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Accurately evaluating the technological improvement effects of wind turbines is crucial for wind farm operators. To this end, this paper proposes an innovative approach that employs a wind power regression model which leverages external environmental information to predict the output power of wind turbines. The effectiveness of technological improvements can be evaluated by comparing the predicted output power with the measured output power. In this paper, a model called stacked LSTM networks with attention mechanisms is designed. In the proposed model, the stacked LSTM networks are used to enhance the nonlinear fitting ability and capture deeper features of the input sequence. Furthermore, temporal attention mechanisms are employed to make the model focus on important time-series information of the data. In addition, a hierarchical attention mechanism is designed to explore the correlation among the outputs of the stacked LSTM networks and enrich the model's output information. The experiments on the data from a wind farm show that the proposed method outperforms various wind power prediction benchmarks, achieving lower RMSE, MAE, and MAPE values of 142.82, 104.2, and 4.85%, respectively. [ABSTRACT FROM AUTHOR]

Transition metal-based cocatalysts play important roles in promoting the surface kinetics of hematite (α-Fe 2 O 3) photoanode. However, their performances are restricted by the shallow reconstruction process for generating highly efficient metal oxyhydroxides, where the oxygen evolution reaction (OER) occurs. Therefore, a Brnsted base-regulated strategy is developed to promote the in situ surface reconstruction of cocatalysts on Ti-doped α-Fe 2 O 3 (Ti–Fe 2 O 3) under photoelectrochemical conditions. After deep surface reconstruction by electrochemical activation, the CoWO 4 cocatalyst decorated Ti–Fe 2 O 3 photoanode (a-CoWO 4 /Ti–Fe 2 O 3) delivers a photocurrent density of 0.88 mA cm−2 at 1.23 V RHE , which is about 3.0 times of activated Ti–Fe 2 O 3 (a-Ti-Fe 2 O 3) and 1.5 times of activated CoO x /Ti–Fe 2 O 3. Tungstate promotes the surface reconstruction of cobalt-based cocatalyst, resulting in a significant increase in bulk charge separation efficiency (η sep) and surface charge injection efficiency (η inj). Moreover, the type-II heterojunction between CoWO 4 -derived CoOOH and a-Ti-Fe 2 O 3 drives the rapid separation and transfer of photogenerated electron-hole pairs, and enhances the performance of Ti–Fe 2 O 3 photoanode. [Display omitted] • In situ reconstruction of cocatalysts on photoanode is proposed. • A Brnsted base-regulated strategy for the cocatalyst reconstruction is developed. • Tungstate promotes the in-depth reconstruction of Co-based cocatalyst. • The activated CoWO 4 /Ti–Fe 2 O 3 type-II heterojunction improves the charge separation. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries have been widely applied in energy conversion sectors, where effective future ageing prediction is crucial to guarantee their safety and performance. Due to the highly nonlinear ageing behaviours, developing a reliable method that could not only consider the knee point effect but also predict the future ageing trajectory with uncertainty quantification poses a formidable task. This paper derives a machine learning solution, based on the migrated Gaussian process regression (GPR), for predicting future battery two-stage ageing trajectory. Specifically, a base model is first offline identified from the easier collected accelerated-speed ageing data, through which the long life ageing information can be effectively learned. With this base model, a migrated mean function is then designed and coupled within the GPR framework for battery ageing predictions. Experimental data from three different batteries are applied for model validation and performance evaluation. Results indicate that the proposed solution leads to effective improvements in prediction accuracy and uncertainty quantification for both cases of training before and after the knee point. This is the first time to couple migration concept within GPR, paving the way to reduce experimental cost and predict battery future two-stage ageing trajectory with only a few (first 30%) data available. [ABSTRACT FROM AUTHOR]

Real-time electrochemical state information of lithium-ion batteries attributes to a high-fidelity estimation of state-of-charge (SOC) and state-of-health (SOH) in advanced battery management systems. However, the consumption of recyclable lithium ions, loss of the active materials, and the interior resistance increase resulted from the irreversible side reactions cause severe battery performance decay. To maintain accurate battery state estimation over time, a scheme using the reduced-order electrochemical model and the dual nonlinear filters is presented in this article for the reliable co-estimations of cell SOC and SOH. Specifically, the full-order pseudo-two-dimensional model is first simplified with Padé approximation while ensuring precision and observability. Next, the feasibility and performance of SOC estimator are revealed by accessing unmeasurable physical variables, such as the surface and bulk solid-phase concentration. To well reflect battery degradation, three key aging factors including the loss of lithium ions, loss of active materials, and resistance increment, are simultaneously identified, leading to an appreciable precision improvement of SOC estimation online particular for aged cells. Finally, extensive verification experiments are carried out over the cell's lifespan. The results demonstrate the performance of the proposed SOC/SOH co-estimation scheme. [ABSTRACT FROM AUTHOR]

The nonlinearity of lithium-ion battery voltage response has been recently gained high attention in battery characterization and health diagnosis. The multisine-based nonlinear characterization method has the potential for development as an expedient on-board technique for analyzing nonlinear responses. Despite this, it remains challenging to analyze the effect of aging degradation on LIB nonlinearity. In this study, the odd random-phase multisine method is performed on fresh and aged three-electrode experimental cells. This allowed for the separation of impedance-related linear approximation and odd or even order nonlinearity toward the full-cell voltage into their respective electrodes. The results demonstrate that, as the LIB degrades, the increase of impedance-related linear approximation estimated by the multisine-based method agrees well with the results of conventional EIS. The variation of nonlinearities is demonstrated in relation to the effect of degradation modes. The multisine-based method presents the advantage of simultaneously capturing impedance-related and nonlinearity information. This makes it become a fast diagnostic method that can be implemented in a BMS to quantify the causes of battery degradation, thereby supporting battery utilization optimization and future battery designs. • A multisine nonlinear characterization method is used to investigate LIB degradation. • A 3-electrode set-up is characterized for individual electrode degradation analysis. • Frequency domain response of individual electrodes is analyzed during cycle aging. • The nonlinearity in LIB voltage is demonstrated to provide degradation information. [ABSTRACT FROM AUTHOR]

A series of integrated equalizers based on joint buck-boost (BB) and switched-capacitor (SC) converters are proposed for balancing the voltages of series-connected battery packs. All these equalizers realize the any-cells-to-any-cells (AC2AC) equalization mode without increasing the count of MOSFETs and drivers. Corresponding operational principles are analyzed and the expressions of balancing currents are derived by analytical methods and verified by experimental waveforms. According to the comparative balancing experiments for four and six series-connected Li-ion cells, one proposed CBB-PCSC equalizer, which achieves the dual AC2AC balancing modes through the integration of both coupled buck-boost (CBB) and parallel-connected switched-capacitor (PCSC) converters, leads to the highest balancing speed and efficiency. Moreover, compared with several conventional equalizers, this CBB-PCSC topology also has the compact size and low cost, making it become a well-performing integrated topology for automotive battery voltages equalization. [ABSTRACT FROM AUTHOR]

Lithium-ion (Li-ion) batteries endure substantial energy loss and power recession under cold environments, requiring to be heated before starting normal operations in electric vehicles (EVs). Currently, many internal ac heating approaches powered by batteries have been proposed to achieve a fast heating speed, high efficiency, and good uniformity. However, due to the bulky size, high cost, and low ac heating frequency, the existing ac heating devices are generally improper for EV applications. In the light of this, a compact high-frequency heater is proposed in this article to achieve effective self-heating for Li-ion batteries. Specifically, based upon the resonant switched capacitors (RSCs), this heater is only powered by the on-board battery pack, leading to an easy implementation. Detailed analytical expressions of the heating current and efficiency are deduced and validated through various experiments. Illustrative results demonstrate that the proposed RSC heater is capable of effectively realizing battery self-heating without using external devices. Particularly, a reliable tradeoff between the fast heating speed (2.67 °C/min) and high efficiency (96.4%) is obtained by using two or more parallel-connected heaters. Due to the compact size, low price, high efficiency, and good reliability, the proposed heater can be also extended to preheat batteries of unmanned aerial vehicles, robots, phones, etc. [ABSTRACT FROM AUTHOR]

• Design of an optimization framework for battery economic-conscious charging. • Costs of both electrical energy waste and battery ageing are directly considered. • Contradictory objectives along with immeasurable states and constraints are optimized. • Charging strategies towards time, energy and economic managements are obtained. • Effects of three sensitive elements are comparatively studied via Pareto frontier. Technical challenges facing the development of battery economic charging for energy management arise from various contradictory objectives, immeasurable internal states, and hard constraints. Available solutions often resort to optimizing economic charging from the perspective of power grid, while few focus on the benefits of electric vehicle (EV) owners. This paper proposes a constrained multi-objective optimization framework to achieve economy-conscious battery charging management. Specifically, a coupled electrothermal-aging model is first applied to capture the nonlinear electrical, thermal, and aging dynamics of a lithium-ion battery with different timescales. Then an economic indicator considering the total charging cost caused by both the battery aging and electrical energy loss is formulated, based on a battery resale cost model and the Beijing peak-valley time-of-use (TOU) price. In addition, two crucial but contradictory objectives including the charging time and battery average temperature are also taken into account, along with hard constraints on the battery State of Charge (SOC), charging current, terminal voltage, and temperature. The complicated multi-objective charging problem is solved by using NSGA-II to optimize the multistage constant-current (MCC) profile. Sensitivities of the cut-off voltage, convection resistance, and ambient temperature are comparatively analysed via Pareto frontier. Numerical results of two extreme cases and several balanced charging cases demonstrate that feasible, economy-conscious battery charging management for various user demands can be achieved through suitable equilibrations among charging speed and thermal variations during charging. [ABSTRACT FROM AUTHOR]

• An integrated process is proposed to recover products and energy of oil sands products. • The economic cost and environmental impact of integrated process is evaluated. • Significant decrease of wastewater emissions was achieved for the integrated process. • An increasing of energy recovery efficiency was achieved for the integrated process. Pyrolysis process, as an important recovery technology of the heavy fossil resources, is limited to further utilization due to high energy consuming and wastewater emission especially for oil sands utility. This paper conducts an integrated process followed by the oil sands pyrolysis with an initial fractionation column of oil sands pyrolysis products based on the pyrolysis stream energy recovery unit. Three processes of separation of oil sands pyrolysis products based on integrated process and traditional ammonia spray process are proposed and compared through separation capability, economic functions, energy recovery efficiency and CO 2 emissions analysis. Compared with traditional process, significant decline of 56.22% and 48.20% in total annual cost can be obtained by the effective integration of separation technology without wastewater emission. The energy recovery efficiency is raised from 8.65% to 74.23% and the CO 2 emissions are eliminated. Furthermore, based on the using of novel integration technology, the cost will be decreased from 5.5 $/t to 3.61 $/t in the oil sands refining process consist of the pyrolysis, quenching and separation processes, including capital and operation cost. [ABSTRACT FROM AUTHOR]

Lithium-ion (Li-ion) battery charging is a crucial issue in energy management of electric vehicles. Developing suitable charging patterns, while taking into account of various contradictory objectives and constraints is a key but challenging topic in battery management. This paper develops a model based strategy that optimizes the charging patterns while considers various key parameters such as the charging speed, energy conversion efficiency as well as temperature variations. To achieve this, a battery model coupling both the electric and thermal characteristics is first introduced. Three key but conflicting objectives, including the charging time, energy loss and temperature rise especially for internal temperature, are formulated. Then, multi-objective biogeography-based optimization (M-BBO) approaches are employed to search the optimal charging patterns and to balance various objectives with different combinations. Optimization results of four M-BBO approaches are compared, and the Pareto fronts for battery charging with various dual-objectives and triple-objectives are analysed in detail. Experimental results confirm that the developed strategy can offer feasible charging patterns and achieve a desirable trade-off among charging speed, energy conversion efficiency and temperature variations. The Pareto fronts obtained by this strategy can be adopted as references to adjust charging pattern to further satisfy various requirements in different charging applications. [ABSTRACT FROM AUTHOR]

Battery temperature is a primary factor affecting the battery performance, and suitable battery temperature control in particular internal temperature control can not only guarantee battery safety but also improve its efficiency. This is however challenging as current controller designs for battery charging have no mechanisms to incorporate such information. This paper proposes a novel battery charging control strategy which applies the constrained generalized predictive control (GPC) to charge a LiFePO 4 battery based on a newly developed coupled thermoelectric model. The control target primarily aims to maintain the battery cell internal temperature within a desirable range while delivering fast charging. To achieve this, the coupled thermoelectric model is firstly introduced to capture the battery behaviours in particular SOC and internal temperature which are not directly measurable in practice. Then a controlled auto-regressive integrated moving average (CARIMA) model whose parameters are identified by the recursive least squares (RLS) algorithm is developed as an online self-tuning predictive model for a GPC controller. Then the constrained generalized predictive controller is developed to control the charging current. Experiment results confirm the effectiveness of the proposed control strategy. Further, the best region of heat dissipation rate and proper internal temperature set-points are also investigated and analysed. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are widely adopted as the power supplies for electric vehicles. A key but challenging issue is to achieve optimal battery charging, while taking into account of various constraints for safe, efficient and reliable operation. In this paper, a triple-objective function is first formulated for battery charging based on a coupled thermoelectric model. An advanced optimal charging strategy is then proposed to develop the optimal constant-current-constant-voltage (CCCV) charge current profile, which gives the best trade-off among three conflicting but important objectives for battery management. To be specific, a coupled thermoelectric battery model is first presented. Then, a specific triple-objective function consisting of three objectives, namely charging time, energy loss, and temperature rise (both the interior and surface), is proposed. Heuristic methods such as Teaching-learning-based-optimization (TLBO) and particle swarm optimization (PSO) are applied to optimize the triple-objective function, and their optimization performances are compared. The impacts of the weights for different terms in the objective function are then assessed. Experimental results show that the proposed optimal charging strategy is capable of offering desirable effective optimal charging current profiles and a proper trade-off among the conflicting objectives. Further, the proposed optimal charging strategy can be easily extended to other battery types. [ABSTRACT FROM AUTHOR]

Battery manufacturing plays a direct and pivotal role in determining battery performance, which, in turn, significantly affects the applications of battery-related energy storage systems. As a complicated process that involves chemical, mechanical and electrical operations, effective battery property predictions and reliable analysis of strongly-coupled battery manufacturing parameters or variables become the key but challenging issues for wider battery applications. In this paper, an interpretable machine learning framework that could effectively predict battery product properties and explain dynamic effects, as well as interactions of manufacturing parameters is proposed. Due to the data-driven nature, this framework can be easily adopted by engineers as no specific battery manufacturing mechanism knowledge is required. Reliable battery manufacturing dataset particularly for coating (one key stage) collected from a real battery manufacturing chain is adopted to evaluate the proposed framework. Illustrative results demonstrate that three types of battery capacities including cell capacity, gravimetric capacity, and volumetric capacity can be accurately predicted with R 2 over 0.98 at the battery early-manufacturing stage. Besides, information regarding how the variations of coating mass, thickness, and porosity affect these battery capacities is effectively identified, while interactions of these coating parameters can be also quantified. The developed framework makes the data-driven model become more interpretable and opens a promising way to quantify the interactions of battery manufacturing parameters and explain how the variations of these parameters affect final battery properties. This could assist engineers to obtain critical insights to understand the underlying complicated battery material and manufacturing behavior, further benefiting smart control of battery manufacturing. • Interpretable machine learning is designed for battery smart manufacturing. • Designed method can effectively predict three types of battery capacities. • Designed method can quantify dynamic effects and interactions of coating parameters. • Designed framework is validated on a real battery manufacturing chain. [ABSTRACT FROM AUTHOR]

Battery management systems based on electrochemical models could achieve more accurate state estimations and efficient battery controls with access to cell unmeasurable physical variables. As battery electrochemical models are governed by first-principle partial differential equation sets, model complexity and multiple parameter determination are bottlenecks for their wider applications. This paper gives a systematical review of recent advancements in electrochemical model development and parameterization. Specifically, classic pseudo-two-dimensional model and related model order reduction methodologies are first summarized and analyzed. Given that the homogenization hypothesis of the pseudo-two-dimensional model could lead to significant model mismatch under some operational conditions, enhanced models considering cell internal inhomogeneity with multi-particles, multi-scales, aging and thermal dynamics are examined. To facilitate model portability, parameter identification techniques of these models are classified, and solutions for optimizing the parameterization procedure are explored. Finally, current research gaps in the literature and remaining challenges are discussed and highlighted with some suggestions. This review will therefore inform the engineers of battery management and control engineering, whilst boosting the research, design and operation of control-oriented electrochemical models for smarter battery management at different readiness levels. • A systematic review on control-oriented battery electrochemical models is proposed. • P2D model and various order-reduction techniques are investigated. • Enhanced multi-particle, multi-scale, aging, and thermal models are explored. • Different parameterization methods for electrochemical models are summarized. • Future trends, challenges and suggestions are discussed. [ABSTRACT FROM AUTHOR]

Traditional in vitro screening assays for identifying probiotics often fail to provide a metabolomic overview of candidate strains. Thus, this work aimed to develop a non-target metabolomics-based method for screening bifidobacteria from a broad perspective. First, methods for extracting intracellular metabolites of bifidobacteria were compared. Methanol extraction with bead mill and sonication was selected as it yielded the highest metabolome signal intensity and coverage. Multivariate analyses revealed obvious differences in the intracellular metabolome between species/strains. Fourteen differential metabolites that have previously been reported to possess probiotic function were identified. Bifidobacterium animalis subsp. lactis Probio-M8 (M8) had significantly more intracellular lactose than other species/strains but showed lower cell density and viable counts in stationary phase. Adding lactose in the culture medium significantly enhanced the growth of M8 strain but not other tested bifidobacteria. This work provided a new screening method and practical data for selecting potential candidates for further validation. [Display omitted] • A non-targeted metabolomic method was first used for screening of bifidobacteria. • Intracellular metabolomes of bifidobacteria are species−/strain-specific. • Differential metabolomes hint at potentially unidentical probiotic function. • Strain-level optimization is required for enhancing growth of bifidobacterial. [ABSTRACT FROM AUTHOR]

Streptococcus thermophilus has been extensively applied in fermented milk. This study used gas chromatography-ion mobility spectroscopy to determine and evaluate the volatile metabolites in raw milk, milk fermented at 37°C, and milk fermented at 42°C. Ten discriminatory volatile metabolites were identified at different incubation temperatures: acetone, 2-heptanone, 2-pentanone, 2-hexanone, butanal, hexanal, ethyl acetate, 3-methylbutanal, 3-methylbutanoic acid, and 2-methylpropanoic acid, indicating that fermentation temperature affected the spectrum of volatiles in milk fermented by different strains of S. thermophilus. Specifically, fermentation at 37°C led to accumulation of short-chain fatty acids, whereas fermentation at 42°C enriched ketones and other flavor substances in the fermented milk, enhancing the flavor of the product. This work examined the differences between the volatile metabolites produced by different S. thermophilus strains fermented at different temperatures to evaluate the effect of temperature on the metabolic pathways. [ABSTRACT FROM AUTHOR]

Biosynthesis of an unnatural amino acid, L -Homophenylalanine, by hyperthermophilic and halophilic phenylalanine dehydrogenase were studied. The catalytic characteristics of phenylalanine dehydrogenase from Natranaerobius thermophiles (NTAaDH) in the reaction systems involving ionic liquids (ILs) were examined. [EMIM]BF 4 activated enzyme activity by 132%. Effect of several ionic liquids with different anions on enzyme activity was studied with associated conformational changes. Immobilization capacity of 0.550 mg/mg for NTAaDH was achieved. Kinetic parameters of reductive amination were calculated and compared. GO-PEI-ILs integrates beneficial characteristics of ionic liquids and polymers and enhanced the activity of NTAaDH. The mechanism of the effect of ionic liquids on GO-PEI for enzyme immobilization was investigated. • Biosynthesis of L -homophenylalanine by hyperthermophilic and halophilic phenylalanine dehydrogenase were studied. • [EMIM]BF 4 enhanced phenylalanine dehydrogenase activity in non-aqueous reaction system. • The multi-level interactions of ILs with GO-PEI and enzymes enhances the activity recovery of NTAaDH. • The enzyme carrying capacity of GO-PEI-ILs reaches 0.550 mg/mg. [ABSTRACT FROM AUTHOR]

As a soft sensor, the state-of-power (SoP) estimator reveals critical information on battery-based energy storage systems. A set of reliable 'referenced values' is the key to evaluate the precision of such soft sensors at their designing stage and could influence the overall reliability of the battery systems. However, experimentally obtaining the 'referenced SoP' is non-trivial since high-current pulse tests (> 10C) are required to charge/discharge the batteries to their cut-off conditions. The associated high-power experimental platforms could be expensive, while frequently applying large current at boundary conditions may leave potential safety issues. Aiming at these problems, this paper focuses on obtaining referenced SoP, rather than onboard SoP estimations. A novel equivalent discharging test is designed to accurately recover the voltage response of high-current pulses from a set of low-current tests, resulting in a 33% reduction of the peak discharging current. In addition, a flexible softmax neural network is further proposed to generate SoP values for the intervals between pulse tests. With these tools, reliable SoP values with errors lower than 0.5% can be readily obtained. The SoP obtained from our approach can be further utilised as a highly accurate benchmark to evaluate the accuracy of other onboard battery SoP estimators. • This paper focuses on obtaining accurate 'referenced state-of-power' in the lab. • The maximum current of our proposed testing procedure can be reduced by 33%. • The error of the generated state-of-power can be bounded by only 0.5%. • The data-driven nature makes our method suitable for general batteries. [ABSTRACT FROM AUTHOR]

The large number of parameters involved in each step of Li-ion electrode manufacturing process as well as the complex electrochemical interactions in those affect the properties of the final product. Optimization of the manufacturing process, although very challenging, is critical for reducing the production time, cost, and carbon footprint. Data-driven models offer a solution for manufacturing optimization problems and underpin future aspirations for manufacturing volumes. This study combines machine-learning approaches with the experimental data to build data-driven models for predicting final battery performance. The models capture the interdependencies between the key parameters of electrode manufacturing, its structural features, and the electrical performance characteristics of the associated Li-ion cells. The methodology here is based on a set of designed experiments conducted in a controlled environment, altering electrode coating control parameters of comma bar gap, line speed and coating ratio, obtaining the electrode structural properties of active material mass loading, thickness, and porosity, extracting the manufactured half-cell characteristics at various cycling conditions, and finally building models for interconnectivity studies and predictions. Investigating and quantifying performance predictability through a systems' view of the manufacturing process is the main novelty of this paper. Comparisons between different machine-learning models, analysis of models' performance with a limited number of inputs, analysis of robustness to measurement noise and data-size are other contributions of this study. The results suggest that, given manufacturing parameters, the coated electrode properties and cell characteristics can be predicted with about 5% and 3% errors respectively. The presented concepts are believed to link the manufacturing at lab-scale to the pilot-line scale and support smart, optimised, and clean production of electrodes for high-quality Li-ion batteries. [Display omitted] • Combining battery manufacturing experiments and machine learning models. • Predicting electrode structural properties based on manufacturing parameters. • Predicting the battery cell performance via electrode properties. • Robustness analysis of prediction models to data size and measurement noise. • Structural and electrochemical characteristics predicted with less than 5% and 3% error. [ABSTRACT FROM AUTHOR]

In real industrial electronic applications that involve batteries, the inevitable health degradation of batteries would result in both the shorter battery service life and decreased performance. In this article, an attention-based model is proposed for Li-ion battery calendar health prognostics, i.e., the capacity forecaster based on knowledge-data-driven attention (CFKDA), which will be the first work that applies attention mechanism to benefit battery calendar health monitor and management. By taking the battery empirical knowledge as the foundation of its crucial part, i.e., the knowledge-driven attention module, the CFKDA has realized a satisfactory combination of the complementary domain knowledge and data, which has improved both its theoretic strength and prognostic performance significantly. Experimental studies on practical battery calendar ageing demonstrate the superiority of CFKDA in forecasting and generalizing to unwitnessed conditions over both state-of-the-art knowledge-driven and data-driven calendar health prognostic models, implying that the introduction of domain knowledge in CFKDA has brought a significant performance improvement. [ABSTRACT FROM AUTHOR]

We evaluated the potential of whey protein hydrolysate as a lyoprotectant for maintaining the cell viability of Bifidobacterium animalis ssp. lactis Probio-M8 during freeze-drying and subsequent storage. The moisture content and water activity of the lyophilized samples treated by different concentrations of whey protein hydrolysate were ≤5.23 ± 0.33 g/100 g and ≤0.102 ± 0.003, respectively. During storage at 25°C and 30°C, whey protein hydrolysate had a stronger protective effect on B. lactis Probio-M8 than the same concentration of whey protein. Using the Excel tool GinaFit, we estimated the microbial inactivation kinetics during storage. Whey protein hydrolysate reduced cell damage caused by an increase in temperature. Whey protein hydrolysate could protect cells by increasing the osmotic pressure as a compatible solute. Whey protein hydrolysate improved cell membrane integrity and reduced the amounts of reactive oxygen species and malondialdehyde produced. The findings indicated that whey protein hydrolysate was a novel antioxidant lyoprotectant that could protect probiotics during freeze-drying and storage. [ABSTRACT FROM AUTHOR]

The inevitable battery ageing is a bottleneck that hinders the advancement of battery-based energy storage systems. Developing a feasible health assessment strategy for battery pack is important but challenging due to the joint requirements of the computational burden, modeling cost, estimation accuracy, and battery equalization. This article proposes a balancing current ratio (BCR) based solution to achieve reliable state-of-health (SoH) estimations of all series-connected cells within a pack while significantly reduce the overall reliance on cell-level battery models. Specifically, after employing BCR to describe the properties of the balancing process, the voltage-based active balancing is combined into the SoH estimator design for the first time, leading to a weighted fusion strategy to effectively estimate SoHs of all cells within a pack. Hardware-in-the-loop experiments show that even if a parameter-fixed open-circuit-voltage-resistance model is used for modeling, the typical estimation error of our proposed solution can still be bounded by only 1.5%, which is 70% lower than that of the benchmarking algorithms. Due to the model-free nature of the integrated voltage-based balancing, the robustness and flexibility of the proposed pack SoH estimation solution are also significantly improved. [ABSTRACT FROM AUTHOR]

With the advent of sustainable and clean energy, lithium-ion batteries have been widely utilised in cleaner productions such as energy storage systems and electrical vehicles, but the management of their electrode production chain has a direct and crucial impact on the battery performance and production efficiency. To achieve a cleaner production chain of battery electrode involving strongly-coupled intermediate parameters and control parameters, a reliable approach to quantify the feature importance and select the key feature variables for predicting battery intermediate products is urgently required. In this paper, a Gaussian process regression-based machine learning framework, which incorporates powerful automatic relevance determination kernels, is proposed for directly quantifying the importance of four intermediate production feature variables and analysing their influences on the prediction of battery electrode mass load. Specifically, these features include three intermediate parameters from the mixing step and a control parameter from the coating step. After deriving four different automatic relevance determination kernels, the importance of these four feature variables based on a regression modelling is comprehensively analysed. Comparative results demonstrate that the proposed automatic relevance determination kernel-based Gaussian process regression models could not only quantify the importance weights for reliable feature selections but also help to achieve satisfactory electrode mass load prediction. Due to the data-driven nature, the proposed framework can be conveniently extended to improve the analysis and control of battery electrode production, further benefitting the manufactured battery yield, efficiencies and performance to achieve cleaner battery production. Image 1 • A data-driven framework is designed for mass load prediction in battery production. • Importance of four production feature variables of interest is directly quantified. • Performance of four ARD kernels is comparatively studied via GPR model. • Effects of these sensitive features on the mass load predictions are analysed. • Designed framework can benefit the optimisation for cleaner battery production. [ABSTRACT FROM AUTHOR]

Incremental capacity analysis is a popular tool for the evaluation of state-of-health in battery management. In digital systems, the incremental capacity is generally approximated with the ratio of the capacity difference to voltage difference (Δ Q ∕ Δ V), which unavoidably amplifies measurement noises. To enhance its resilience against noises and improve the estimation accuracy, a two-dimensional filter is designed by employing historical information from both time and batch (cycle) directions inspired by batch-wise repetitiveness of the incremental capacity trajectories. Specifically, in the batch direction, a Luenberger observer is utilised to provide a batch-to-batch smoothing at the beginning of each charging cycle, while in the time direction, a bias-corrected Gaussian moving average filter is applied to smooth the incremental capacity value with respect to the voltage at every sampling time. Experimental results show that the root-mean-square-error of the proposed filter is 50% lower than the benchmark algorithms, and the noise sensitivity is significantly reduced by 93%. When using incremental capacity peaks extracted from the proposed filter for state-of-health modelling, the width of the 99% confidence interval would be narrowed by 45%. Moreover, the model-free nature of the proposed method enables its application to different batteries, paving a reliable way for effective battery health assessment. • The incremental capacity values are modelled as a function of voltage. • Bias-corrected Gaussian-moving-average filter is developed for real-time filtering. • Symmetric windows could be used for filtering without violating the causality. • Batch-wise Luenberger observer is developed for curve smoothing in each cycle. [ABSTRACT FROM AUTHOR]

Prediction of battery calendar ageing is a key but challenging issue in the development of durable electric vehicles. This paper simultaneously evaluates three mainstream types of modelling techniques for calendar ageing prediction of Lithium-ion (Li-ion) batteries. They are the pseudo two dimensional (P2D)-based electrochemical model, Arrhenius law-based semi-empirical model, and Gaussian process regression (GPR)-based data-driven model. Specifically, both the electrochemical and semi-empirical models are consciously developed or selected from the state-of-the-art modelling literature. For the data-driven model, due to the limited research in the existing publications, a machine learning-enabled GPR model is derived and applied for calendar ageing prediction. An experimental setup is developed to load the commercial Panasonic NCR18650BD batteries and to collect the experimental calendar ageing data under different storage temperature and SOC levels over 435 days. Based upon this well-rounded database, each model is well trained through using its corresponding training solution. Then the prediction performances of these models are studied and evaluated in terms of the model accuracy, generalization ability and uncertainty management. Both the challenges and future prospects of each model type are highlighted to assist the industrial and academic research communities, thus boosting the progress of designing advanced modelling techniques in battery calendar ageing prediction domain. • Accurate predictions of calendar ageing are crucial for battery real applications. • Three representative modelling techniques are evaluated based on a well-rounded database. • The accuracy, generalization ability and uncertainty management of these models are compared. • Benefits and key challenges of each model type are systematically analysed and discussed. • Inspiring future prospects are given toward the improvement of existing techniques. [ABSTRACT FROM AUTHOR]

Predicting batteries' future degradation is essential for developing durable electric vehicles. The technical challenges arise from the absence of full battery degradation model and the inevitable local aging fluctuations in the uncontrolled environments. This paper proposes a base model-oriented gradient-correction particle filter (GC-PF) to predict aging trajectories of Lithium-ion batteries. Specifically, under the framework of typical particle filter, a gradient corrector is employed for each particle, resulting in the evolution of particle could follow the direction of gradient descent. This gradient corrector is also regulated by a base model. In this way, global information suggested by the base model is fully utilized, and the algorithm's sensitivity could be reduced accordingly. Further, according to the prediction deviations of base model, weighting factors between the local observations and base model can be updated adaptively. Four different battery datasets are used to extensively verify the proposed algorithm. Quantitatively, the RMSEs of GC-PF can be limited to 1.75%, which is 44% smaller than that of the conventional particle filter. In addition, the consistency of predictions when using different size of training data is also improved by 32%. Due to the pure data-driven nature, the proposed algorithm can also be extendable to other battery types. • Design of a gradient-correction particle filter for battery aging prediction • Gradient corrector is employed to improve the tracking performance of particles. • Model-based regularization is proposed to reduce the algorithm sensitivity. • The consistency of predictions under different training data size is guaranteed. • Experimental results show that the prediction errors can be limited within 1.75%. [ABSTRACT FROM AUTHOR]

Accurate health estimation and lifetime prediction of lithium-ion batteries are crucial for durable electric vehicles. Early detection of inadequate performance facilitates timely maintenance of battery systems. This reduces operational costs and prevents accidents and malfunctions. Recent advancements in "Big Data" analytics and related statistical/computational tools raised interest in data-driven battery health estimation. Here, we will review these in view of their feasibility and cost-effectiveness in dealing with battery health in real-world applications. We categorise these methods according to their underlying models/algorithms and discuss their advantages and limitations. In the final section we focus on challenges of real-time battery health management and discuss potential next-generation techniques. We are confident that this review will inform commercial technology choices and academic research agendas alike, thus boosting progress in data-driven battery health estimation and prediction on all technology readiness levels. • Battery ageing mechanisms and the most common stress factors are discussed. • Data-driven technologies for battery SOH estimations are summarized regarding the benefits and drawbacks. • Data-driven health prediction methods including analytical models with data fitting, and machine learning methods are reviewed. • A compilation of the existing issues and challenges in this field is given. • Feasible and cost-effective solutions are suggested toward the improvement of related data-driven technologies. [ABSTRACT FROM AUTHOR]

Energy management is an enabling technique to guarantee the reliability and economy of hybrid electric systems. This paper proposes a novel machine learning-based energy management strategy for a hybrid electric bus (HEB), with an emphasized consciousness of both thermal safety and degradation of the onboard lithium-ion battery (LIB) system. Firstly, the deep deterministic policy gradient (DDPG) algorithm is combined with an expert-assistance system, for the first time, to enhance the “cold start” performance and optimize the power allocation of HEB. Secondly, in the framework of the proposed algorithm, the penalties to over-temperature and LIB degradation are embedded to improve the management quality in terms of the thermal safety enforcement and overall driving cost reduction. The proposed strategy is tested under different road missions to validate its superiority over state-of-the-art techniques in terms of training efficiency and optimization performance. [ABSTRACT FROM AUTHOR]

Solar power is an important renewable energy resource and acts as a major contributor to replacing fossil fuel generators and reducing carbon emissions. However, the intermittent power output due to the uncertain solar irradiance significantly challenges the economic integrations of solar generation within the existing power system, which calls for effective forecasting methods to improve the solar prediction accuracy. In this paper, a novel improved radial basis function neural network model is proposed and applied in forecasting the short-term solar power generation. A recent proposed meta-heuristic approach named competitive swarm optimization is adopted to train the non-linear and linear parameters of the radial basis function neural network model. The proposed model has been validated in nonlinear benchmark functions and then employed in forecasting the solar power generation of a real-world case study in the Netherlands. Numerical results demonstrate that the proposed competitive swarm optimized radial basis function neural network model could obtain higher accuracy compared to other counterparts and thus provides a useful tool for solar power forecasting. [ABSTRACT FROM AUTHOR]

The addition of Bifidobacterium to goat milk has dual effects on health, for which various inherent nutrients of goat milk are retained and live probiotics are provided. We explored the effect of Bifidobacterium animalis ssp. lactis Probio-M8 (Probio-M8) on fermentation characteristics, formation of organic acid, sensory properties, and storage characteristics of fermented goat milk (with added 4.0% sucrose). Addition of Probio-M8 decreased the fermentation time and significantly increased the content of functional organic acids, such as acetic acid, and functional long-chain unsaturated fatty acids, including linoleic acid, α-linolenic acid, and docosahexaenoic acid. Furthermore, the contents of medium-chain and short-chain fatty acids, which are related to "goaty" flavor, were significantly lower in the Probio-M8 treatment compared with the control. The number of living Probio-M8 decreased from 8.27 log cfu/mL (1.80 × 108 cfu/mL) to 7.94 log cfu/mL (0.79 × 108 cfu/mL) after 28 d of storage. Titratable acidity and pH value did not differ between the control group and experimental group (containing Probio-M8). Sensory evaluation indicated a lower goaty flavor and odor in the Probio-M8 fermented milk. Our results suggest that the addition of the probiotic Probio-M8 could improve the sensory, physicochemical, and functional properties of fermented goat milk. [Display omitted] [ABSTRACT FROM AUTHOR]

The addition of Bifidobacterium to goat milk has dual effects on health, for which various inherent nutrients of goat milk are retained and live probiotics are provided. We explored the effect of Bifidobacterium animalis ssp. lactis Probio-M8 (Probio-M8) on fermentation characteristics, formation of organic acid, sensory properties, and storage characteristics of fermented goat milk (with added 4.0% sucrose). Addition of Probio-M8 decreased the fermentation time and significantly increased the content of functional organic acids, such as acetic acid, and functional long-chain unsaturated fatty acids, including linoleic acid, α-linolenic acid, and docosahexaenoic acid. Furthermore, the contents of medium-chain and short-chain fatty acids, which are related to "goaty" flavor, were significantly lower in the Probio-M8 treatment compared with the control. The number of living Probio-M8 decreased from 8.27 log cfu/mL (1.80 × 108 cfu/mL) to 7.94 log cfu/mL (0.79 × 108 cfu/mL) after 28 d of storage. Titratable acidity and pH value did not differ between the control group and experimental group (containing Probio-M8). Sensory evaluation indicated a lower goaty flavor and odor in the Probio-M8 fermented milk. Our results suggest that the addition of the probiotic Probio-M8 could improve the sensory, physicochemical, and functional properties of fermented goat milk. [Display omitted] [ABSTRACT FROM AUTHOR]

Ionic liquid modified organic-inorganic hybrid materials were applied for the immobilization of thermophilic and halophilic phenylalanine dehydrogenase from Natranaerobius thermophiles (NTAaDH). Biosynthesis of L -homophenylalanine by asymmetric reductive amination was studied. ILs modification induced the formation of nanoflowers with different morphologies. The activity of Cu 3 (PO 4) 2 -[EMIM]BF 4 -NTAaDH was 1.92-fold of Cu 3 (PO 4) 2 -NTAaDH, which indicated activity enhancement of ionic liquid modification. The correlation between the logP value of organic solvents and the activity of immobilized enzyme showed that high logP value increased the activity of enzyme. The effect of ionic liquid on the morphology of immobilized enzyme particles was studied by SEM. Mechanism of ionic liquids modification was investigated by multi-level interactions between the enzyme and carrier, and the cage effects enhanced enzyme stability. Ionic liquids modified organic-inorganic hybrid materials have excellent biocompatibility and conductivity, and are expected to expand the application in biocatalysis. [Display omitted] • Fine-tuning the microenvironment of nanoflower by ionic liquids enhanced enzyme activity and stability. • Activity of Cu 3 (PO 4) 2 -[EMIM]BF 4 -NTAaDH was 1.92-fold of Cu 3 (PO 4) 2 -NTAaDH. • Organic solvent with high LogP value enhanced immobilized enzymes activity. • ILs modified nanoflower with biocompatibility and conductivity is expected for biocatalysis. [ABSTRACT FROM AUTHOR]

—Batteries are acknowledged as an important carrier for next-generation energy and future electrification. Capacity degradation is an inevitable process that will eventually lead to a decline in battery performance. This paper presents a battery degradation trajectory prognostics approach under storage conditions based on the data-driven method with necessary knowledge inspiration. This data-driven approach is based on Support Vector Regression (SVR), incorporating mechanisms and empirical knowledge of battery calendar aging as the kernel. The developed data-driven approach is evaluated using two real but different capacity degradation datasets: Dataset A has two storage temperatures (25 °C and 45 °C) and storage SoCs (50% and 90%), while Dataset B has the same storage temperatures but four different storage SoCs (20%, 50%, 70% and 95%). The illustrative results show that the developed SVR-based data-driven approach with the battery knowledge-motivated kernel can make accurate future capacity prognostics under different storage conditions. Due to the superiority of its flexibility and knowledge-data-motivation, the developed approach could be extended and help engineers monitor or capture the future capacity degradation trajectory of other types of batteries when their relevant data become available, which in turn will benefit the life management of battery-based energy storage applications. • Knowledge-motivated data-driven method for battery degradation is achieved. • Mechanism knowledge and empirical knowledge are completely coupled. • A long-term capacity degradation trajectory prediction method is proposed. • The methodologies are completely validated based on experiments. [ABSTRACT FROM AUTHOR]

Lithium‐ion battery manufacturing chain is extremely complex with many controllable parameters especially for the drying process. These processes affect the porous structure and properties of these electrode films and influence the final cell performance properties. However, there is limited available drying information and the dynamics are poorly understood due to the limitation of the existing metrology. There is an emerging need to develop new methodologies to understand the drying dynamics to achieve improved quality control of the electrode coatings. A comprehensive summary of the parameters and variables relevant to the wet electrode film drying process is presented, and its consequences/effects on the finished electrode/final cell properties are mapped. The development of the drying mechanism is critically discussed according to existing modeling studies. Then, the existing and potential metrology techniques, either in situ or ex situ in the drying process are reviewed. This work is intended to develop new perspectives on the application of advanced techniques to enable a more predictive approach to identify optimum lithium‐ion battery manufacturing conditions, with a focus upon the critical drying process. [ABSTRACT FROM AUTHOR]

Accurate estimations of battery state-of-charge (SOC) for energy storage systems are popular research topics in recent years. Numerous challenges remain in several aspects, especially in dealing with the conflict of high model accuracy and complex model structure with heavy computational cost. This paper proposes a compact and optimized SOC estimation model, integrating a fast input selection algorithm to choose important terms as input variables, followed by a simple and efficient JAYA optimization scheme to tune the key parameters of neural network functions. From the real-system experiment results, it can be seen that the estimation model errors are greatly reduced by applying optimization method, and the model performance is validated through statistical error values including root mean square error, mean absolute error, mean absolute percentage error and SOC error. The experimental results demonstrate that the SOC estimations can be greatly improved after optimization of neural network parameters under different charging and discharging process. • A novel compact and optimized radial basis function neural network is proposed. • The method is adopted for the SOC estimation of energy storage systems. • Simulation results validate the excellent performance of the proposed method. [ABSTRACT FROM AUTHOR]

Coal Chemical Industry (CCI) have more abundant resources potential on fossil fuel under the background of the exhaustion of world petroleum resource, but still need to overcome the operational problems and environmental issues. Petroleum Chemical Industry (PCI) technology, related CCI is viewed as a more mature technology, which could often be applied in CCI processes. This paper made a brief contrast of existing coal and petroleum chemical technologies, gave new insights on the application of PCI technologies in CCI, and provided a case about the recovery and separation of coal pyrolysis products using a process that has a similar structure compare with FCC fractionator. The results showed that the integration process gives a 41.27% reduction in total annual cost with total product yield almost unchanged, and achieves a total economic cost with a positive value of 15.74 × 105 $/y by saturated steam generation. The wastewater producing amount is decreased by 99.71% in Type I, and the product loss is also decreased by 99.38%. The discussed case proves the feasibility and significance of novel technology development of CCI based on the PCI technology and will lead to a wealth of research topics of the CCI field. • New sight of PCI technologies retrofit application in CCI is proposed. • Novel pyrolysis crude gas of coal separation process achieves significant cost reduction. • Wastewater generation of CCI may be greatly decreased by using PCI technologies applied. [ABSTRACT FROM AUTHOR]

[Display omitted] • Key factors influencing the severity of traffic accidents in the four stages before and after the outbreak of the Covid-19 in Illinois, USA are determined. • Over the Covid-19, adapting regulatory measures to human perception factors and changes in customary travel times and accident-prone spatial segments can improve traffic resilience. • Recommendations are proposed for policy makers to improve the resilience of the transport sector in the event of public health crises. Transportation systems are critical lifelines and vulnerable to various disruptions, including unforeseen social events such as public health crises, and have far-reaching social impacts such as economic instability. This paper aims to determine the key factors influencing the severity of traffic accidents in four different stages during the pre- and the post Covid-19 pandemic in Illinois, USA. For this purpose, a Random Forest-based model is developed, which is combined with techniques of explainable machine learning. The results reveal that during the pandemic, human perceptual factors, notably increased air pressure, humidity and temperature, play an important role in accident severity. This suggests that alleviating driver anxiety, caused by these factors, may be more effective in curbing crash severity than conventional road condition improvements. Further analysis shows that the pandemic leads notable shifts in residents' daily travel time and accident-prone spatial segments, indicating the need for increased regulatory measures. Our findings provide new insights for policy makers seeking to improve transportation resilience during disruptive events. [ABSTRACT FROM AUTHOR]

Battery modeling and state estimation are critical to the battery safety and vehicle driving range. Currently, electrochemical mechanism based models require huge computation effort for solving partial differential equations, equivalent circuit models do not consider actual battery mechanism, while machine learning models are lack of generalization ability due to their data-driven nature. All these problems bring the challenges to guarantee the model effectiveness in wide temperature and large current conditions. To estimate the battery state of charge (SOC) and state of temperature (SOT) under these conditions, an electrochemical-thermal-neural-network (ETNN) model is formulated in this paper. Specifically, a simplified single particle model and a lumped thermal model are served as the sub-models of ETNN to predict core temperature and provide approximate terminal voltage. Then a neural network is incorporated to enhance the performance of sub-models. According to the extensive experiments, ETNN model is able to accurately estimate battery voltage and core temperature under the ambient temperatures of -10–40 °C and the discharge rate of 10-C. After that, an unscented Kalman filter (UKF) is integrated with ETNN to achieve reliable co-estimation of SOC-SOT. Experimental results illustrate that proposed ETNN-UKF can rapidly eliminate initial errors and provide satisfactory co-estimation performance. • Formulation of a hybrid battery electrochemical-thermal-neural-network model. • High model accuracy under large currents and wide range of ambient temperatures. • Accurate co-estimation of SOC and SOT by integrating the unscented Kalman filter. [ABSTRACT FROM AUTHOR]

Batteries are presently pervasive in portable electronics, electrified vehicles, and renewable energy storage. These indispensable engineering applications are all safety-critical and energy efficiency-demanding such that batteries must be meticulously monitored and manipulated, where effectively estimating the internal battery states is a key enabler. The primary goal of this paper is to present a concise, understandable overview of existing methods, key issues, technical challenges, and future trends of the battery state estimation domain. More specifically, for the first time, the state of the art in State of Charge (SOC), State of Energy (SOE), State of Health (SOH), State of Power (SOP), State of Temperature (SOT), and State of Safety (SOS) estimation is all elucidated in a tutorial yet systematical way, along with existing issues exposed. In addition, from six different viewpoints, some future important research opportunities and evolving trends of this prosperous field are disclosed, in order to stimulate more technologically innovative breakthroughs in SOC/SOE/SOH/SOP/SOT/SOS estimation. • Critical battery internal states are defined and explained. • Battery state estimation methods are systematically overviewed. • Key issues, technical challenges and opportunities are elucidated. • Future research directions and trends are presented. [ABSTRACT FROM AUTHOR]

One-step solvothermal method for the synthesis of highly entropic hydroxymethoxy compounds with lamellar structures. [Display omitted] • A new high entropy metal hydroxymethylate material (FeCoNiMgCr-HM) is synthesized. • A rule for guiding the synthesis of metal hydroxymethylate materials is proposed. • The activated electrocatalyst exhibits an outstanding OER performance. Much attention has been paid to high entropy materials in the field of electrochemical water splitting. In this research, we present a synthesis of a high-entropy metal hydroxymethylate FeCoNiMgCr(OH)(OCH 3) (labeled as FeCoNiMgCr-HM) material via a one-step solvothermal method. Due to the synergistic effect of five metal cations and complete surface reconstruction induced by methoxyl group, FeCoNiMgCr-HM after electrochemical activation exhibits an outstanding performance for catalyzing lattice-oxygen-participated OER with a low overpotential of 291 mV to reach a current density of 20 mA cm−2 and a small Tafel slope of 49.1 mV dec-1. In addition, we summarize a universal ionic potential rule for guiding the synthesis of metal hydroxymethylate materials. [ABSTRACT FROM AUTHOR]

This article presents a thorough comparative study of energy management strategies (EMSs) for a parallel hybrid electric vehicle (HEV), while the battery ageing is considered. The principle of dynamic programming (DP), Pontryagin's minimum principle (PMP), and equivalent consumption minimization strategy (ECMS) considering battery ageing is elaborated. The gearshift map is obtained from the optimization results in DP to prevent frequent shifts by taking into account drivability and fuel economy, which is then applied in the PMP and ECMS. Comparison of different EMSs is conducted by means of fuel economy, battery state-of-charge charge-sustainability, and computational efficiency. Moreover, battery ageing is included in the optimization solution by utilizing a control-oriented model, aiming to fulfill one of the main cost-related design concerns in the development of HEVs. Through a unified framework, the torque split and battery degradation are simultaneously optimized in this study. Simulations are carried out for DP, PMP, and ECMS to analyze their features, wherein results indicate that DP obtains the best fuel economy compared with other methods. Additionally, the difference between DP and PMP is about 2% in terms of fuel economy. The observations from analysis results provide a good insight into the merits and demerits of each approach. • Comparative studies of different energy management strategies are performed for a parallel HEV. • The fuel economy, drivability and battery ageing are concurrently considered. • The gearshift map is extracted considering drivability and fuel economy simultaneously. • A control-oriented battery ageing model is given to consider battery ageing dynamics into the optimization solution. • The performance for these methods under China's typical city driving conditions is analyzed. [ABSTRACT FROM AUTHOR]

• Metal(oxy)hydroxides were constructed on FeCoNiAl HEA through surface reconstruction. • Al accelerates the surface reconstruction of FeCoNiAl HEA. • The obtained electrocatalyst exhibits a lattice oxygen-participated OER activity. A lattice-oxygen-participated OER electrocatalyst was obtained by in-situ constructing metal (oxy) hydroxides on FeCoNiAl high entropy alloy (HEA). The electrocatalyst exhibits a remarkable activity, requiring a low overpotential of 370 mV to achieve a current intensity of 50 mA cm−2 and performing rapid reaction kinetics with a Tafel slope of 90 mV dec−1. [ABSTRACT FROM AUTHOR]

High energy density has made Li-ion battery become a reliable energy storage technology for transport-grid applications. Safely disposing batteries that below 80% of their nominal capacity is a matter of great concern to reduce overall carbon footprint. As battery typically accounts for 40% of the total cost of an electrical vehicle, it becomes necessary to combine reutilization and recycling for extending the lifetime of retired automotive batteries and make the overall battery supply chain profitable rather than dumping them directly. From an economic, technical, and environmental standpoint, this paper provides a comprehensive overview of the present state of second-life Li-ion batteries through exploring relevant literature. Specifically, the fundamental of Li-ion battery degradation and experimental approaches are first surveyed. After examining the obstacles and methods of reusing and recycling Li-ion battery, related applications, cost issues, and business models of second-life Li-ion batteries are discussed. By offering a systematical survey of current status of recycled Li-ion battery, this review could inform commercial technology selections and academic research agendas alike, thus boosting progress in Li-ion battery second-life applications. • Second-life battery is economically benefitting and environmentally sustainable. • An elaborate discussion on the identification of battery degradation is presented. • Profitable economics of the second-life battery is discussed. • Cost analysis and business model for second-life battery application are discussed. • Challenges and potential solutions for second-life battery application are identified. [ABSTRACT FROM AUTHOR]

Accurate generation forecasting can effectively accelerate the use of renewable energy in hybrid energy systems, contributing significantly to the delivery of the net-zero emission target. Recently, neural-network-based quantile forecast models have shown superior performance on renewable energy generation forecasting, partially because they have subtly embedded quantile forecast evaluation metrics into their loss functions. However, the non-differentiability of involved metrics has rendered their metric-embedded loss functions not everywhere-derivable, resulting in inapplicability of gradient-based training approaches. Instead, they have resorted to heuristic searches for Neural Network (NN) training, bringing low training efficiency and a rigid restriction on the size of the resultant NN. In this paper, the Indicator Gradient Descent (IGD) is proposed to overcome the non-differentiability of involved metrics, and several metric-embedded loss functions are innovatively customized combining IGD, enabling NNs to be trained efficiently in a 'gradient-descent-like' manner. Moreover, the deep Bidirectional Long Short-Term Memory (BiLSTM) is adopted to capture the periodicity of renewable generation (diurnal and seasonal patterns), and the residual technique is used to improve the training efficiency of the deep BiLSTM. Finally, a Deep Quantile Forecast Network (DQFN) based on IGD and deep residual BiLSTM is developed for wind and solar power quantile forecasting. Practical experiments in four cases have verified the effectiveness and efficiency of DQFN and IGD, where DQFN has achieved the lowest average proportion deviations (all below 1.7%) and the highest skill scores. • First study on the differentiability of quantile forecast evaluation metrics. • The obstacles of applying deep learning to quantile forecast are revealed. • IGD is proposed to overcome the nondifferentiability of quantile evaluation metrics. • The IGD is GPU-compatible and time-efficient. • A deep-learning-based non-parametric quantile forecast model is developed. [ABSTRACT FROM AUTHOR]