Hypoxia attenuate ionic transport in the isolated gill epithelium of Carcinus maenas

The gills are osmorespiratory organs of aquatic organisms and the prime target of environmentally induced hypoxia. We have studied the impact of severe hypoxia (0.5 mg O2/L) on the ionic transport across posterior gills of Carcinus maenas acclimated to 12 ppt seawater (DSW). The short-circuit current (Isc) across hemilamellae from gills i.e. active ion transport was studied in micro Ussing chambers. Hypoxia induced by deoxygenation of the basolateral side, and not the apical side, resulted in time-dependent inhibition of Isc and full recovery of Isc after reoxygenation. Exposure of the crabs to severe 7 h hypoxia decreased the specific activity of Na+, K+-ATPase in the gills by 36%. Full recovery of enzyme activity occurred in fasted crabs after 3 days of reoxygenation. The intensity of Western blotting bands was not different in the gills of oxygenated, hypoxic and reoxygenated crabs. The reversible, nonspecific blocker of K+ channels Cs and hypoxia inhibited over 90% of Isc which is after reoxygenation fully recovered. The specific blocker of Cl− channels NPPB [5-nitro-2-(3-phenylpropylamino) benzoic acid] blocked Isc by 68.5%. Only the rest of not inhibited Isc in aerated saline was blocked by hypoxia and recovered after reoxygenation. The activity of the antioxidant enzyme catalase was not changed during hypoxia and reoxygenation kept the high enzyme activity in the gills at the level of crabs acclimated to DSW. As a response to hypoxia the presence of 2 mM H2O2 induce an initial slight transient decrease of Isc followed by a rise and after reoxygenation fully recovered Isc. Incubation of hemilamellae with the antioxidant derivative Trolox did not affect the inhibition of Isc by hypoxia

Data-driven nonparametric Li-ion battery ageing model aiming at learning from real operation data - Part B : cycling operation

Conventional Li-ion battery ageing models, such as electrochemical, semi-empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real-world battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing. In a series of two papers, a data-driven ageing model is developed for Li-ion batteries under the Gaussian Process framework. A special emphasis is placed on illustrating the ability of the Gaussian Process model to learn from new data observations, providing more accurate and confident predictions, and extending the operating window of the model. The first paper of the series focussed on the systematic modelling and experimental verification of cell degradation through calendar ageing. Conversantly, this second paper addresses the same research challenge when the cell is electrically cycled. A specific covariance function is composed, tailored for use in a battery ageing application. Over an extensive dataset involving 124 cells tested during more than three years, different training possibilities are contemplated in order to quantify the minimal number of laboratory tests required for the design of an accurate ageing model. A model trained with only 26 tested cells achieves an overall mean-absolute-error of 1.04% in the capacity curve prediction, after being validated under a broad window of both dynamic and static cycling temperatures, Depth-of-Discharge, middle-SOC, charging and discharging C-rates

Gaussian process regression with automatic relevance determination kernel for calendar aging prediction of lithium-ion batteries

Battery calendar aging prediction is of extreme importance for developing durable electric vehicles. This paper derives machine learning-enabled calendar aging prediction for lithium-ion batteries. Specifically, the Gaussian process regression (GPR) technique is employed to capture the underlying mapping among capacity, storage temperature, and SOC. By modifying the isotropic kernel function with an automatic relevance determination (ARD) structure, high relevant input features can be effectively extracted to improve prediction accuracy and robustness. Experimental battery calendar aging data from nine storage cases are utilized for model training, validation, and comparison, which is more meaningful and practical than using the data from a single condition. Illustrative results demonstrate that the proposed GPR model with ARD Matern32 (M32) kernel outperforms other counterparts and can achieve reliable prediction results for all storage cases. Even for the partial-data training test, multi-step prediction test and accelerated aging training test, the proposed ARD-based GPR model is still capable of excavating the useful features, therefore offering good generalization ability and accurate prediction results for calendar aging under various storage conditions. This is the first known data-driven application that utilizes the GPR with ARD kernel to perform battery calendar aging prognosis

The effects of hypoxia on active ionic transport processes in the gill epithelium of hyperregulating crab, Carcinus maneas

Effects of hypoxia on the osmorespiratory functions of the posterior gills of the shore crab Carcinus maenas acclimated to 12 ppt seawater (DSW) were studied. Short-circuit current (Isc) across the hemilamella (one epithelium layer supported by cuticle) was substantially reduced under exposure to 1.6, 2.0, or 2.5 mg O2/L hypoxic saline (both sides of epithelium) and fully recovered after reoxygenation. Isc was reduced equally in the epithelium exposed to 1.6 mg O2/L on both sides and when the apical side was oxygenated and the basolateral side solely exposed to hypoxia. Under 1.6 mg O2/L, at the level of maximum inhibition of Isc, conductance was decreased from 40.0 mS cm−2 to 34.7 mS cm−2 and fully recovered after reoxygenation. Isc inhibition under hypoxia and reduced 86Rb+ (K+) fluxes across apically located K+ channels were caused preferentially by reversible inhibition of basolaterally located and ouabain sensitive Na+,K+-ATPase mediated electrogenic transport. Reversible inhibition of Isc is discussed as decline in active transport energy supply down regulating metabolic processes and saving energy during oxygen deprivation. In response to a 4 day exposure of Carcinus to 2.0 mg O2/L, hemolymph Na+ and Cl− concentration decreased, i.e. hyperosmoregulation was weakened. Variations of the oxygen concentration level and exposure time to hypoxia lead to an increase of the surface of mitochondria per epithelium area and might in part compensate for the decrease in oxygen availability under hypoxic conditions

Data-driven nonparametric Li-ion battery ageing model aiming at learningfrom real operation data – Part A: Storage operation

Conventional Li-ion battery ageing models, such as electrochemical, semi-empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real-world battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing. In a series of two papers, a data-driven ageing model is developed for Li-ion batteries under the Gaussian Process framework. A special emphasis is placed on illustrating the ability of the Gaussian Process model to learn from new data observations, providing more accurate and confident predictions, and extending the operating window of the model. This first paper focusses on the systematic modelling and experimental verification of cell degradation through calendar ageing. A specific covariance function is composed, tailored for use in a battery ageing application. Over an extensive dataset involving 32 cells tested during more than three years, different training possibilities are contemplated in order to quantify the minimal number of laboratory tests required for the design of an accurate ageing model. A model trained with only 18 tested cells achieves an overall mean-absolute-error of 0.53% in the capacity curves prediction, after being validated under a broad window of both dynamic and static temperature and SOC storage conditions.This investigation work was financially supported by ELKARTEK (CICe2018 -Desarrollo de actividades de investigacion fundamental estrategica en almacenamiento de energia electroquimica y termica para sistemas de almacenamiento hibridos, KK-2018/00098) and EMAITEK Strategic Programs of the Basque Government. In addition, the research was undertaken as a part of ELEVATE project (EP/M009394/1) funded by the Engineering and Physical Sciences Research Council (EPSRC) and partnership with the WMG High Value Manufacturing (HVM) Catapult. Authors would like to thank the FP7 European project Batteries 2020 consortium (grant agreement No. 608936) for the valuable battery ageing data provided during the course of the project

Data-driven nonparametric Li-ion battery ageing model aiming at learningfrom real operation data - Part B: Cycling operation

Conventional Li-ion battery ageing models, such as electrochemical, semi-empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real-world battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing. In a series of two papers, a data-driven ageing model is developed for Li-ion batteries under the Gaussian Process framework. A special emphasis is placed on illustrating the ability of the Gaussian Process model to learn from new data observations, providing more accurate and confident predictions, and extending the operating window of the model. The first paper of the series focussed on the systematic modelling and experimental verification of cell degradation through calendar ageing. Conversantly, this second paper addresses the same research challenge when the cell is electrically cycled. A specific covariance function is composed, tailored for use in a battery ageing application. Over an extensive dataset involving 124 cells tested during more than three years, different training possibilities are contemplated in order to quantify the minimal number of laboratory tests required for the design of an accurate ageing model. A model trained with only 26 tested cells achieves an overall mean-absolute-error of 1.04% in the capacity curve prediction, after being validated under a broad window of both dynamic and static cycling temperatures, Depth-of-Discharge, middle-SOC, charging and discharging C-rates.This investigation work was financially supported by ELKARTEK (CICe2018 - Desarrollo de actividades de investigacion fundamental estrategica en almacenamiento de energia electroquimica y termica para sistemas de almacenamiento hibridos, KK-2018/00098) and EMAITEK Strategic Programs of the Basque Government. In addition, the research was undertaken as a part of ELEVATE project (EP/M009394/1) funded by the Engineering and Physical Sciences Research Council (EPSRC) and partnership with the WMG High Value Manufacturing (HVM) Catapult. Authors would like to thank the FP7 European project Batteries 2020 consortium (grant agreement No. 608936) for the valuable battery ageing data provided during the project

Data-driven nonparametric Li-ion battery ageing model aiming at learning from real operation data – Part A : storage operation

Conventional Li-ion battery ageing models, such as electrochemical, semi-empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real-world battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing

Estrategia inteligente basada en envejecimiento de la batería de litio-ion para la gestión energética de autobuses híbridos eléctricos.

El objetivo de este trabajo es proponer una estrategia inteligente basada en el envejecimiento de la batería de litio-ion instalada abordo de vehículo como aplicación de gestión energética. El diseño inicial de una estrategia de gestión energética (EMS) es un paso significativo para cumplir los objetivos de eficiencia en la operación a corto plazo. Sin embargo, debido al envejecimiento de la batería las condiciones iniciales de la EMS pueden variar. La nueva tendencia hacia la digitalización permite monitorizar la operación, brindando la posiblidad de mejorar el desempeño de la estrategia inicialmente propuesta en el largo plazo. Por lo tanto, se propone una metodología para actualizar la EMS con el objetivo de mejorar los costos de operación y extender a vida útil de la batería. La metodología se basa en una optimización mediante programación dinámica para parametrizar las funciones de pertenencia de un control difuso. Los resultados de simulación muestran una reducción en los costos de operación entorno al 47% junto con una extensión de la vida útil de la batería de alrededor de 2.94 %.This paper aims to propose a battery aging conscious energy management strategy. The initial design of an energy management strategy is a significant point to fulfill the efficiency goals in the short term. However, with aging, the initial conditions may vary. The new trend of digitalization allows monitoring the operation, having the possibility to improve the performance of the initially proposed strategy in the long term. Therefore, a methodology for updating the energy management strategy along the bus lifetime is intended to improve the operating costs and extend the battery lifetime. This methodology is based on a dynamic programming optimization, tuning the membership functions in a fuzzy logic control. The simulation results show a reduction of the operation costs up to 47% as long as it stands for battery (BT) lifetime extension of around 2.94%

Haemolymph constituents and osmolality as functions of moult stage, body weight, and feeding status in marron, Cherax cainii (Austin and Ryan, 2002) and yabbies, Cherax destructor (Clark, 1936)

The study investigates the change in osmolality and haemolymph constituents in marron Cherax cainii and yabbies Cherax destructor associated with moult stages, body weights and their feeding status. A total of 582 haemolymph samples from 5 moult stages (postmoult-AB, intermoult-C, and premoult stages – D0, D1, D2), two body weight classes (2–15 g and 61–75 g) and nutritional status were used for analysis of osmolality, protein, glucose, and ionic concentrations of potassium and chloride following the standard biochemical procedures. The haemolymph protein, glucose, potassium and chloride levels were highest at intermoult and early premoult stages, and lowest at postmoult in both crayfish species. Except protein, no significant differences were seen in analyzed parameters between various weight classes and two species. Haemolymph osmolality, protein and glucose were significantly higher in fed crayfish, whereas no variations in haemolymph potassium and chloride concentrations were observed between the fed and unfed crayfish. Maximum osmolality was recorded at 7–8 h after feeding in both crayfish species. The results showed that the biochemical changes in the haemolymph of marron and yabbies are related to moult stages, body weight and feeding and thus can be used as tools for determining suitable diets