‐derived method. Compared to the pristine polyethylene (PE) separators, this SiOx‐co‐PE separator exhibits significantly improved electrolyte wettability, mechanical strength, and thermal stability without any increase in thickness, leading to vastly enhanced cycling stability and dendrite resistance in cells with Li metal anodes. In a practical demonstration, a 402.9 Wh·kg−1 Li‐S pouch cell, with a high sulfur loading of 9 mg cm−2 and a low E/S ratio of 3.3 µL mg−1, is assembled with the SiOx‐co‐PE separator, achieving stable cycling over 70 cycles at 25 °C.

Nanomaterials in Solid-State Batteries: Enhancing Safety and Performance

Solid-state batteries (SSBs), utilizing solid electrolytes instead of liquid ones, represent a promising advancement in energy storage technology due to their higher energy density and enhanced safety. Despite their potential, SSBs face significant challenges such as high interfacial resistance, low ionic conductivity, and high production costs. Recent advancements in nanomaterial technology have offered innovative solutions to these issues. Nanomaterials with high specific surface areas and controllable morphologies optimize interfacial contact, reduce resistance, and enhance ionic conductivity through efficient ion transport channels. Additionally, surface modifications and doping improve the chemical and thermal stability of SSB components, extending battery life and preventing adverse reactions. Although initial preparation costs are high, advancements in production technology and large-scale manufacturing are expected to lower these costs, facilitating commercialization. Future research should focus on new nanostructure designs, nanocomposites, and interfacial engineering to further enhance battery performance and safety. Understanding the influence of nanomaterials on safety performance and improving thermal and mechanical shock resistance are crucial for the reliable implementation of solid-state batteries in practical applications.

Enhancing Battery Performance and Safety Through Nanomaterial Coatings

The global transition to electric vehicles (EVs) is crucial for reducing carbon dioxide (CO₂) emissions and combating climate change. Although EVs offer environmental and economic advantages, battery performance challenges such as electrode degradation and electrolyte decomposition hinder their widespread adoption. This paper investigates the use of nanomaterials in battery coatings and membranes to improve EV battery performance and lifespan. Conductive polymers like Polyaniline (PANI), carbon-based materials such as g-C₃N₄/CNTs, and α-Fe₂O₃@C are explored for their capacity to enhance electrode stability, electrical conductivity, and specific capacity. Additionally, the paper addresses how nanocoatings mitigate issues like electrode degradation, parasitic reactions, and thermal management, ultimately extending battery life and improving safety. By integrating nanotechnology, significant advancements in battery efficiency, longevity, and safety can be achieved, making EVs a more viable and sustainable transportation option. These developments are essential for achieving global carbon reduction goals and supporting a cleaner future.

High-Stable All-Iron Redox Flow Battery with Innovative Anolyte based on Steric Hindrance Regulation.

All-soluble all-iron redox flow batteries (AIRFBs) are an innovative energy storage technology that offer significant financial benefits. Stable and affordable redox-active materials are essential for the commercialization of AIRFBs, yet the battery stability must be significantly improved to achieve practical value. Herein, ferrous complexes combined with the triisopropanolamine (TIPA) ligand are identified as promising anolytes to extend battery life by reducing cross-contamination due to a pronounced steric hindrance effect. The coordination structure and failure mechanism of our Fe-TIPA complexes were determined by molecular dynamics simulation and spectroscopic experiments. By coupling with [Fe(CN)6]4-/3-, Fe-TIPA/Fe-CN AIRFBs retained excellent stability exceeding 1831 cycles at 80 mA ⋅ cm-2, yielding an energy efficiency of ~80 % and maintaining a steady discharge capacity. Moreover, the all-soluble electrolyte was tested in an industrial-scale Fe-TIPA/Fe-CN AIRFB prototype energy storage system, where an energy efficiency of 81.3 % was attained. Given the abundance of iron resources, we model the TIPA AIRFB electrolyte cost to be as low as 32.37 $/kWh, which is significantly cheaper than the current commercial level. This work demonstrates that steric hindrance is an effective measure to extended battery life, facilitating the commercial development of affordable flow batteries.

State of Charge Estimation for Lithium-ion Battery Using Long Short-Term Memory Networks

Abstract Accurate estimation of the State of Charge (SOC) in lithium-ion batteries is crucial for enhancing performance and extending battery life, especially in applications like electric vehicles and energy storage systems. This study introduces a novel method for SOC estimation that utilizes Long Short-Term Memory (LSTM) neural networks. To evaluate the LSTM model’s effectiveness, we compared its performance with that of Backpropagation (BP) neural networks and Recurrent Neural Networks (RNN) using the Root Mean Square Error (RMSE) as the evaluation metric. The findings reveal that the LSTM model achieved a significantly lower RMSE of 0.0107, surpassing the performance of the BP neural network and RNN, which had RMSEs of 0.015 and 0.0149, respectively. These results highlight the LSTM network’s exceptional capability to capture the temporal dynamics inherent in battery data, resulting in more accurate SOC predictions. This research underscores the substantial potential of LSTM networks in battery management systems and provides valuable insights into advancing SOC estimation methodologies.

Zincophilic Sites Enriched Hydrogen-bonded Organic Framework as Multifunctional Regulating Interfacial Layers for Stable Zinc Metal Batteries.

To construct an efficient regulating layer for Zn anodes that can simultaneously address the issues of dendritic growth and side reactions is highly demanded for stable zinc metal batteries (ZMBs). Herein, we fabricate a hydrogen-bonded organic framework (HOF) enriched with zincophilic sites as a multifunctional layer to regulate Zn anodes with controlled spatial ion flux and stable interfacial chemistry (MA-BTA@Zn). The framework with abundant H-bonds helps capture H2O and remove the solvated shells on [Zn(H2O)6]2+, leading to suppressed side reactions. The HOF layer also helps form electrolyte-philic surfaces and expose Zn (002) crystal planes which benefit for rapid conduction and uniform deposition of Zn2+, and weakened sides reactions. Additionally, the electrochemically active zincophilic sites (C=O, -NH2 and triazine groups) favor the targeted guidance and penetration of Zn2+ and provide advantageous sites for uniform Zn deposition. High Young's modulus of the HOF layer further contributes to a high interfacial flexibility and stability against Zn plating-associated stress. The MA-BTA@Zn symmetric cells thereby obtain a substantially extended battery life over 1000 h at 4 mA cm-2. The MA-BTA@Zn||Cu half-cell demonstrates a highly reversible Zn stripping/plating process over 1500 cycles with impressive average Coulombic efficiency (CE) of 99.5% at 10 mA cm-2.

Zincophilic Sites Enriched Hydrogen‐Bonded Organic Framework as Multifunctional Regulating Interfacial Layers for Stable Zinc Metal Batteries

AbstractTo construct an efficient regulating layer for Zn anodes that can simultaneously address the issues of dendritic growth and side reactions is highly demanded for stable zinc metal batteries (ZMBs). Herein, we fabricate a hydrogen‐bonded organic framework (HOF) enriched with zincophilic sites as a multifunctional layer to regulate Zn anodes with controlled spatial ion flux and stable interfacial chemistry (MA‐BTA@Zn). The framework with abundant H‐bonds helps capture H2O and remove the solvated shells on [Zn(H2O)6]2+, leading to suppressed side reactions. The HOF layer also helps form electrolyte‐philic surfaces and expose Zn (002) crystal planes which benefit for rapid conduction and uniform deposition of Zn2+, and weakened sides reactions. Additionally, the electrochemically active zincophilic sites (C=O, −NH2 and triazine groups) favor the targeted guidance and penetration of Zn2+ and provide advantageous sites for uniform Zn deposition. High Young's modulus of the HOF layer further contributes to a high interfacial flexibility and stability against Zn plating‐associated stress. The MA‐BTA@Zn symmetric cells thereby obtain a substantially extended battery life over 1000 h at 4 mA cm−2. The MA‐BTA@Zn||Cu half‐cell demonstrates a highly reversible Zn stripping/plating process over 1500 cycles with impressive average Coulombic efficiency (CE) of 99.5 % at 10 mA cm−2.

AI-Augmented Chemical Engineering Models for Predictive Battery Maintenance in IoT Applications

This research presents a cutting-edge approach that combines chemical engineering principles with artificial intelligence (AI) to enhance battery maintenance in Internet of Things (IoT) applications. The study develops predictive maintenance models capable of accurately forecasting battery degradation, thereby optimizing usage patterns and extending battery life. By integrating electrochemical data with AI-driven predictive analytics, the project enables real-time monitoring of battery health, achieving over 90% accuracy in anticipating maintenance needs, which reduces unplanned downtime and lowers operational costs. Employing machine learning frameworks, data analysis tools, and chemical engineering simulation software, the focus is on lithium-ion and solid-state batteries, commonly used in IoT networks across sectors such as smart cities, agriculture, and healthcare. Key results include a 20−30% increase in battery lifespan and a 15% improvement in energy efficiency, significantly reducing electronic waste and environmental footprint. This interdisciplinary approach provides a foundation for sustainable battery technology in IoT technology, paving the way for future advancements in AI-augmented predictive maintenance and chemical engineering models for next-generation applications. The integration of AI with chemical engineering principles allows for the development of models that adapt to the unique electrochemical behavior of different battery types, such as lithium-ion and solid-state batteries. By analyzing lifecycle data and device usage patterns, predictive models can account for variables that influence battery performance over time, including temperature fluctuations and charging cycles. This project utilizes neural networks and reinforcement learning algorithms to train models capable of learning from historical data, enhancing their predictive capabilities. A significant aspect of this research involves the preprocessing of battery lifecycle data to ensure high data quality, which is critical for accurate model training and validation.

Health management of power batteries in low temperatures based on Adaptive Transfer Enformer framework

Accurate State of Charge (SOC) estimation is essential for extending battery life and improving the safety of battery management systems. However, many existing methods face challenges, including a lack of sufficient samples in specific driving modes, overlooking hidden factors such as low temperatures, and experiencing negative transfer in transfer learning. This paper introduces the Adaptive Transfer Enformer (ATE) Framework, which integrates an Enhanced Transformer (Enformer) model with Adaptive Transfer Learning (ATL). The Enformer incorporates Multilevel Residual Attention (MRA) and Pattern Dynamic Decomposition (PDD), forming the backbone of the pre-trained model. MRA addresses gradient vanishing issues due to limited samples and captures the underlying relationships at each time point. PDD dynamically learns temporal trends, hidden factors, and their interactions. ATL provides an effective feature learning strategy to promote positive transfer in SOC estimation. Experimental results on two public datasets with added noise show that the proposed method improves average accuracy compared to state-of-the-art methods. Additionally, results from nine transfer scenarios demonstrate the strong generalization and noise resistance capabilities of the ATE Framework.

Innovative hybrid energy storage systems with sustainable integration of green hydrogen and energy management solutions for standalone PV microgrids based on reduced fractional gradient descent algorithm

This paper investigates innovative solutions to enhance the performance and lifespan of standalone photovoltaic (PV)-based microgrids, with a particular emphasis on off-grid communities. A major challenge in these systems is the limited lifespan of batteries. To overcome this issue, researchers have created hybrid energy storage systems (HESS) along with advanced power management strategies. This study introduces innovative multi-level HESS approaches and a related energy management strategy designed to alleviate the charge/discharge stress on batteries. Comprehensive Matlab Simulink models of various HESS topologies within standalone PV microgrids are utilized to evaluate system performance under diverse weather conditions and load profiles for rural site. The findings reveal that the proposed HESS significantly extends battery life expectancy compared to existing solutions. Furthermore, the paper presents a novel energy management strategy based on the Reduced Fractional Gradient Descent (RFGD) algorithm optimization, tailored for hybrid systems that include photovoltaic, fuel cell, battery, and supercapacitor components. This strategy aims to minimize hydrogen consumption of Fuel Cells (FCs), thereby supporting the production of green ammonia for local industrial use. The RFGD algorithm is selected for its minimal user-defined parameters and high convergence efficiency. The proposed method is compared with other algorithms, such as the Lyrebird Optimization Algorithm (LOA) and Osprey Optimization Algorithm (OOA). The RFGD algorithm exhibits superior accuracy in optimizing energy management, achieving a 15 % reduction in hydrogen consumption. Its efficiency is evident from the reduced computational time compared to conventional algorithms. Although minor losses in computational resources were observed, they were substantially lower than those associated with traditional optimization techniques. Overall, the RFGD algorithm offers a robust and efficient solution for enhancing the performance of hybrid energy systems.

Aging Retardation of Lead-Acid Batteries by Adjusting Charge Controller Threshold Values in Off-grid Photovoltaic System

Hybrid systems or coupled power plants with energy storage are becoming more and more popular due to the rising need for energy. Researchers have been conducting several experiments to assure longer-lasting battery use due to the increasing widespread use of energy storage and the depletion of resources such as silicon and precious metals. In this study, a simulation study is carried out in PVSyst software on lead-acid batteries, which have a low cycle and a very traditional electrochemical structure. The simulation is realized by scaling Spain’s consumption curve in 2023, taken from the European Network of Transmission System Operators for Electricity (ENTSO-E), together with lead acid batteries and off-grid photovoltaic (PV) modules. After the off-grid system design is created and consumption data is imported, it is aimed to extend battery life by changing the charge-discharge threshold values of the charge controllers. Threshold values are in two categories, high and low, and seventeen different cases are simulated. According to the results of the simulation, the state of charge (SOC) levels of the batteries decreases significantly due to radiation and cloudiness in the winter months and remain high in the spring and summer months. When charge controllers are set to high thresholds levels, they cause gassing currents that rise to an average of 20 amps, while battery life extends up to 5.3 years. When low threshold values are used, negligible levels of oxygen and hydrogen gasses are formed in the battery, but battery life decreases to 4.1 years.

Hearing aid benefit in daily life: a qualitative ecological momentary assessment study

IntroductionUnderstanding hearing aid wearer experiences in real-world settings is important to provide responsive and individualized hearing care. This study aimed to describe real-life benefits of hearing aids (HAs), as reported by hearing aid wearers through Ecological Momentary Assessment (EMA) in various listening environments.MethodQualitative content analysis of 1,209 open-text responses, provided through self-initiated EMAs, was conducted. The de-identified data was collected retrospectively via a smartphone app compatible with these HAs. Only text responses reflecting positive hearing aid experiences were analyzed. The 1,209 open-text responses were categorized into 18 pre-determined sub-categories, further organized into five overarching categories: Conversational, Leisure, Device-related aspects, Lifestyle, and Other factors.ResultsAcross these categories, 48 self-generated meaning units highlighted the multifaceted benefits of HAs. In particular, participants reported significant improvements in conversational settings, specifically during phone conversations and meetings, attributed to improved sound quality and speech understanding when wearing their HAs. During leisure activities, particularly TV watching and music listening, clearer sound and ease of Bluetooth streaming contributed to experienced benefits. Lifestyle improvements were reported in occupational and social settings, as hearing aid wearers stated enhanced communication and sound awareness. Device-related factors contributing to positive wearer experiences included extended battery life and the convenience of rechargeable batteries. The most prominent sub-category, other factors, underscored overall satisfaction, comfort with the device, and improved auditory experiences across various environments.ConclusionThis study reveals the diverse benefits of HAs in improving communication, listening experiences, and quality of life across various settings, as captured through EMA. By emphasizing features like direct streaming and rechargeability, the findings highlight the importance of personalized hearing care and the potential of real-time listener feedback to inform device enhancements and support strategies, advancing more tailored and effective hearing rehabilitation.

Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement

This paper explores the advancements and challenges in State of Charge (SOC) estimation techniques for lithium-ion batteries, particularly within the context of electric vehicles (EVs) and renewable energy systems. As the global demand for sustainable energy solutions grows, accurate SOC estimation becomes essential for optimizing battery performance, enhancing safety, and extending battery life. The paper categorizes various SOC estimation methods into three primary approaches: direct measurement techniques, model-based methods, and data-driven algorithms. Direct measurement techniques, such as Coulomb counting and Open Circuit Voltage (OCV), are straightforward but often lack precision under dynamic conditions. Model-based methods, including Equivalent Circuit Models (ECM) and electrochemical models, provide detailed insights into battery behavior but can be computationally intensive. In contrast, data-driven approaches utilizing machine learning algorithms exhibit promising adaptability and accuracy, albeit relying heavily on large datasets. Despite these advancements, several limitations persist. Current SOC estimation methods are hindered by their dependence on accurate battery models and quality data, which can degrade over time. Furthermore, hybrid methods, which combine strengths from various techniques, introduce complexity and demand substantial computational resources. The integration of SOC estimation techniques in EV applications poses additional challenges due to varying operational conditions, necessitating efficient processing of real-time data from multiple sensors. Emerging trends in Battery Management Systems (BMS) are also examined, highlighting the role of AI and machine learning in improving real-time SOC computations and predictive capabilities. Cloud-based BMS systems are identified as a significant development for remote monitoring and control, enhancing the overall reliability of battery systems. This paper aims to provide a comprehensive overview of SOC estimation techniques, identify ongoing challenges, and suggest future research directions to enhance the effectiveness of battery management strategies in the evolving landscape of energy systems. DOI: https://doi.org/10.52783/pst.906

Enhancing electric car lithium-ion batteries by using adaptive materials to improve heat transfer and reduce fatigue

The widespread adoption of electric vehicles (EVs) hinges on the efficiency and reliability of their battery systems. However, EV batteries still have many issues, mostly in terms of quantity, storage efficacy, charging speed, lifespan, and security, particularly, temperature variations, which can cause accidents that can lead to losses of products and persons. Hence, any advancement in the field of EV batteries would improve their performance and thus, boost daily usage of EVs that would reduce the pollution caused by traditional cars. Therefore, the main objective of this study focuses on improving the thermal and mechanical stability, energy storage efficiency, and safety of electric vehicle batteries by incorporating multifunctional materials, particularly phase-change materials (PCM) and shape memory alloys (SMA). By incorporating PCM, the thermal regulation of batteries is enhanced, resulting in better charge-discharge cycles, extended battery life, and reduced overheating risks, all of which contribute to increased safety. SMA integration, on the other hand, offers mechanical protection, flexibility in heat management, and resilience against thermomechanical fatigue, further improving battery longevity. Moreover, the incorporation of these adaptive materials not only boosts battery performance but also ensures that the materials can be seamlessly integrated without affecting the overall design or significantly increasing the weight of the battery. Thus, a systematic numerical study was conducted in order to adapt the multifunctional materials with each other and with EV battery systems. The study demonstrated a significant improvement in battery thermomechanical stability, leading to better storage performance, quality, and charging speed. Particularly, the findings showed a remarkable increase around 23: % in thermal stability duration, preventing excessive temperature variations, and an important reduction in mechanical vibrations around 79 %, which minimizes fatigue and enhances durability. Finally, the results revealed that the proposed adaptable materials are the most stable for car batteries even during fast charging while driving. These materials can also support alternative ways of charging, such as using solar sources.

Development of a Non-Linear Battery Management System Model for Lithium-Ion Batteries Using MATLAB Simulation

Abstract: This research focuses on developing a comprehensive battery model tailored for Battery Management Systems (BMS) using lithium-ion batteries. Given the increasing reliance on lithium-ion batteries in electric vehicles and portable electronic devices, this study aims to construct a non-linear battery model that accurately reflects the charging and discharging characteristics under various load conditions. The research identifies suitable characteristics of lithium-ion batteries essential for effective battery model construction. A MATLAB-Simulink approach is employed to build the model, which integrates both charging and discharging processes into a single simulation framework. This merged model enhances the existing non-linear models by offering improved accuracy in simulating battery behavior under fixed parameters. The outcome is expected to provide a reference model for future studies and applications in power management systems. Additionally, this research underscores the critical role of BMS in extending battery life and ensuring safety, making it a significant contribution to the field of energy management. The study's findings highlight the potential of the proposed model to serve as a foundation for further research and development in battery technologies.

Estimation of state of charge for polymer solid-state batteries: Ensemble learning models and temperature impact study

In addition to the research and development of solid electrolytes to improve battery performance, an efficient battery management system (BMS) is a must to ensure safe use and extend battery life, and state of charge (SOC) estimation is critical in the BMS. This paper presents a novel temperature-influenced method for estimating the SOC of polymer-based solid-state batteries in real-time, using machine learning to capture the relationship between SOC and battery characteristics at different temperatures. The results show that accurate SOC estimation results can be achieved at different temperatures, with an average root mean square error of 1.42 %. Furthermore, the method uses Shapley Additive explanation (SHAP) to reduce the degree of black box nature of the model and analyzes the electrochemical processes inside the battery at different temperatures through relaxation time distribution. Accurate estimates of SOC and guidelines for appropriate operating temperatures can serve as a basis for BMS decision-making and improve the lifetime of polymer-based solid-state batteries.

AI-enhanced backpack with double frequency-up conversion vibration energy converter for motion recognition and extended battery life

With growing interest in physical activities, wearable device sales have been increasing each year due to their help in tracking movement. However, keeping these devices running for a long time is still a key challenge. This paper presents an AI-enhanced backpack equipped with a hybrid vibration energy converter that integrates piezoelectric (PE) and electromagnetic (EM) technologies, featuring a double frequency-up conversion (FUC) mechanism. The PE unit enables efficient motion recognition, while the EM unit extends battery life via kinetic energy harvesting. The double FUC mechanism boosts human motion signal strength and resolution by converting sub-Hertz frequencies to hundreds of Hertz. This makes the device particularly effective in environments with performance constraints and interference. By adopting deep learning techniques, the system maintains a high motion recognition accuracy of 97.33 % even under low data sampling rates and significant noise interference. The EM unit effectively harvests kinetic energy, generating an average output power of 4.5 mW during activities such as running at 4.5 Hz, despite of the device’s compact size. Additionally, a power management circuit with human motion-stimulated switch optimizes battery life by managing the system’s sleep and active states. This innovative energy management approach ensures that during human activity, the system conserves energy by remaining in sleep state for at least 30 % of the time, significantly extending battery life and enhancing the backpack’s suitability for outdoor applications.