This paper presents a novel battery health management technology for the new generation of electric unmanned aerial vehicles powered by long-life, high-density, scalable power sources. Current reliability based techniques are insufficient to manage the use of such batteries when they are an active power source with frequently varying loads in uncertain environments. The technique presented here encodes the basic electrochemical processes of a Lithium-polymer battery in an advanced Bayesian inference framework to simultaneously track battery state-of-charge as well as tune the battery model to make accurate predictions of remaining useful life. Results from ground tests with emulated flight profiles are presented with discussions on the use of such prognostics results for decision making.

Cathode materials that have high specific energies and low manufacturing costs are vital for the scaling up of lithium-ion batteries (LIBs) as energy storage solutions. Fe-based intercalation cathodes are highly attractive because of the low-cost and the abundance of the raw materials. However, existing Fe-based materials, such as LiFePO₄ suffer from low capacity due to the large size of the polyanions. Turning to mixed anion systems can be a promising strategy to achieve higher specific capacity. Recently, anti-perovskite structured oxysulphide Li₂FeSO has been synthesised and reported to be electrochemically active.
In this work, we perform an extensive computational search for iron-based oxysulphides using ab initio random structure searching (AIRSS). By performing an unbiased sampling of the Li-Fe-S-O chemical space, several new oxysulphide phases have been discovered which are predicted to be less than 50 meV/atom from the convex hull and potentially accessible for synthesis.
Among the predicted phases, two anti-Ruddlesden-Popper structured materials Li₂Fe₂S₂O and Li₄Fe₃S₃O₂
have been found to be attractive as they have high theoretical capacities with calculated average voltages 2.9 V and 2.5 V respectively. With band gaps as low as about 2.0 eV, they are expected to exhibit good electronic conductivities.
By performing nudged-elastic band calculations, we show that the Li-ion transport in these materials takes place by hopping between the nearest neighbouring sites with low activation barriers between 0.3 eV and 0.5 eV.
The richness of new materials yet to be synthesised in the Li-Fe-S-O phase field illustrate the great opportunity in these mixed anion systems for energy storage applications and beyond.

The dataset includes the structure searching results and outputs of further property calculations. The analysis codes are also included as Jupyter Notebooks.

Also hosted on GitHub.

Preprint hosted on ChemRxiv.

Validation of prognostic technologies through ground and flight tests is an important step in maturing these novel technologies and deploying them on real-world systems. To this end, a series of flight tests have been conducted using an unmanned electric vehicle during which the motor system batteries were monitored by a prognostic algorithm. The research presented here endeavors to produce and validate a technology for predicting the remaining time until end-ofdischarge of the batteries on an electric aircraft as a function of an expected future flight and online estimates of the charge contained in the batteries. Flight data and flight experiment results are presented along with an assessment of model and algorithm performance

A novel RUL prediction approach for lithium-ion batteries using quantum particle swarm optimization (QPSO)-based particle filter (PF) is proposed. Compared to particle swarm optimization (PSO)-based PF, QPSO-based PF is proved to have a better performance in global searching and has fewer parameters to control, which makes QPSO-PF easier for applications. Moreover, fewer particles are required by QPSO-PF to accurately track the battery's health status, leading to a reduction of computation complexity. RUL prediction results using real data provided by NASA and compared with benchmark approaches demonstrates the superiority of the proposed approach.

This repository contains MATLAB code for predicting battery capacity using an Artificial Neural Network (ANN) trained on structured cycling data. The script utilizes Bayesian optimization to fine-tune hyperparameters, enabling more accurate forecasting of capacity degradation over time. This code was used in the paper titled: "Computational Micromechanics and Machine Learning-Informed Design of Composite Carbon Fiber-Based Structural Battery for Multifunctional Performance Prediction."

It is a clear and modular code that takes voltage and current data as input features, performs normalization, splits the data into training/validation/testing sets, and builds an ANN using MATLAB’s Deep Learning Toolbox. In my case, the code was applied to carbon fiber-based structural battery data to evaluate long-term electrochemical performance. This code was developed during my Master’s research at KAIST (Korea Advanced Institute of Science and Technology).

AI-Driven Battery Management Systems Market by Component (Hardware, Software, Services), Application (Electric Vehicles, Energy Storage), Distribution Channel, End User, and Geography - Global Forecast to 2032

The Battery Monitoring IC market is experiencing robust growth, driven by the increasing demand for electric vehicles (EVs), portable electronics, and energy storage systems. The market's expansion is fueled by the crucial role these ICs play in ensuring battery safety, extending battery lifespan, and optimizing battery performance. Advancements in battery technology, particularly in lithium-ion batteries, are further stimulating demand for sophisticated monitoring solutions. Factors such as increasing adoption of renewable energy sources and stringent government regulations regarding battery safety are also contributing to market growth. The market is segmented by type (single-cell, multi-cell), application (EVs, portable electronics, industrial equipment), and geography. Major players in this space are actively investing in research and development to improve accuracy, reduce power consumption, and enhance the functionality of their offerings. This competitive landscape fosters innovation and drives down costs, making battery monitoring ICs increasingly accessible for diverse applications. The forecast period (2025-2033) anticipates continued strong growth, propelled by the expanding EV market and the proliferation of smart devices. While the supply chain complexities and potential material shortages pose challenges, the long-term outlook remains positive. The integration of advanced features like predictive maintenance capabilities and improved communication protocols is expected to drive premiumization within the market. Regional variations in market growth will depend on factors like government incentives for EVs, the adoption of renewable energy technologies, and the overall economic climate. The dominance of established players like Texas Instruments, Analog Devices, and STMicroelectronics is likely to persist, although smaller, specialized companies could carve out niches with innovative product offerings. Overall, the Battery Monitoring IC market presents a significant opportunity for growth and technological advancement in the coming years.

The Battery Life Cycle Management (BLCM) solution market, valued at $230 million in 2025, is poised for robust growth, driven by the burgeoning electric vehicle (EV) and energy storage system (ESS) sectors. A Compound Annual Growth Rate (CAGR) of 5.1% from 2025 to 2033 projects a significant market expansion, fueled by increasing demand for efficient battery management, extended battery lifespan, and sustainable battery recycling practices. Key growth drivers include stringent environmental regulations promoting responsible battery disposal, the escalating adoption of EVs globally, and the rising demand for grid-scale energy storage solutions to support renewable energy integration. The market segmentation reveals strong growth across application areas, with Electric Vehicles and Energy Storage Batteries leading the charge, complemented by substantial growth in the UPS segment. Hardware solutions currently dominate the market, but the software and service segments are expected to witness significant growth as businesses increasingly look to leverage data-driven insights for predictive maintenance and optimized battery utilization. Geographical analysis reveals North America and Europe as mature markets, while the Asia-Pacific region is predicted to exhibit substantial growth potential due to its large EV manufacturing base and expanding energy storage infrastructure. The competitive landscape is dynamic, with established players like Siemens and Bosch Mobility alongside innovative technology providers like TWAICE and NExT-e Solutions Inc. vying for market share. The market’s future trajectory hinges on continued technological advancements in battery chemistry, improved data analytics capabilities for predictive maintenance, and the development of robust and economically viable battery recycling infrastructure. Furthermore, collaborations between battery manufacturers, technology providers, and recycling companies will be crucial in shaping the future of BLCM solutions, ensuring the seamless integration of sustainable practices throughout the entire battery life cycle. The market’s success is intrinsically linked to the ongoing transition towards cleaner energy solutions and the global push for sustainable transportation. Addressing challenges like the high cost of implementation and the need for standardized battery management protocols will be crucial for unlocking the full market potential.

Market Analysis: Lithium-Ion Battery Management Systems The global lithium-ion battery management systems market is projected to reach $XX million by 2033, exhibiting a CAGR of XX% over the forecast period (2025-2033). The market is primarily driven by the increasing adoption of electric vehicles, growing concerns over grid stability and power outages, and rising demand for energy storage solutions in various industries. Additionally, advancements in battery technology, such as the development of solid-state batteries, are expected to further enhance growth in the coming years. Major trends prevailing in the market include the adoption of advanced sensing and monitoring technologies for improved battery performance and safety, the integration of artificial intelligence and machine learning algorithms for predictive analytics, and the emergence of cloud-based BMS platforms for remote monitoring and control. Key market players such as Calsonic Kansei, Continental AG, DENSO, and Panasonic are investing heavily in research and development to introduce innovative BMS solutions. The increasing emphasis on sustainability and environmental regulations is expected to drive the adoption of BMS across multiple sectors, including automotive, energy storage, and consumer electronics. The global lithium-ion battery management systems (BMS) market is set to witness robust growth in the coming years, driven by the rising adoption of electric vehicles (EVs) and the increasing demand for renewable energy. BMSs play a crucial role in optimizing the performance and safety of lithium-ion batteries, making them an essential component in various applications, including EVs, consumer electronics, and industrial equipment.

Batteries represent complex systems whose internal state vari- ables are either inaccessible to sensors or hard to measure un- der operational conditions. This work exemplifies how more detailed model information and more sophisticated prediction techniques can improve both the accuracy as well as the re- sidual uncertainty of the prediction in Prognostics and Health Management. The more dramatic performance improvement between various prediction techniques is in their ability to learn complex non-linear degradation behavior from the train- ing data and discard any external noise disturbances. An algorithm that manages these sources of uncertainty well can yield higher confidence in predictions, expressed by narrower uncertainty bounds. We observed that the particle filter approach results in RUL distributions which have better precision (narrower pdfs) by several σs (if approximated as Gaussian) as compared to the other regression methods. How- ever, PF requires a more complex implementation and compu- tational overhead than the other methods. This illustrates the basic tradeoff between modeling and algorithm development versus prediction accuracy and precision. For situations like battery health management where the rate of capacity degrada- tion is rather slow, one can rely on simple regression methods that tend to perform well as more data are accumulated and still predict far enough in advance to avoid any catastrophic failures. Techniques like GPR or even the baseline approach can offer a suitable platform in these situations by managing the uncertainty fairly well with much simpler implementations. Other data sets may allow much smaller prediction horizons and hence require precise techniques like particle filters. In this study, we conclude that there are several methods one could employ for battery health management applica- tions. Based on end user requirements and available resources, a choice can be made between simple or more elegant tech- niques. The particle filter based approach emerges as the winner when accuracy and precision are considered more important than other requirements.

such as on voltage or state-of-charge

Early and accurate battery lifetime predictions could accelerate battery R&D and product development timelines by providing insights into performance after only a few days or weeks of testing rather than waiting months to reach degradation thresholds. However, most machine learning (ML) models are developed using a single dataset, leaving unanswered questions about the broader applicability and potential impact of such models for other battery chemistries or cycling conditions. In this work, we take advantage of the open-access cycling performance data within the recently released Voltaiq Community to determine the extensibility of a highly cited feature-based linear ML model used for battery lifetime prediction. We find that the model is unable to extrapolate to different datasets, with severe model overfitting resulting in unphysical lifetime predictions of much of the unseen data. We further identify that the features engineered for this model are likely specific to the degradation mode for the original lithium iron phosphate (LFP) fast-charge dataset and are unable to capture the lifetime behavior of other cathode chemistries and cycling protocols. We provide an open access widget-based Jupyter Notebook script that can be used to explore model training and lifetime prediction on data within the Voltaiq Community platform. This work motivates the importance of using larger and more diverse datasets to identify ML model boundaries and limitations, and suggests training on larger and diverse datasets is required to develop data features that can predict a broader set of failure modes.

The Battery Detection Software market is experiencing robust growth, projected to reach a market size of $3,366.8 million by 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 10.7% from 2025 to 2033. This significant expansion is driven by several key factors. The increasing adoption of electric vehicles (EVs) and hybrid electric vehicles (HEVs) is a primary catalyst, demanding sophisticated software solutions for battery health monitoring and predictive maintenance. Furthermore, the growing prevalence of portable electronic devices, such as smartphones and laptops, necessitates reliable battery management systems to optimize performance and extend lifespan. Advancements in battery technology, including the development of solid-state batteries and improved battery chemistries, are also fueling market growth as these innovations require more advanced software for efficient management. The market is segmented by deployment type (local and cloud-based) and application (automotive, mobile devices, and others), with the automotive segment currently dominating due to the increasing demand for EV battery monitoring systems. The cloud-based segment is experiencing faster growth due to the advantages of remote monitoring and data analytics. Geographical expansion is also a key driver, with North America and Europe representing significant market shares initially, followed by rapid growth in the Asia-Pacific region due to the burgeoning EV market and increasing smartphone penetration. The competitive landscape comprises both established players and emerging startups. Established players often possess extensive experience in providing battery testing equipment and related services, leveraging this expertise into software solutions. Startups are innovating with advanced algorithms and machine learning techniques to deliver improved prediction capabilities and enhanced user experiences. While regulatory changes and cybersecurity concerns represent potential restraints, the overall market outlook remains highly positive, fueled by technological advancements, the global shift towards electrification, and the increasing demand for reliable battery performance across diverse applications. The continued development of AI and machine learning capabilities within battery detection software will likely drive further innovation and market growth in the years to come.

The Battery Detection Software market is experiencing robust growth, projected to reach $3,842.5 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 11.2% from 2025 to 2033. This expansion is driven by the increasing demand for electric vehicles (EVs) and mobile devices, necessitating sophisticated battery management systems for safety, performance, and longevity. The rise of the Internet of Things (IoT) and connected devices further fuels this demand, as these applications require constant monitoring of battery health. Cloud-based solutions are gaining traction due to their scalability and remote monitoring capabilities, offering advantages over local software solutions in terms of data analysis and predictive maintenance. Key players like Voltaiq, TWAICE, and Machinery Analytics are actively contributing to innovation and market competition, offering diverse software solutions tailored to various applications. The automotive sector currently holds a significant share of the market, but the mobile device segment is anticipated to witness substantial growth, driven by the ever-increasing adoption of smartphones and wearables with advanced battery management requirements. Geographically, North America and Europe are currently leading the market, but Asia-Pacific is expected to witness the fastest growth due to the burgeoning EV and electronics manufacturing sectors in countries like China and India. The market’s restraints mainly stem from the complexity of battery chemistry and the need for specialized expertise in integrating these software solutions. High initial investment costs and the need for continuous updates to accommodate evolving battery technologies also pose challenges. However, ongoing technological advancements in artificial intelligence (AI) and machine learning (ML) are providing opportunities for more accurate and predictive battery health assessments, mitigating some of these restraints. The market is expected to further segment based on battery type (Lithium-ion, lead-acid, etc.), further driving specialization and competition within the sector. The focus on improving battery life, extending vehicle range, and preventing catastrophic failures will continue to propel growth throughout the forecast period.

The global Lithium Battery Energy Management System (BEMS) market, valued at $1350 million in 2025, is projected to experience steady growth, driven by the escalating demand for electric vehicles (EVs), energy storage systems (ESS), and grid-scale energy storage solutions. The 3.1% CAGR from 2025 to 2033 reflects a consistent, albeit moderate, expansion fueled by advancements in battery technology, increasing concerns about climate change, and supportive government policies promoting renewable energy adoption. The hardware segment currently dominates the market, given the significant component costs associated with battery monitoring and control hardware. However, the software segment is expected to witness faster growth due to the increasing sophistication of BEMS algorithms and the need for advanced data analytics and predictive maintenance capabilities. Key applications include power stations, which utilize BEMS for optimizing energy storage and distribution, and other emerging applications such as portable electronics, industrial equipment and medical devices. Growth will be geographically diverse; North America and Europe will continue to be major markets due to established EV infrastructure and renewable energy investments, while Asia Pacific, particularly China and India, will showcase substantial growth potential given increasing EV adoption rates and supportive government initiatives. Competitive pressures are high, with established players such as GE, Honeywell, Johnson Controls, Schneider Electric, Siemens, ABB Group, Emerson Electric, and HNAC Technology vying for market share through technological innovation and strategic partnerships. The market's moderate growth rate is partly constrained by the relatively high initial investment costs associated with BEMS implementation and the ongoing need to address safety and reliability concerns related to lithium-ion batteries. However, ongoing technological advancements leading to improved efficiency, reduced costs, and enhanced safety features will likely mitigate these challenges over the forecast period. Future market growth hinges on continued investment in research and development, the successful integration of BEMS into smart grids, and the expansion of EV adoption globally. The market will also benefit from advancements in artificial intelligence (AI) and machine learning (ML) which allow for improved predictive maintenance and real-time optimization of battery performance.

Mechanical, Electrical and Thermal Information on Mechanical Abuse of Batteries under Different Operating Conditions

Please see the file readme.txt for information about the data Lithium-ion (Li-ion) batteries are the most popular energy storage technology in consumer electronics and electric vehicles and are increasingly applied in stationary storage systems. Yet, concerns about safety and reliability remain major obstacles, which must be addressed in order to improve the acceptance of this technology. The gradual degradation of Li-ion cells over time lies at the heart of this problem. Time, usage and environmental conditions lead to performance deterioration and cell failures, which, in rare cases, can be catastrophic due to fires or explosions. The physical and chemical mechanisms responsible for degradation are numerous, complex and interdependent. Our understanding of degradation and failure of Li-ion cells is still very limited and more limited yet are reliable and practical methods for the detection and prediction of these phenomena. This dataset contains the results of long term cycling of 8 lithium-ion cells in our lab in Oxford. The full details are given in the readme.txt file.

Automotive Cathode Current Collector for Lithium-Ion Battery Market Outlook 2032

The global automotive cathode current collector for lithium-ion battery market size was USD 1.5 Billion in 2023 and is projected to reach USD 14.6 Billion by 2032, expanding at a CAGR of 25% during 2024–2032. The market growth is attributed to the growing electrification in the automotive industry.



Increasing electrification in the automotive industry is bringing the automotive cathode current collector for lithium-ion batteries into the spotlight. This critical component, which facilitates the flow of electrons during the discharge process, plays a pivotal role in enhancing the energy density, lifespan, and overall performance of lithium-ion batteries. These advanced batteries, equipped with efficient cathode current collectors, are driving the transition towards sustainable transportation as the heart of electric vehicles.



Rising regulatory pressure from organizations such as the European Union and the Environmental Protection Agency is leading to strict standards for vehicle emissions and energy efficiency. These regulations encourage the adoption of electric vehicles and, by extension, the use of efficient lithium-ion batteries with high-performance cathode current collectors. This regulatory landscape and the escalating demand for electric vehicles, is poised to drive the market.

Impact of Artificial Intelligence (AI) in Automotive Cathode Current Collector for Lithium-Ion Battery Market

Artificial Intelligence (AI) has a significant impact on the automotive cathode current collector for lithium-ion battery market. AI's integration into these systems facilitates real-time monitoring and predictive analysis, optimizing battery performance and extending lifespan. This technology enables smart charging, adjusting charging rates based on usage patterns and battery health, thereby improving energy efficiency.



AI-driven cathode current collectors are crucial in ensuring optimal battery performance and vehicle range in the realm of electric vehicles. Moreover, AI's ability to process and learn from vast amounts of data allows for continuous system improvement, leading to efficient and durable lithium-ion batteries. Therefore, AI&

Battery Management System (BMS) Market Outlook

The global Battery Management System (BMS) market size was estimated at USD 5.2 billion in 2023 and is projected to reach approximately USD 15.7 billion by 2032, growing at a robust CAGR of 12.7% over the forecast period. This substantial growth can be attributed to the increasing adoption of electric vehicles (EVs), rising demand for renewable energy storage systems, and technological advancements in battery management solutions. The need for efficient battery management to ensure safety, reliability, and longevity of batteries across various applications is driving the market forward.

One of the key growth factors fueling the BMS market is the burgeoning electric vehicle industry. As governments worldwide enforce stringent emissions regulations and offer incentives for EV adoption, automakers are ramping up production of electric and hybrid vehicles. BMS plays a crucial role in managing battery performance, enhancing efficiency, and ensuring safety, which in turn boosts the demand for advanced BMS solutions. Additionally, the growing consumer awareness regarding the environmental benefits of EVs further propels market growth.

Another significant driver is the rising demand for renewable energy storage systems. With the global shift towards renewable energy sources such as solar and wind power, the need for efficient energy storage solutions has become paramount. BMS ensures the optimal performance of energy storage systems by monitoring and managing battery health, thus enhancing the reliability and lifespan of these systems. This trend is expected to accelerate the adoption of BMS in renewable energy applications.

Technological advancements in battery management solutions are also contributing to market growth. Innovations such as AI and IoT integration in BMS are providing enhanced monitoring, predictive maintenance, and superior battery performance analytics. These advancements offer opportunities for improved battery efficiency and longevity, which are critical for various applications ranging from consumer electronics to industrial systems. The continuous development of more sophisticated BMS technologies is likely to create new growth avenues in the market.

Geographically, North America and Asia Pacific are poised to dominate the BMS market. North America, with its strong automotive industry and growing adoption of EVs, is a significant contributor to market growth. Meanwhile, Asia Pacific's rapid industrialization, extensive consumer electronics market, and increasing renewable energy projects are driving substantial demand for BMS in the region. Europe, with its stringent environmental regulations and strong focus on sustainability, also presents promising growth opportunities for the BMS market.

Battery Type Analysis

The Battery Management System market can be segmented by battery type, which includes Lithium-Ion, Lead-Acid, Nickel-Based, Flow Batteries, and others. Among these, Lithium-Ion batteries hold the largest market share owing to their high energy density, long cycle life, and broad application range. The automotive industry's strong preference for Lithium-Ion batteries in electric vehicles significantly boosts the demand for BMS in this segment. Additionally, the consumer electronics industry’s reliance on Lithium-Ion batteries for various gadgets underscores the importance of efficient battery management solutions.

Lead-Acid batteries, traditionally used in automotive and industrial applications, also exhibit considerable demand for BMS. Although they are being gradually replaced by Lithium-Ion batteries, Lead-Acid batteries remain relevant due to their lower cost and robustness in certain applications such as backup power and uninterruptible power supplies (UPS). The need to optimize performance and extend the lifespan of Lead-Acid batteries continues to drive the adoption of BMS in this segment.

Nickel-Based batteries, known for their stability and high energy density, find applications in various sectors including aerospace and defense. The demand for Nickel-Based batteries is particularly high in critical applications where reliability is paramount. Despite being more expensive than Lead-Acid batteries, their long service life and ability to operate under extreme conditions make them a viable option, necessitating advanced BMS solutions to ensure optimal performance.

Flow batteries, which offer advantages such as scalability and long-duration energy storage, are gaining traction in renewable energy