Closed-loop optimization of fast-charging protocols for batteries with machine learning

Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis

Accurate state-of-health (SOH) estimation is critical for reliable and safe operation of lithium-ion batteries. However, reliable and stable battery SOH estimation remains challenging due to diverse battery types and operating conditions. In this paper, we propose a physics-informed neural network (PINN) for accurate and stable estimation of battery SOH. Specifically, we model the attributes that affect the battery degradation from the perspective of empirical degradation and state space equations, and utilize neural networks to capture battery degradation dynamics. A general feature extraction method is designed to extract statistical features from a short period of data before the battery is fully charged, enabling our method applicable to different battery types and charge/discharge protocols. Additionally, we generate a comprehensive dataset consisting of 55 lithium-nickel-cobalt-manganese-oxide (NCM) batteries. Combined with three other datasets from different manufacturers, we use a total of 387 batteries with 310,705 samples to validate our method. The mean absolute percentage error (MAPE) is 0.87%. Our proposed PINN has demonstrated remarkable performance in regular experiments, small sample experiments, and transfer experiments when compared to alternative neural networks. This study highlights the promise of physics-informed machine learning for battery degradation modeling and SOH estimation.

Fast-Charging Phosphorus-Based Anodes: Promises, Challenges, and Pathways for Improvement

Fast-charging batteries are highly sought after. However, the current battery industry has used carbon as the preferred anode, which can suffer from dendrite formation problems at high current density, causing failure after prolonged cycling and posing safety hazards. The phosphorus (P) anode is being considered as a promising successor to graphite due to its safe lithiation potential, low ion diffusion energy barrier, and high theoretical storage capacity. Since 2019, fast-charging P-based anodes have realized the goals of extreme fast charging (XFC), which enables a 10 min recharging time to deliver a capacity retention larger than 80%. Rechargeable battery technologies that use P-based anodes, along with high-capacity conversion-type cathodes or high-voltage insertion-type cathodes, have thus garnered substantial attention from both the academic and industry communities. In spite of this activity, there remains a rather sparse range of high-performance and fast-charging P-based cell configurations. Herein, we first systematically examine four challenges for fast-charging P-based anodes, including the volumetric variation during the cycling process, the electrode interfacial instability, the dissolution of polyphosphides, and the long-lasting P/electrolyte side reactions. Next, we summarize a range of strategies with the potential to circumvent these challenges and rationally control electrochemical reaction processes at the P anode. We also consider both binders and electrode structures. We also propose other remaining issues and corresponding strategies for the improvement and understanding of the fast-charging P anode. Finally, we review and discuss the existing full cell configurations based on P anodes and forecast the potential feasibility of recycling spent P-based full cells according to the trajectory of recent developments in batteries. We hope this review affords a fresh perspective on P science and engineering toward fast-charging energy storage devices.

Phase Change Nanocapsules Enabling Dual-Mode Thermal Management for Fast-Charging Lithium-Ion Batteries

The fast-charging performance of conventional lithium-ion batteries (LIBs) is determined by the working temperature. LIBs may fail to work under harsh conditions, especially in the low-temperature range of the local environment or in the high-temperature circumstances resulting from the release of substantial Joule heating in the short term. Constructing a thermal engineering framework for thermal regulation and maintaining the battery running at an appropriate temperature range are feasible strategies for developing temperature-tolerant, fast-charging LIBs. In this work, we prepare phase change nanocapsules as a thermal regulating layer on the cell surface. The polyurea shells of the nanocapsules are decorated with polyaniline, where the molecular vibration of polyaniline is enhanced under solar irradiation, enabling light-to-heat conversion that achieves an effective temperature increment at low temperatures. Based on the large latent heat storage capability of the n-octadecane core in the nanocapsules, the thermal regulating layer is sufficient to modulate strong heat release when operating LIBs at a high current rate, which efficiently prevents strong side reactions at high temperatures or even the occurrence of thermal runaway. This work highlights the promise of optimizing the operating temperature with a thermal regulator to ensure the safety and performance stability of fast-charging LIBs.

Ether-Based High-Voltage Lithium Metal Batteries: The Road to Commercialization

Ether-based high-voltage lithium metal batteries (HV-LMBs) are drawing growing interest due to their high compatibility with the Li metal anode. However, the commercialization of ether-based HV-LMBs still faces many challenges, including short cycle life, limited safety, and complex failure mechanisms. In this Review, we discuss recent progress achieved in ether-based electrolytes for HV-LMBs and propose a systematic design principle for the electrolyte based on three important parameters: electrochemical performance, safety, and industrial scalability. Finally, we summarize the challenges for the commercial application of ether-based HV-LMBs and suggest a roadmap for future development.

Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation

Electrochemical research often requires stringent combinations of experimental parameters that are demanding to manually locate. Recent advances in automated instrumentation and machine-learning algorithms unlock the possibility for accelerated studies of electrochemical fundamentals via high-throughput, online decision-making. Here we report an autonomous electrochemical platform that implements an adaptive, closed-loop workflow for mechanistic investigation of molecular electrochemistry. As a proof-of-concept, this platform autonomously identifies and investigates an EC mechanism, an interfacial electron transfer (E step) followed by a solution reaction (C step), for cobalt tetraphenylporphyrin exposed to a library of organohalide electrophiles. The generally applicable workflow accurately discerns the EC mechanism's presence amid negative controls and outliers, adaptively designs desired experimental conditions, and quantitatively extracts kinetic information of the C step spanning over 7 orders of magnitude, from which mechanistic insights into oxidative addition pathways are gained. This work opens opportunities for autonomous mechanistic discoveries in self-driving electrochemistry laboratories without manual intervention.

Machine learning-enabled performance prediction and optimization for iron-chromium redox flow batteries

Iron-chromium flow batteries (ICRFBs) are regarded as one of the most promising large-scale energy storage devices with broad application prospects in recent years. However, transitioning from laboratory-scale development to industrial-scale deployment can be a time-consuming process due to the multitude of complex factors that impact ICRFB stack performance. Herein, a data-driven optimization methodology applying active learning, informed by an extensive survey of the literature encompassing diverse experimental conditions, is proposed to enable exceptional precision in predicting ICRFB system performance considering both operation conditions and key materials selection. Specifically, multitask ML models are trained on experimental data with a high prediction accuracy (R2 > 0.92) to link ICRFB properties to energy efficiency, coulombic efficiency, and capacity. We also interpret the ML models based on Shapley additive explanations and extract valuable insights into the importance of descriptors. It is noted that the operation conditions (current density and cycle number) and the electrode type are the most critical descriptors affecting the voltage efficiency and coulombic efficiency while the electrode size strongly affects the capacity. Moreover, active learning is used to explore the most optimized cases considering the highest energy efficiency and capacity. The versatility and robustness of the approach are demonstrated by the successful validation between ML prediction and our experiments of energy efficiency (±0.15%) and capacity (±0.8%). This work not only affords fruitful data-driven insight into the property-performance relationship, but also unveils the explainability of critical properties on the performance of ICRFBs, which accelerates the rational design of next-generation ICRFBs.

On Accelerating Substrate Optimization Using Computational Gibbs Energy Barriers: A Numerical Consideration Utilizing a Computational Data Set

Substrate optimization is a time- and resource-consuming step in organic synthesis. Recent advances in chemo- and materials-informatics provide systematic and efficient procedures utilizing tools such as Bayesian optimization (BO). This study explores the possibility of reducing the required experiments further by utilizing computational Gibbs energy barriers. To thoroughly validate the impact of using computational Gibbs energy barriers in BO-assisted substrate optimization, this study employs a computational Gibbs energy barrier data set in the literature and performs an extensive numerical investigation virtually regarding the Gibbs energy barriers as virtual experimental results and those with systematic and random noises as virtual computational results. The present numerical investigation shows that even the computational reactivity affected by noises of as much as 20 kJ/mol helps reduce the number of required experiments.

A cross-scale framework for evaluating flexibility values of battery and fuel cell electric vehicles

Flexibility has become increasingly important considering the intermittency of variable renewable energy in low-carbon energy systems. Electrified transportation exhibits great potential to provide flexibility. This article analyzed and compared the flexibility values of battery electric vehicles and fuel cell electric vehicles for planning and operating interdependent electricity and hydrogen supply chains while considering battery degradation costs. A cross-scale framework involving both macro-level and micro-level models was proposed to compute the profits of flexible EV refueling/charging with battery degradation considered. Here we show that the flexibility reduction after considering battery degradation is quantified by at least 4.7% of the minimum system cost and enlarged under fast charging and low-temperature scenarios. Our findings imply that energy policies and relevant management technologies are crucial to shaping the comparative flexibility advantage of the two transportation electrification pathways. The proposed cross-scale methodology has broad implications for the assessment of emerging energy technologies with complex dynamics.

Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory

The past decade has witnessed the significant efforts in novel material discovery in the use of data-driven techniques, in particular, machine learning (ML). However, since it needs to consider the precursors, experimental conditions, and availability of reactants, material synthesis is generally much more complex than property and structure prediction, and very few computational predictions are experimentally realized. To solve these challenges, a universal framework that integrates high-throughput experiments, a priori knowledge of chemistry, and ML techniques such as subgroup discovery and support vector machine is proposed to guide the experimental synthesis of materials, which is capable of disclosing structure-property relationship hidden in high-throughput experiments and rapidly screening out materials with high synthesis feasibility from vast chemical space. Through application of our approach to challenging and consequential synthesis problem of 2D silver/bismuth organic-inorganic hybrid perovskites, we have increased the success rate of the synthesis feasibility by a factor of four relative to traditional approaches. This study provides a practical route for solving multidimensional chemical acceleration problems with small dataset from typical laboratory with limited experimental resources available.

Breaking Mass Transport Limitations by Iodized Polyacrylonitrile Anodes for Extremely Fast-Charging Lithium-Ion Batteries

The fast-charging capability of rechargeable batteries is greatly limited by the sluggish ion transport kinetics in anode materials. Here we develop an iodized polyacrylonitrile (I-PAN) anode that can boost the bulk/interphase lithium (Li)-ion diffusion kinetics and accelerate Li-ion desolvation process to realize high-performance fast-charging Li-ion batteries. The iodine immobilized in I-PAN framework expands ion transport channels, compresses the electric double layer, and changes the inner Helmholtz plane to form LiF/LiI-rich solid electrolyte interphase layer. The dissolved iodine ions in the electrolyte self-induced by the interfacial nucleophilic substitution of PF6 - not only promote the Li-ion desolvation process, but also reuse the plated/dead Li formed on the anode under fast-charging conditions. Consequently, the I-PAN anode exhibits a high capacity of 228.5 mAh g-1 (39 % of capacity at 0.5 A g-1 delivered in 18 seconds) and negligible capacity decay for 10000 cycles at 20 A g-1 . The I-PAN||LiNi0.8 Co0.1 Mn0.1 O2 full cell shows excellent fast-charging performance with attractive capacities and negligible capacity decay for 1000 cycles at extremely high rates of 5 C and 10 C (1 C=180 mA g-1 ). We also demonstrate high-performance fast-charging sodium-ion batteries using I-PAN anodes.

Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning

Unsorted retired batteries with varied cathode materials hinder the adoption of direct recycling due to their cathode-specific nature. The surge in retired batteries necessitates precise sorting for effective direct recycling, but challenges arise from varying operational histories, diverse manufacturers, and data privacy concerns of recycling collaborators (data owners). Here we show, from a unique dataset of 130 lithium-ion batteries spanning 5 cathode materials and 7 manufacturers, a federated machine learning approach can classify these retired batteries without relying on past operational data, safeguarding the data privacy of recycling collaborators. By utilizing the features extracted from the end-of-life charge-discharge cycle, our model exhibits 1% and 3% cathode sorting errors under homogeneous and heterogeneous battery recycling settings respectively, attributed to our innovative Wasserstein-distance voting strategy. Economically, the proposed method underscores the value of precise battery sorting for a prosperous and sustainable recycling industry. This study heralds a new paradigm of using privacy-sensitive data from diverse sources, facilitating collaborative and privacy-respecting decision-making for distributed systems.

Multiscale dynamics of charging and plating in graphite electrodes coupling operando microscopy and phase-field modelling

The phase separation dynamics in graphitic anodes significantly affects lithium plating propensity, which is the major degradation mechanism that impairs the safety and fast charge capabilities of automotive lithium-ion batteries. In this study, we present comprehensive investigation employing operando high-resolution optical microscopy combined with non-equilibrium thermodynamics implemented in a multi-dimensional (1D+1D to 3D) phase-field modeling framework to reveal the rate-dependent spatial dynamics of phase separation and plating in graphite electrodes. Here we visualize and provide mechanistic understanding of the multistage phase separation, plating, inter/intra-particle lithium exchange and plated lithium back-intercalation phenomena. A strong dependence of intra-particle lithiation heterogeneity on the particle size, shape, orientation, surface condition and C-rate at the particle level is observed, which leads to early onset of plating spatially resolved by a 3D image-based phase-field model. Moreover, we highlight the distinct relaxation processes at different state-of-charges (SOCs), wherein thermodynamically unstable graphite particles undergo a drastic intra-particle lithium redistribution and inter-particle lithium exchange at intermediate SOCs, whereas the electrode equilibrates much slower at low and high SOCs. These physics-based insights into the distinct SOC-dependent relaxation efficiency provide new perspective towards developing advanced fast charge protocols to suppress plating and shorten the constant voltage regime.

Insights into the solvation chemistry in liquid electrolytes for lithium-based rechargeable batteries

Lithium-based rechargeable batteries have dominated the energy storage field and attracted considerable research interest due to their excellent electrochemical performance. As indispensable and ubiquitous components, electrolytes play a pivotal role in not only transporting lithium ions, but also expanding the electrochemical stable potential window, suppressing the side reactions, and manipulating the redox mechanism, all of which are closely associated with the behavior of solvation chemistry in electrolytes. Thus, comprehensively understanding the solvation chemistry in electrolytes is of significant importance. Here we critically reviewed the development of electrolytes in various lithium-based rechargeable batteries including lithium-metal batteries (LMBs), nonaqueous lithium-ion batteries (LIBs), lithium-sulfur batteries (LSBs), lithium-oxygen batteries (LOBs), and aqueous lithium-ion batteries (ALIBs), and emphasized the effects of interactions between cations, anions, and solvents on solvation chemistry, and functions of solvation chemistry in different types of electrolytes (strong solvating electrolytes, moderate solvating electrolytes, and weak solvating electrolytes) on the electrochemical performance and redox mechanism in the abovementioned rechargeable batteries. Specifically, the significant effects of solvation chemistry on the stability of electrode-electrolyte interphases, suppression of lithium dendrites in LMBs, inhibition of the co-intercalation of solvents in LIBs, improvement of anodic stability at high cut-off voltages in LMBs, LIBs and ALIBs, regulation of redox pathways in LSBs and LOBs, and inhibition of hydrogen/oxygen evolution reactions in LOBs are thoroughly summarized. Finally, the review concludes with a prospective outlook, where practical issues of electrolytes, advanced in situ/operando techniques to illustrate the mechanism of solvation chemistry, and advanced theoretical calculation and simulation techniques such as "material knowledge informed machine learning" and "artificial intelligence (AI) + big data" driven strategies for high-performance electrolytes have been proposed.

Understanding capacity fade in organic redox-flow batteries by combining spectroscopy with statistical inference techniques

Organic redox-active molecules are attractive as redox-flow battery (RFB) reactants because of their low anticipated costs and widely tunable properties. Unfortunately, many lab-scale flow cells experience rapid material degradation (from chemical and electrochemical decay mechanisms) and capacity fade during cycling (>0.1%/day) hindering their commercial deployment. In this work, we combine ultraviolet-visible spectrophotometry and statistical inference techniques to elucidate the Michael attack decay mechanism for 4,5-dihydroxy-1,3-benzenedisulfonic acid (BQDS), a once-promising positive electrolyte reactant for aqueous organic redox-flow batteries. We use Bayesian inference and multivariate curve resolution on the spectroscopic data to derive uncertainty-quantified reaction orders and rates for Michael attack, estimate the spectra of intermediate species and establish a quantitative connection between molecular decay and capacity fade. Our work illustrates the promise of using statistical inference to elucidate chemical and electrochemical mechanisms of capacity fade in organic redox-flow battery together with uncertainty quantification, in flow cell-based electrochemical systems.

Extreme fast charging of commercial Li-ion batteries via combined thermal switching and self-heating approaches

The mass adoption of electric vehicles is hindered by the inadequate extreme fast charging (XFC) performance (i.e., less than 15 min charging time to reach 80% state of charge) of commercial high-specific-energy (i.e., >200 Wh/kg) lithium-ion batteries (LIBs). Here, to enable the XFC of commercial LIBs, we propose the regulation of the battery's self-generated heat via active thermal switching. We demonstrate that retaining the heat during XFC with the switch OFF boosts the cell's kinetics while dissipating the heat after XFC with the switch ON reduces detrimental reactions in the battery. Without modifying cell materials or structures, the proposed XFC approach enables reliable battery operation by applying <15 min of charge and 1 h of discharge. These results are almost identical regarding operativity for the same battery type tested applying a 1 h of charge and 1 h of discharge, thus, meeting the XFC targets set by the United States Department of Energy. Finally, we also demonstrate the feasibility of integrating the XFC approach in a commercial battery thermal management system.

A battery dataset for electric vertical takeoff and landing aircraft

Electric vertical takeoff and landing aircraft have a unique duty cycle characterized by high discharge currents at the beginning and end of the mission (corresponding to takeoff and landing of the aircraft) and a moderate power requirement between them with no rest periods during the mission. Here, we generated a dataset of battery duty profiles for an electric vertical takeoff and landing aircraft using a cell typical for that application. The dataset features 22 cells, comprising a total of 21,392 charge and discharge cycles. 3 of the cells use the baseline cycle while each of the other cells vary either charge current, discharge power, discharge duration, ambient cooling conditions, or end of charge voltage. While it was designed to mimic the expected duty cycle of an electric aircraft, this dataset is relevant for training machine learning models on battery life, fitting physical or empirical models for battery performance and/or degradation, and countless other applications.

Machine learning integrated photocatalysis: progress and challenges

Discovering efficient photocatalysts has long been the goal of photocatalysis, which has traditionally been driven by serendipitous or try-and-error strategies. Recent developments in photocatalysis integrated with machine learning techniques promise to accelerate the discovery of photocatalysts, but are also facing significant challenges. In this review, advances in machine learning integrated photocatalysis are first presented from the perspective of three main photocatalytic processes: light harvesting, charge generation and separation, and surface redox reactions. Next, progress in using machine learning to understand complex photoactivity-structure relationships and identify the factors governing activity follows. A future photocatalysis paradigm is then provided with the integration of artificial intelligence, robots and automation. Lastly, we discuss the current challenges in machine learning integrated photocatalysis. This review aims to provide a systematic overview and guidelines to the broad scientific community interested in photocatalysis and artificial intelligence for solar fuel synthesis.

Multi-Fractal Weibull Adaptive Model for the Remaining Useful Life Prediction of Electric Vehicle Lithium Batteries

In this paper, an adaptive remaining useful life prediction model is proposed for electric vehicle lithium batteries. Capacity degradation of the electric car lithium batteries is modeled by the multi-fractal Weibull motion. The varying degree of long-range dependence and the 1/f characteristics in the frequency domain are also analyzed. The age and state-dependent degradation model is derived, with the associated adaptive drift and diffusion coefficients. The adaptive mechanism considers the quantitative relations between the drift and diffusion coefficients. The unit-to-unit variability is considered a random variable. To facilitate the application, the convergence of the RUL prediction model is proved. Replacement of the lithium battery in the electric car is recommended according to the remaining useful life prediction results. The effectiveness of the proposed model is shown in the case study.

Improving the Performance of a Triboelectric Nanogenerator by Using an Asymmetric TiO2/PDMS Composite Layer

To improve the output power of the polydimethylsiloxane (PDMS)-based triboelectric nanogenerators (TENGs), we fabricated an asymmetric TiO₂/PDMS composite film in which a pure PDMS thin film was deposited as a capping layer on a TiO₂ nanoparticles (NPs)-embedded PDMS composite film. Although in the absence of the capping layer, the output power decreased when the content of TiO₂ NPs exceeded a certain value, the asymmetric TiO₂/PDMS composite films showed that the output power increased with increasing content. The maximum output power density was approximately 0.28 W/m² at a TiO₂ content of 20 vol.%. The capping layer could be responsible not only for maintaining the high dielectric constant of the composite film but also for suppressing interfacial recombination. To further improve the output power, we applied a corona discharge treatment to the asymmetric film and measured the output power at a measurement frequency of 5 Hz. The maximum output power density was approximately 78 W/m². The idea of the asymmetric geometry of the composite film should be applicable to various combinations of materials for TENGs.

Towards interactional management for power batteries of electric vehicles

With the ever-growing digitalization and mobility of electric transportation, lithium-ion batteries are facing performance and safety issues with the appearance of new materials and the advance of manufacturing techniques. This paper presents a systematic review of burgeoning multi-scale modelling and design for battery efficiency and safety management. The rise of cloud computing provides a tactical solution on how to efficiently achieve the interactional management and control of power batteries based on the battery system and traffic big data. The potential of selecting adaptive strategies in emerging digital management is covered systematically from principles and modelling, to machine learning. Specifically, multi-scale optimization is expounded in terms of materials, structures, manufacturing and grouping. The progress on modelling, state estimation and management methods is summarized and discussed in detail. Moreover, this review demonstrates the innovative progress of machine learning based data analysis in battery research so far, laying the foundation for future cloud and digital battery management to develop reliable onboard applications.

Patterning Wettability for Open-Surface Fluidic Manipulation: Fundamentals and Applications

Effective manipulation of liquids on open surfaces without external energy input is indispensable for the advancement of point-of-care diagnostic devices. Open-surface microfluidics has the potential to benefit health care, especially in the developing world. This review highlights the prospects for harnessing capillary forces on surface-microfluidic platforms, chiefly by inducing smooth gradients or sharp steps of wettability on substrates, to elicit passive liquid transport and higher-order fluidic manipulations without off-the-chip energy sources. A broad spectrum of the recent progress in the emerging field of passive surface microfluidics is highlighted, and its promise for developing facile, low-cost, easy-to-operate microfluidic devices is discussed in light of recent applications, not only in the domain of biomedical microfluidics but also in the general areas of energy and water conservation.

Revealing An Intercalation-Conversion-Heterogeneity Hybrid Lithium-Ion Storage Mechanism in Transition Metal Nitrides Electrodes with Jointly Fast Charging Capability and High Energy Output

The performance of electrode materials depends intensively on the lithium (Li)-ion storage mechanisms correlating ultimately with the Coulombic efficiency, reversible capacity, and morphology variation of electrode material upon cycling. Transition metal nitrides anode materials have exhibited high-energy density and superior rate capability; however, the intrinsic mechanism is largely unexplored and still unclear. Here, a typical 3D porous Fe₂ N micro-coral anode is prepared and, an intercalation-conversion-heterogeneity hybrid Li-ion storage mechanism that is beyond the conventional intercalation or conversion reaction is revealed through various characterization techniques and thermodynamic analysis. Interestingly, using advanced in situ magnetometry, the ratio (ca. 24.4%) of the part where conversion reaction occurs to the entire Fe₂ N can further be quantified. By rationally constructing a Li-ion capacitor comprising 3D porous Fe₂ N micro-corals anode and commercial AC cathode, the hybrid full device delivers a high energy-density (157 Wh kg^-1 ) and high power-density (20 000 W kg^-1 ), as well as outstanding cycling stability (93.5% capacitance retention after 5000 cycles). This research provides an original and insightful method to confirm the reaction mechanism of material related to transition metals and a fundamental basis for emerging fast charging electrode materials to be efficiently explored for a next-generation battery.

Machine learning for a sustainable energy future

Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it demands advances - at the materials, devices and systems levels - for the efficient harvesting, storage, conversion and management of renewable energy. Energy researchers have begun to incorporate machine learning (ML) techniques to accelerate these advances. In this Perspective, we highlight recent advances in ML-driven energy research, outline current and future challenges, and describe what is required to make the best use of ML techniques. We introduce a set of key performance indicators with which to compare the benefits of different ML-accelerated workflows for energy research. We discuss and evaluate the latest advances in applying ML to the development of energy harvesting (photovoltaics), storage (batteries), conversion (electrocatalysis) and management (smart grids). Finally, we offer an overview of potential research areas in the energy field that stand to benefit further from the application of ML.

Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learning coupling

Developing high-energy and efficient battery technologies is a crucial aspect of advancing the electrification of transportation and aviation. However, battery innovations can take years to deliver. In the case of non-aqueous battery electrolyte solutions, the many design variables in selecting multiple solvents, salts and their relative ratios make electrolyte optimization time-consuming and laborious. To overcome these issues, we propose in this work an experimental design that couples robotics (a custom-built automated experiment named "Clio") to machine-learning (a Bayesian optimization-based experiment planner named "Dragonfly"). An autonomous optimization of the electrolyte conductivity over a single-salt and ternary solvent design space identifies six fast-charging non-aqueous electrolyte solutions in two work-days and forty-two experiments. This result represents a six-fold time acceleration compared to a random search performed by the same automated experiment. To validate the practical use of these electrolytes, we tested them in a 220 mAh graphite∣∣LiNi_0.5Mn_0.3Co_0.2O₂ pouch cell configuration. All the pouch cells containing the robot-developed electrolytes demonstrate improved fast-charging capability against a baseline experiment that uses a non-aqueous electrolyte solution selected a priori from the design space.

Impedance-based forecasting of lithium-ion battery performance amid uneven usage

Accurate forecasting of lithium-ion battery performance is essential for easing consumer concerns about the safety and reliability of electric vehicles. Most research on battery health prognostics focuses on the research and development setting where cells are subjected to the same usage patterns. However, in practical operation, there is great variability in use across cells and cycles, thus making forecasting challenging. To address this challenge, here we propose a combination of electrochemical impedance spectroscopy measurements with probabilistic machine learning methods. Making use of a dataset of 88 commercial lithium-ion coin cells generated via multistage charging and discharging (with currents randomly changed between cycles), we show that future discharge capacities can be predicted with calibrated uncertainties, given the future cycling protocol and a single electrochemical impedance spectroscopy measurement made immediately before charging, and without any knowledge of usage history. The results are robust to cell manufacturer, the distribution of cycling protocols, and temperature. The research outcome also suggests that battery health is better quantified by a multidimensional vector rather than a scalar state of health.

Estimation of Online State of Charge and State of Health Based on Neural Network Model Banks Using Lithium Batteries

Lithium batteries are secondary batteries used as power sources in various applications, such as electric vehicles, portable devices, and energy storage devices. However, because explosions frequently occur during their operation, improving battery safety by developing battery management systems with excellent reliability and efficiency has become a recent research focus. The performance of the battery management system varies depending on the estimated accuracy of the state of charge (SOC) and state of health (SOH). Therefore, we propose a SOH and SOC estimation method for lithium–ion batteries in this study. The proposed method includes four neural network models—one is used to estimate the SOH, and the other three are configured as normal, caution, and fault neural network model banks for estimating the SOC. The experimental results demonstrate that the proposed method using the long short-term memory model outperforms its counterparts.

Chemomechanics of Rechargeable Batteries: Status, Theories, and Perspectives

Chemomechanics is an old subject, yet its importance has been revived in rechargeable batteries where the mechanical energy and damage associated with redox reactions can significantly affect both the thermodynamics and rates of key electrochemical processes. Thanks to the push for clean energy and advances in characterization capabilities, significant research efforts in the last two decades have brought about a leap forward in understanding the intricate chemomechanical interactions regulating battery performance. Going forward, it is necessary to consolidate scattered ideas in the literature into a structured framework for future efforts across multidisciplinary fields. This review sets out to distill and structure what the authors consider to be significant recent developments on the study of chemomechanics of rechargeable batteries in a concise and accessible format to the audiences of different backgrounds in electrochemistry, materials, and mechanics. Importantly, we review the significance of chemomechanics in the context of battery performance, as well as its mechanistic understanding by combining electrochemical, materials, and mechanical perspectives. We discuss the coupling between the elements of electrochemistry and mechanics, key experimental and modeling tools from the small to large scales, and design considerations. Lastly, we provide our perspective on ongoing challenges and opportunities ranging from quantifying mechanical degradation in batteries to manufacturing battery materials and developing cyclic protocols to improve the mechanical resilience.

A generic battery-cycling optimization framework with learned sampling and early stopping strategies

Battery optimization is challenging due to the huge cost and time required to evaluate different configurations in experiments or simulations. Optimizing the cycling performance is especially costly since battery cycling is extremely time consuming. Here, we introduce an optimization framework building on recent advances in machine learning, which optimizes battery parameters efficiently to significantly reduce the total cycling time. It consists of a pruner and a sampler. The pruner, using the Asynchronous Successive Halving Algorithm and Hyperband, stops unpromising cycling batteries to save the budget for further exploration. The sampler, using Tree of Parzen Estimators, predicts the next promising configurations based on query history. The framework can deal with categorical, discrete, and continuous parameters and can run in an asynchronously parallel way to allow multiple simultaneous cycling cells. We demonstrated the performance by a parameter-fitting problem for calendar aging. Our framework can foster both simulations and experiments in the battery field.

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A battery-parameter optimization framework is proposed with a pruner and a sampler

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The framework can optimize categorical, discrete, and continuous variables

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An early-stopping strategy is introduced to reduce the high cycling cost

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Parameter fitting problems are investigated to demonstrate fast optimization

There are many parameters to optimize for a battery, in both simulations and experiments, from design to manufacturing. It is time consuming and costly to evaluate the lifetime performance of batteries since it takes a long period to cycle them. We introduce a generic framework leveraging machine-learning algorithms. The framework is designed to optimize battery parameters to enhance cycling performance in a systematic and efficient way, which allows parallel cyclers, stops unpromising cycles, and automatically yields new configurations of parameters. The framework could reduce the average cycling time per battery from years to months/weeks for cycling experiments or from weeks to days/hours for cycling computations. This method is flexible to scale up for many applications, from fundamental research to industrial development in batteries and other similar fields.

Theoretical and Experimental Analysis of a New Intelligent Charging Controller for Off-Board Electric Vehicles Using PV Standalone System Represented by a Small-Scale Lithium-Ion Battery

Electric vehicles are rapidly infiltrating the power grid worldwide, initiating an immediate need for a smart charging technique to maintain the stability and robustness of the charging process despite the generation type. Renewable energy sources (RESs), especially photovoltaic (PV), are becoming the essential source for electric vehicle charging points. The stochastic behavior of the PV output power affects the power conversion for regulating the battery charger voltage levels, which influences the battery to overheat and degrade. This study presents a PV standalone smart charging process for off-board plug-in electric vehicles, represented by a small-scale lithium-ion battery based on the multistage charging currents (MSCC) protocol. The charger comprises a DC–DC buck converter controlled by an artificial neural network predictive controller (NNPC), trained and supported by the long short-term memory recurrent neural network (LSTM). The LSTM network model was utilized in the offline forecasting of the PV output power, which was fed to the NNPC as the training data. Additionally, it was used as an alarm flag for any possible PV output shortage during the charging process in the long- and short-term prediction to be supported by any other electricity source. The NNPC–LSTM controller was compared with the fuzzy logic and the conventional PID controllers while varying the input voltage and implementing the MSCC protocol. The proposed charging controller perfectly ensured that the minimum battery terminal voltage ripple and charging current ripple reached 1 mV and 1 mA, respectively, with a very high-speed response of 1 ms in reaching the predetermined charging current stages. The present simulated and experimental results are in good agreement with the previous related work in the literature survey.

The Dual Role of Artificial Intelligence in Developing Smart Cities

Defining smart city pillars, and their nature and essence, continues to be debated in the scientific literature. The vast amount of information collected by electronic devices, often regarded merely as a means of rationalizing the use of resources and improving efficiency, could also be considered as a pillar. Information by itself cannot be deciphered or understood without analysis performed by algorithms based on Artificial Intelligence. Such analysis extracts new forms of knowledge in the shape of correlations and patterns used to support the decision-making processes associated with governance and, ultimately, to define new policies. Alongside information, energy plays a crucial role in smart cities as many activities that lead to growth in the economy and employment depend on this pillar. As a result, it is crucial to highlight the link between energy and the algorithms able to plan and forecast the energy consumption of smart cities. The result of this paper consists in the highlighting of how AI and information together can be legitimately considered foundational pillars of smart cities only when their real impact, or value, has been assessed. Furthermore, Artificial Intelligence can be deployed to support smart grids, electric vehicles, and smart buildings by providing techniques and methods to enhance their innovative value and measured efficiency.

Applying Classical, Ab Initio, and Machine-Learning Molecular Dynamics Simulations to the Liquid Electrolyte for Rechargeable Batteries

Rechargeable batteries have become indispensable implements in our daily life and are considered a promising technology to construct sustainable energy systems in the future. The liquid electrolyte is one of the most important parts of a battery and is extremely critical in stabilizing the electrode-electrolyte interfaces and constructing safe and long-life-span batteries. Tremendous efforts have been devoted to developing new electrolyte solvents, salts, additives, and recipes, where molecular dynamics (MD) simulations play an increasingly important role in exploring electrolyte structures, physicochemical properties such as ionic conductivity, and interfacial reaction mechanisms. This review affords an overview of applying MD simulations in the study of liquid electrolytes for rechargeable batteries. First, the fundamentals and recent theoretical progress in three-class MD simulations are summarized, including classical, ab initio, and machine-learning MD simulations (section 2). Next, the application of MD simulations to the exploration of liquid electrolytes, including probing bulk and interfacial structures (section 3), deriving macroscopic properties such as ionic conductivity and dielectric constant of electrolytes (section 4), and revealing the electrode-electrolyte interfacial reaction mechanisms (section 5), are sequentially presented. Finally, a general conclusion and an insightful perspective on current challenges and future directions in applying MD simulations to liquid electrolytes are provided. Machine-learning technologies are highlighted to figure out these challenging issues facing MD simulations and electrolyte research and promote the rational design of advanced electrolytes for next-generation rechargeable batteries.

Theory-guided experimental design in battery materials research

A reliable energy storage ecosystem is imperative for a renewable energy future, and continued research is needed to develop promising rechargeable battery chemistries. To this end, better theoretical and experimental understanding of electrochemical mechanisms and structure-property relationships will allow us to accelerate the development of safer batteries with higher energy densities and longer lifetimes. This Review discusses the interplay between theory and experiment in battery materials research, enabling us to not only uncover hitherto unknown mechanisms but also rationally design more promising electrode and electrolyte materials. We examine specific case studies of theory-guided experimental design in lithium-ion, lithium-metal, sodium-metal, and all-solid-state batteries. We also offer insights into how this framework can be extended to multivalent batteries. To close the loop, we outline recent efforts in coupling machine learning with high-throughput computations and experiments. Last, recommendations for effective collaboration between theorists and experimentalists are provided.

A Digital Twin-Driven Life Prediction Method of Lithium-Ion Batteries Based on Adaptive Model Evolution

Accurate life prediction and reliability evaluation of lithium-ion batteries are of great significance for predictive maintenance. In the whole life cycle of a battery, the accurate description of the dynamic and stochastic characteristics of life has always been a key problem. In this paper, the concept of the digital twin is introduced, and a digital twin for reliability based on remaining useful cycle life prediction is proposed for lithium-ion batteries. The capacity degradation model, stochastic degradation model, life prediction, and reliability evaluation model are established to describe the randomness of battery degradation and the dispersion of the life of multiple cells. Based on the Bayesian algorithm, an adaptive evolution method for the model of the digital twin is proposed to improve prediction accuracy, followed by experimental verification. Finally, the life prediction, reliability evaluation, and predictive maintenance of the battery based on the digital twin are implemented. The results show the digital twin for reliability has good accuracy in the whole life cycle. The error can be controlled at about 5% with the adaptive evolution algorithm. For battery L1 and L6 in this case, predictive maintenance costs are expected to decrease by 62.0% and 52.5%, respectively.

Machine learning for flow batteries: opportunities and challenges

With increased computational ability of modern computers, the rapid development of mathematical algorithms and the continuous establishment of material databases, artificial intelligence (AI) has shown tremendous potential in chemistry. Machine learning (ML), as one of the most important branches of AI, plays an important role in accelerating the discovery and design of key materials for flow batteries (FBs), and the optimization of FB systems. In this perspective, we first provide a fundamental understanding of the workflow of ML in FBs. Moreover, recent progress on applications of the state-of-art ML in both organic FBs and vanadium FBs are discussed. Finally, the challenges and future directions of ML research in FBs are proposed.

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 capacity estimation of commercial lithium-ion batteries from voltage relaxation

Accurate capacity estimation is crucial for the reliable and safe operation of lithium-ion batteries. In particular, exploiting the relaxation voltage curve features could enable battery capacity estimation without additional cycling information. Here, we report the study of three datasets comprising 130 commercial lithium-ion cells cycled under various conditions to evaluate the capacity estimation approach. One dataset is collected for model building from batteries with LiNi_0.86Co_0.11Al_0.03O₂-based positive electrodes. The other two datasets, used for validation, are obtained from batteries with LiNi_0.83Co_0.11Mn_0.07O₂-based positive electrodes and batteries with the blend of Li(NiCoMn)O₂ - Li(NiCoAl)O₂ positive electrodes. Base models that use machine learning methods are employed to estimate the battery capacity using features derived from the relaxation voltage profiles. The best model achieves a root-mean-square error of 1.1% for the dataset used for the model building. A transfer learning model is then developed by adding a featured linear transformation to the base model. This extended model achieves a root-mean-square error of less than 1.7% on the datasets used for the model validation, indicating the successful applicability of the capacity estimation approach utilizing cell voltage relaxation.

Accurate capacity estimation is crucial for lithium-ion batteries' reliable and safe operation. Here, the authors propose an approach exploiting features from the relaxation voltage curve for battery capacity estimation without requiring other previous cycling information.

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

High-entropy nanoparticles: Synthesis-structure-property relationships and data-driven discovery

High-entropy nanoparticles have become a rapidly growing area of research in recent years. Because of their multielemental compositions and unique high-entropy mixing states (i.e., solid-solution) that can lead to tunable activity and enhanced stability, these nanoparticles have received notable attention for catalyst design and exploration. However, this strong potential is also accompanied by grand challenges originating from their vast compositional space and complex atomic structure, which hinder comprehensive exploration and fundamental understanding. Through a multidisciplinary view of synthesis, characterization, catalytic applications, high-throughput screening, and data-driven materials discovery, this review is dedicated to discussing the important progress of high-entropy nanoparticles and unveiling the critical needs for their future development for catalysis, energy, and sustainability applications.

Chemically-informed data-driven optimization (ChIDDO): leveraging physical models and Bayesian learning to accelerate chemical research

A method combining information from both experiments and physics-based models is used to improve experimental Bayesian optimization.

Bayesian optimization of electrochemical devices for electrons-to-molecules conversions: the case of pulsed CO2 electroreduction

Bayesian optimization (BO) was implemented to improve a membrane electrode assembly CO2 electroreduction device undergoing pulsed operation.

Machine learning-accelerated design and synthesis of polyelemental heterostructures

In materials discovery efforts, synthetic capabilities far outpace the ability to extract meaningful data from them. To bridge this gap, machine learning methods are necessary to reduce the search space for identifying desired materials. Here, we present a machine learning–driven, closed-loop experimental process to guide the synthesis of polyelemental nanomaterials with targeted structural properties. By leveraging data from an eight-dimensional chemical space (Au-Ag-Cu-Co-Ni-Pd-Sn-Pt) as inputs, a Bayesian optimization algorithm is used to suggest previously unidentified nanoparticle compositions that target specific interfacial motifs for synthesis, results of which are iteratively shared back with the algorithm. This feedback loop resulted in successful syntheses of 18 heterojunction nanomaterials that are too complex to discover by chemical intuition alone, including extremely chemically complex biphasic nanoparticles reported to date. Platforms like the one developed here are poised to transform materials discovery across a wide swath of applications and industries.

Challenges and Recent Advances in High Capacity Li-Rich Cathode Materials for High Energy Density Lithium-Ion Batteries

Li-rich cathode materials have attracted increasing attention because of their high reversible discharge capacity (>250 mA h g^-1 ), which originates from transition metal (TM) ion redox reactions and unconventional oxygen anion redox reactions. However, many issues need to be addressed before their practical applications, such as their low kinetic properties and inefficient voltage fading. The development of cutting-edge technologies has led to cognitive advances in theory and offer potential solutions to these problems. Herein, a recent in-depth understanding of the mechanisms and the frontier electrochemical research progress of Li-rich cathodes are reviewed. In addition, recent advances associated with various strategies to promote the performance and the development of modification methods are discussed. In particular, excluding Li-rich Mn-based (LRM) cathodes, other branches of the Li-rich cathode materials are also summarized. The consistent pursuit is to obtain energy storage devices with high capacity, reliable practicability, and absolute safety. The recent literature and ongoing efforts in this area are also described, which will create more opportunities and new ideas for the future development of Li-rich cathode materials.

Suppressing High-Current-Induced Phase Separation in Ni-Rich Layered Oxides by Electrochemically Manipulating Dynamic Lithium Distribution

Understanding the cycling rate-dependent kinetics is crucial for managing the performance of batteries in high-power applications. Although high cycling rates may induce reaction heterogeneity and affect battery lifetime and capacity utilization, such phase transformation dynamics are poorly understood and uncontrollable. In this study, synchrotron-based operando X-ray diffraction is performed to monitor the high-current-induced phase transformation kinetics of LiNi_0.6 Co_0.2 Mn_0.2 O₂ . The sluggish Li diffusion at high Li content induces different phase transformations during charging and discharging, with strong phase separation and homogeneous phase transformation during charging and discharging, respectively. Moreover, by exploiting the dependence of Li diffusivity on the Li content and electrochemically tuning the initial Li content and distribution, phase separation pathway can be redirected to solid solution kinetics at a high charging rate of 7 C. Finite element analysis further elucidates the effect of the Li-content-dependent diffusion kinetics on the phase transformation pathway. The findings suggest a new direction for optimizing fast-cycling protocols based on the intrinsic properties of the materials.