A practical method for optimum seismic design of friction wall dampers

Friction control systems have been widely used as one of the efficient and cost effective solutions to control structural damage during strong earthquakes. However, the height-wise distribution of slip loads can significantly affect the seismic performance of the strengthened frames. In this study, a practical design methodology is developed for more efficient design of friction wall dampers by performing extensive nonlinear dynamic analyses on 3, 5, 10, 15, and 20-story RC frames subjected to seven spectrum-compatible design earthquakes and five different slip load distribution patterns. The results show that a uniform cumulative distribution can provide considerably higher energy dissipation capacity than the commonly used uniform slip load pattern. It is also proved that for a set of design earthquakes, there is an optimum range for slip loads that is a function of number of stories. Based on the results of this study, an empirical equation is proposed to calculate a more efficient slip load distribution of friction wall dampers for practical applications. The efficiency of the proposed method is demonstrated through several design examples

Environmental constraint of intraguild predation: Inorganic turbidity modulates omnivory in fairy shrimps

Omnivory is widespread in food webs, with an important stabilising effect. The strength of omnivorous trophic interactions may change considerably with changes in the local environment. Shallow temporary waters are often characterised by high levels of inorganic turbidity that may directly limit the food uptake of filter-feeding organisms, but there is little evidence on how it might affect omnivorous species. Anostracans are key species of temporary waters and recent evidence suggests that these organisms are omnivorous consumers of both phyto- and zooplankton. Using Branchinecta orientalis as a model species, our aim was to test how turbidity affects the feeding of an omnivorous anostracan. To do this, we used short-term feeding experiments and stable isotope analyses, with animals collected from soda pans in eastern Austria. In the feeding experiments, algae and zooplankton were offered as food either separately or in combination. The prey type treatments were crossed with turbidity levels in a factorial design. There was a pronounced decrease in the ingested algal biomass with increasing turbidity. Conversely, ingestion rates on zooplankton were less affected by turbidity. Stable isotope analyses from field material supported our experimental results by showing a positive relationship of the trophic position of anostracans and the trophic niche of the communities with turbidity. Our results show that turbidity modulates the intraguild trophic relationship between anostracans and their prey by shifting the diet of anostracans from more herbivorous in transparent to more carnivorous in turbid waters. Thus, inorganic turbidity might also have a community-shaping role in plankton communities of temporary waters through altering trophic relationships

State of Health analysis for Lithium-ion Batteries considering temperature effect

Lithium-ion batteries have become an integral component of machines and products in every field of modern life. In order to assure optimal use of the batteries, it is necessary to accurately predict their various parameters such as state-of-health (SoH), end of life (EoL) and state-of-charge (SoC). In this paper the use of the third-degree polynomial and hybrid function for SoH estimation and remaining useful life (RUL) prediction are further validated on a different dataset. Furthermore, linear interpolation is used to enlarge the dataset and achieve more accurate results. Finally, the battery state-of-health estimation in terms of temperature dependency is analyzed

Automated test equipment for battery characterization: a proposal

The fast pace of innovation in the field of lithium-ion batteries and changing battery chemistry has resulted in significant scientific research in the development of battery models. Considerable emphasis is placed on battery state of health estimation and rest of useful life predictions. Tackling these challenges requires large datasets of aged batteries currently obtained using very expensive test setups. Furthermore, as the number of manufacturers increases, such setups are necessary for the validation and comparison of competing cells. This paper presents an economical, automated battery testing system, capable of aging batteries of different sizes. Moreover, it enables users to programmatically define various testing cycles. Its capabilities and performances are proven by aging 8 Li-ion cells under different state of charge and voltage limits

Battery Remaining Useful Life Prediction Supported by Long Short-Term Memory Neural Network

The rise of renewable energy and electric vehicles has led to enormous growth and development in the field of lithium-ion batteries. Ensuring long-life and safe operation of these batteries requires accurate estimation of key parameters such as state of charge, state of health (SoH), and remaining useful life (RUL). In this paper, a long short-term memory neural network (LSTM NN) is presented for RUL prediction. Furthermore, the predictors used are discussed in detail, and a comparison between the two models is presented. The network has been trained and tested on a substantial dataset of 124 batteries, aged under various fast charging conditions, and published by the Toyota Research Institute in collaboration with MIT and Stanford. Despite their vastly different cycle lives, the proposed LSTM NN structure has performed very accurate RUL prediction for all tested cells

State of health prediction of lithium-ion batteries

The presence of lithium-ion batteries has been steadily growing in stationary and mobile applications and their development continues to play a key role in the wide spread adoption of electric vehicles. They are characterized by high energy density and long life; however, they are not impervious to aging effects. It is necessary to accurately predict this process in order to make sound technical and commercial decisions. Unfortunately, battery aging is a complex mechanism depending on several factors such as temperature, state of charge, voltage levels and current rates. Aging effect has resulted in many different model-based and data-driven methods attempting to predict the aging process under certain working conditions. In this paper, two functions are considered to model the battery aging behavior. Their coefficients are calculated following the leastsquares method, using data collected under controlled conditions. Additionally, it is shown that one of the two functions allows one to forecast the aging behavior. Finally, the prediction capability of the aging trend of two other batteries being discharged at different currents is analyzed

An Innovative Model-Based Algorithm for Power Control Strategy of Photovoltaic Panels

The rise of renewable energy has expanded the impact of the photovoltaic (PV) systems in the grid, and their presence is expected to grow in the next years. Crucial challenges must be faced in the new operation control and planning by also forcing photovoltaic power to adhere to primary regulation. To achieve this task, it is necessary for PV plants to emulate the inertial response of conventional generators by adding the feature to operate outside the maximum power point (MPP) and vary its power production according to grid request. In this paper, an innovative model-based (MB) algorithm devoted to frequency regulation is presented. Thanks to the new formulation, the algorithm can vary the power curtailment according to a reduction factor given by the power system operators. Results show the remarkable accuracy of the new MB algorithm in the power prediction over an observation interval of more than six months

Reduced Power Model-Based Tracker for Photovoltaic Panels

The shift towards renewable energy has led to an ever-increasing integration of photovoltaic (PV) plants in the grid. Especially during peak production times of these plants, this leads to a reduction of rotational inertia in the power system limiting, primary frequency control capabilities. To truly participate in primary frequency regulation, PV systems must operate outside the maximum power point (MPPT) and retain an active power reserve. This paper presents the implementation and performance evaluation of an innovative model-based (MB) algorithm dedicated to PV grid frequency regulation. Simulation results demonstrate the algorithm's ability to set, with proper precision, an operation point between 80% and 100% of maximum available power. The control strategy is tested under various irradiation and temperature conditions, providing, in all cases, accurate and rapid power curtailment according to grid request