Machine learning toward advanced energy storage devices and systems

New Engineering Science Insights into the Electrode Materials Pairing of Electrochemical Energy Storage Devices

Pairing the positive and negative electrodes with their individual dynamic characteristics at a realistic cell level is essential to the practical optimal design of electrochemical energy storage devices. However, the complex relationship between the performance data measured for individual electrodes and the two-electrode cells used in practice often makes an optimal pairing experimentally challenging. Taking advantage of the developed tunable graphene-based electrodes with controllable structure, experiments with machine learning are successfully united to generate a large pool of capacitance data for graphene-based electrode materials with varied slit pore sizes, thicknesses, and charging rates and numerically pair them into different combinations for two-electrode cells. The results show that the optimal pairing parameters of positive and negative electrodes vary considerably with the operation rate of the cells and are even influenced by the thickness of inactive components. The best-performing individual electrode does not necessarily result in optimal cell-level performance. The machine learning-assisted pairing approach presents much higher efficiency compared with the traditional trial-and-error approach for the optimal design of supercapacitors. The new engineering science insights observed in this work enable the adoption of artificial intelligence techniques to efficiently translate well-developed high-performance individual electrode materials into real energy storage devices.

Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management

Rheumatoid arthritis (RA) is an autoimmune disease causing progressive joint damage. Early diagnosis and treatment is critical, but remains challenging due to RA complexity and heterogeneity. Machine learning (ML) techniques may enhance RA management by identifying patterns within multidimensional biomedical data to improve classification, diagnosis, and treatment predictions. In this review, we summarize the applications of ML for RA management. Emerging studies or applications have developed diagnostic and predictive models for RA that utilize a variety of data modalities, including electronic health records, imaging, and multi-omics data. High-performance supervised learning models have demonstrated an Area Under the Curve (AUC) exceeding 0.85, which is used for identifying RA patients and predicting treatment responses. Unsupervised learning has revealed potential RA subtypes. Ongoing research is integrating multimodal data with deep learning to further improve performance. However, key challenges remain regarding model overfitting, generalizability, validation in clinical settings, and interpretability. Small sample sizes and lack of diverse population testing risks overestimating model performance. Prospective studies evaluating real-world clinical utility are lacking. Enhancing model interpretability is critical for clinician acceptance. In summary, while ML shows promise for transforming RA management through earlier diagnosis and optimized treatment, larger scale multisite data, prospective clinical validation of interpretable models, and testing across diverse populations is still needed. As these gaps are addressed, ML may pave the way towards precision medicine in RA.

Connectionist technique estimates of hydrogen storage capacity on metal hydrides using hybrid GAPSO-LSSVM approach

The AB2 metal hydrides are one of the preferred choices for hydrogen storage. Meanwhile, the estimation of hydrogen storage capacity will accelerate their development procedure. Machine learning algorithms can predict the correlation between the metal hydride chemical composition and its hydrogen storage capacity. With this purpose, a total number of 244 pairs of AB2 alloys including the elements and their respective hydrogen storage capacity were collected from the literature. In the present study, three machine learning algorithms including GA-LSSVM, PSO-LSSVM, and HGAPSO-LSSVM were employed. These models were able to appropriately predict the hydrogen storage capacity in the AB2 metal hydrides. So the HGAPSO-LSSVM model had the highest accuracy. In this model, the statistical factors of R2, STD, MSE, RMSE, and MRE were 0.980, 0.043, 0.0020, 0.045, and 0.972%, respectively. The sensitivity analysis of the input variables also illustrated that the Sn, Co, and Ni elements had the highest effect on the amount of hydrogen storage capacity in AB2 metal hydrides.

Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional "trial and error" method for producing advanced electrocatalysts is not only cost-ineffective but also time-consuming and labor-intensive. Fortunately, the advancement of machine learning brings new opportunities for electrocatalysts discovery and design. By analyzing experimental and theoretical data, machine learning can effectively predict their hydrogen evolution reaction (HER) performance. This review summarizes recent developments in machine learning for low-dimensional electrocatalysts, including zero-dimension nanoparticles and nanoclusters, one-dimensional nanotubes and nanowires, two-dimensional nanosheets, as well as other electrocatalysts. In particular, the effects of descriptors and algorithms on screening low-dimensional electrocatalysts and investigating their HER performance are highlighted. Finally, the future directions and perspectives for machine learning in electrocatalysis are discussed, emphasizing the potential for machine learning to accelerate electrocatalyst discovery, optimize their performance, and provide new insights into electrocatalytic mechanisms. Overall, this work offers an in-depth understanding of the current state of machine learning in electrocatalysis and its potential for future research.

Clustering and dynamic recognition based auto-reservoir neural network: A wait-and-see approach for short-term park power load forecasting

This paper proposes a novel clustering and dynamic recognition-based auto-reservoir neural network (CDbARNN) for short-term load forecasting (STLF) of industrial park microgrids. In CDbARNN, the available load sets are first decomposed into several clusters via K-means clustering. Then, by extracting characteristic information of the load series input to CDbARNN and the load curves belonging to each cluster center, a dynamic recognition technology is developed to identify which cluster of the input load series belongs to. After that, the input load series and the load curves of the cluster to which it belongs constitute a short-term high-dimensional matrix entered into the reservoir of CDbARNN. Finally, reservoir node numbers of CDbARNN which are used to match different clusters are optimized. Numerical experiments conducted on STLF of an actual industrial park microgrid indicate the dominating performance of the proposed approach through several cases and comparisons with other well-known deep learning methods.

A Comparative Study on Different Online State of Charge Estimation Algorithms for Lithium-Ion Batteries

With an accurate state of charge (SOC) estimation, lithium-ion batteries (LIBs) can be protected from overcharge, deep discharge, and thermal runaway. However, selecting appropriate algorithms to maintain the trade-off between accuracy and computational efficiency is challenging, especially under dynamic load profiles such as electric vehicles. In this study, seven different widely utilized online SOC estimation algorithms were considered with the following goals: (a) to compare the accuracy of the different algorithms; (b) to compare the computational time in the simulation. Since the 2-RC battery model is highly accurate and not very computationally complex, it was selected for implementing the considered algorithms for the model-based SOC estimation. The considered online SOC estimation performance was evaluated using measurement data obtained from experimental tests on commercial lithium manganese cobalt oxide batteries. The experimental analysis consisted of a dynamic current profile comprising a worldwide harmonized light vehicle test procedure (WLTP) cycle and constant current discharging pulses. In addition, the performance of the considered different algorithms was compared in terms of estimation error and computational time to understand the challenges of each algorithm. The results indicated that the extended Kalman filter (EKF) and sliding mode observer (SMO) were the best choices because of their estimation accuracy and computation time. However, achieving the SOC estimation accuracy depended on the battery modeling. On the other hand, the estimated SOC root means square error (RMSE) using a backpropagation neural network (BPNN) was less than that using a Luenberger observer (LO). Moreover, with the advantages of BPNNs, such as no need for battery modeling, the estimation error could be further reduced using a large size dataset.

Evaluation guidelines for machine learning tools in the chemical sciences

Machine learning (ML) promises to tackle the grand challenges in chemistry and speed up the generation, improvement and/or ordering of research hypotheses. Despite the overarching applicability of ML workflows, one usually finds diverse evaluation study designs. The current heterogeneity in evaluation techniques and metrics leads to difficulty in (or the impossibility of) comparing and assessing the relevance of new algorithms. Ultimately, this may delay the digitalization of chemistry at scale and confuse method developers, experimentalists, reviewers and journal editors. In this Perspective, we critically discuss a set of method development and evaluation guidelines for different types of ML-based publications, emphasizing supervised learning. We provide a diverse collection of examples from various authors and disciplines in chemistry. While taking into account varying accessibility across research groups, our recommendations focus on reporting completeness and standardizing comparisons between tools. We aim to further contribute to improved ML transparency and credibility by suggesting a checklist of retro-/prospective tests and dissecting their importance. We envisage that the wide adoption and continuous update of best practices will encourage an informed use of ML on real-world problems related to the chemical sciences.

Understanding the Electric Double-Layer Structure, Capacitance, and Charging Dynamics

Significant progress has been made in recent years in theoretical modeling of the electric double layer (EDL), a key concept in electrochemistry important for energy storage, electrocatalysis, and multitudes of other technological applications. However, major challenges remain in understanding the microscopic details of the electrochemical interface and charging mechanisms under realistic conditions. This review delves into theoretical methods to describe the equilibrium and dynamic responses of the EDL structure and capacitance for electrochemical systems commonly deployed for capacitive energy storage. Special emphasis is given to recent advances that intend to capture the nonclassical EDL behavior such as oscillatory ion distributions, polarization of nonmetallic electrodes, charge transfer, and various forms of phase transitions in the micropores of electrodes interfacing with an organic electrolyte or ionic liquid. This comprehensive analysis highlights theoretical insights into predictable relationships between materials characteristics and electrochemical performance and offers a perspective on opportunities for further development toward rational design and optimization of electrochemical systems.

Battery health evaluation using a short random segment of constant current charging

Accurately evaluating the health status of lithium-ion batteries (LIBs) is significant to enhance the safety, efficiency, and economy of LIBs deployment. However, the complex degradation processes inside the battery make it a thorny challenge. Data-driven methods are widely used to resolve the problem without exploring the complex aging mechanisms; however, random and incomplete charging-discharging processes in actual applications make the existing methods fail to work. Here, we develop three data-driven methods to estimate battery state of health (SOH) using a short random charging segment (RCS). Four types of commercial LIBs (75 cells), cycled under different temperatures and discharging rates, are employed to validate the methods. Trained on a nominal cycling condition, our models can achieve high-precision SOH estimation under other different conditions. We prove that an RCS with a 10mV voltage window can obtain an average error of less than 5%, and the error plunges as the voltage window increases.

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A short random charging segment enables battery health evaluation

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Two features with high correlations to battery capacity are extracted

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Three typical machine learning methods are compared for battery health estimation

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The method is verified by four types of batteries cycled under different conditions

Data-driven prediction of battery failure for electric vehicles

Despite great progress in battery safety modeling, accurately predicting the evolution of multiphysics systems is extremely challenging. The question on how to ensure safety of billions of automotive batteries during their lifetime remains unanswered. In this study, we overcome the challenge by developing machine learning techniques based on the recorded data that are uploaded to the cloud. Using charging voltage and temperature curves from early cycles that are yet to exhibit symptoms of battery failure, we apply data-driven models to both predict and classify the sample data by health condition based on the observational, empirical, physical, and statistical understanding of the multiscale systems. The best well-integrated machine learning models achieve a verified classification accuracy of 96.3% (exhibiting an increase of 20.4% from initial model) and an average misclassification test error of 7.7%. Our findings highlight the need for cloud-based artificial intelligence technology tailored to robustly and accurately predict battery failure in real-world applications.

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A well-integrated machine learning technique is applied to failure prediction

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A cloud-based closed-loop framework is established for real-world EV applications

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Cloud-based AI solution is based on an in-depth analysis of the field data

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Both electrochemical and statistical feature engineering are established

Overview of Machine Learning Methods for Lithium-Ion Battery Remaining Useful Lifetime Prediction

Lithium-ion batteries play an indispensable role, from portable electronic devices to electric vehicles and home storage systems. Even though they are characterized by superior performance than most other storage technologies, their lifetime is not unlimited and has to be predicted to ensure the economic viability of the battery application. Furthermore, to ensure the optimal battery system operation, the remaining useful lifetime (RUL) prediction has become an essential feature of modern battery management systems (BMSs). Thus, the prediction of RUL of Lithium-ion batteries has become a hot topic for both industry and academia. The purpose of this work is to review, classify, and compare different machine learning (ML)-based methods for the prediction of the RUL of Lithium-ion batteries. First, this article summarizes and classifies various Lithium-ion battery RUL estimation methods that have been proposed in recent years. Secondly, an innovative method was selected for evaluation and compared in terms of accuracy and complexity. DNN is more suitable for RUL prediction due to its strong independent learning ability and generalization ability. In addition, the challenges and prospects of BMS and RUL prediction research are also put forward. Finally, the development of various methods is summarized.

A Multi-Criteria Decision-Making Approach for Energy Storage Technology Selection Based on Demand

Energy storage technologies can reduce grid fluctuations through peak shaving and valley filling and effectively solve the problems of renewable energy storage and consumption. The application of energy storage technologies is aimed at storing energy and supplying energy when needed according to the storage requirements. The existing research focuses on ranking technologies and selecting the best technologies, while ignoring storage requirements. Here, we propose a multi-criteria decision-making (MCDM) framework for selecting a suitable technology based on certain storage requirements. Specifically, we consider nine criteria in four aspects: technological, economic, environmental, and social. The interval number, crisp number, and linguist terms can be transformed into a probabilistic dual hesitant fuzzy set (PDHFS) through the transformation and fusion method we proposed, and a suitable technology can be selected through distance measurements. Subsequently, the proposed method is applied in a representative case study for energy storage technology selection in Shanxi Province, and a sensitivity analysis gives different scenarios for elaboration. The results show that the optimal selection of energy storage technology is different under different storage requirement scenarios. The decision-making model presented herein is considered to be versatile and adjustable, and thus, it can help decision makers to select a suitable energy storage technology based on the requirements of any given use case.

Artificial Intelligence Applied to Battery Research: Hype or Reality?

This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily understandable, review of general interest to the battery community. It addresses the concepts, approaches, tools, outcomes, and challenges of using AI/ML as an accelerator for the design and optimization of the next generation of batteries—a current hot topic. It intends to create both accessibility of these tools to the chemistry and electrochemical energy sciences communities and completeness in terms of the different battery R&D aspects covered.

Review of Energy Storage and Energy Management System Control Strategies in Microgrids

A microgrid (MG) is a discrete energy system consisting of an interconnection of distributed energy sources and loads capable of operating in parallel with or independently from the main power grid. The microgrid concept integrated with renewable energy generation and energy storage systems has gained significant interest recently, triggered by increasing demand for clean, efficient, secure, reliable and sustainable heat and electricity. However, the concept of efficient integration of energy storage systems faces many challenges (e.g., charging, discharging, safety, size, cost, reliability and overall management). Additionally, proper implementation and justification of these technologies in MGs cannot be done without energy management systems, which control various aspects of power management and operation of energy storage systems in microgrids. This review discusses different energy storage technologies that can have high penetration and integration in microgrids. Moreover, their working operations and characteristics are discussed. An overview of the controls of energy management systems for microgrids with distributed energy storage systems is also included in the scope of this review.