Battery energy storage systems and SWOT (strengths, weakness, opportunities, and threats) analysis of batteries in power transmission

A novel method for state of health estimation of lithium-ion batteries based on improved LSTM and health indicators extraction

The state of health (SOH) of the lithium‐ion battery (LIB) is a key parameter of the battery management system. Due to the complex internal electrochemical properties of LIBs and the uncertain external working environment, it is difficult to achieve accurate SOH determination. In this paper, we propose a new SOH estimation method using a directed acyclic graph (DAG) structure based on incremental capacity analysis and empirical mode decomposition (EMD), and finally with gated recurrent unit (GRU) for fitting. First, we combine IC curves and real features into the input feature map and use EMD to separate out high‐frequency capacity regeneration fluctuations. Then, the feature maps are input into the DAG‐GRU structure to fit multiple EMD decomposition functions and build SOH prediction models, which are compared with different neural network prediction models. The prediction method simplifies the prediction process, does not need to select complex health indicators as features, and has the ability to capture the fluctuations of capacity regeneration, and can fit the fluctuations in the capacity decay curve with high precision, this integrated multi‐linear model takes into account accuracy and computational efficiency, reduces manual subjective operations, and uses artificial intelligence to complete most of the work, which is one of the important directions in future SOH research. The experimental results show that using the method proposed in this paper, the minimum mean square error and mean absolute error of SOH are reduced to 0.65‰ and 1.61%, respectively, and it also possesses excellent generalization ability.

A method for estimating the aging state of lithium‐ion batteries based on a multi‐linear integrated model

Constant current charging time based fast state-of-health estimation for lithium-ion batteries

Data-driven battery state of health estimation based on interval capacity for real-world electric vehicles

State-of-health estimation for lithium-ion batteries by combining model-based incremental capacity analysis with support vector regression

Study on Field Test and Plugging Simulation of the Knot Temporary Plugging Agent

The invention provides a data-driven earth observation satellite power supply system emergency guarantee capability evaluation method comprising the following steps: obtaining a satellite telemetry historical data set, and preprocessing the historical data set; predicting the output power of the solar cell array in the future according to the preprocessed historical data set of the solar cell array; according to the preprocessed historical data set of the relevant parameters of the storage battery pack, calculating the energy consumption of the satellite for executing various types of tasks onorbit; constructing a storage battery pack state-of-charge simulation process according to the predicted output power of the solar battery array and various types of on-orbit task energy consumptionof the satellite; inputting a to-be-evaluated emergency task into the storage battery pack state-of-charge simulation process to obtain a storage battery pack state-of-charge prediction value; and comparing the predicted value of the state of charge of the storage battery pack with a discharge depth threshold, and giving an evaluation result. According to the invention, the satellite telemetry historical data set and the task instruction information are analyzed to obtain the guarantee evaluation result of the power supply system under the emergency task condition.

Data-driven earth observation satellite power supply system emergency guarantee capability evaluation method

OBJECTIVE
Platelet-rich fibrin (PRF) plays a role in promoting wound healing by releasing cytokines, chemokines and growth factors, and by inducing proliferation and activation of cells. A pressure injury (PI) is a treatable but serious and costly disease with adverse outcomes for the patient. However, traditional PI treatments are time-consuming, with limited effectiveness. Thus, we aimed to investigate the effects and mechanisms of PRF on skin wound healing in PIs in vivo in a rat model.


METHOD
PRF was prepared from the blood of male Wistar rats. A rat model for PI ischaemia/reperfusion injury was established by placing a magnet onto the back skin, where a magnetic steel plate had been previously implanted. The rats were randomised into two groups: the control group was treated with sterile gauze dressings and the iPRF group received additional PRF. Skin wound healing rate was calculated and a CD31/Masson's trichrome stain performed.


RESULTS
In this study, 16 rats were allocated to the two groups (n=8 in each group). PRF improved the skin wound healing rate of PIs in the rats; haematoxylin and eosin staining and CD31 staining showed that the number of capillaries increased significantly in the wound. However, Masson's trichrome staining showed no increase in fibrotic tissues after PRF treatment.


CONCLUSION
In this in vivo rat model for PI, PRF accelerated skin wound healing by increasing angiogenesis in the wound.

Jia Wang

Papers

State-of-health estimation for lithium-ion batteries by combining model-based incremental capacity analysis with support vector regression

Study on Field Test and Plugging Simulation of the Knot Temporary Plugging Agent

Data-driven earth observation satellite power supply system emergency guarantee capability evaluation method

Platelet-rich fibrin accelerates skin wound healing in pressure injuries: a rat model.