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Lithium battery, as its main power source, needs to ensure the safety of drivers and passengers not only under some complex external conditions, but also under harsh use conditions, even when damaged. At some point throughout this process, it is required to evaluate the status of the battery itself in order to assure safe usage of the battery and to develop a more effective battery management plan. SOC, SOH, and condition of power are all variables that are commonly used to describe the state of a lithium battery (SOP). The ability of the battery to constantly provide or receive power, the remaining service the life cycle of the battery, and the ability of the battery to output or receive power promptly are all described by these three characteristics. In order to effectively evaluate the health status of batteries, this paper proposes a dual-mode extended Kalman filter (EKF) algorithm for the remote estimation of SOC and SOH of high-energy lithium batteries. In the estimating procedure, the open circuit voltage (OCV) is also included as a state variable in the iterative process, which allows for more accurate results. In this paper, the state space equation is established based on the first-order RC equivalent circuit model, and the battery state estimation and parameter identification are completed by using the double EKF (the dual extended Kalman filtering, DEKF) algorithm, resulting in the realization of the estimation of SOC and SOH.

A standard-free method for hoof samples taken from cattle such as cow, calf, pony and sheep has been developed in order to estimate the state of health of these animals. The standard-free method developed for human nails was confirmed to be applicable to quantitative analysis of hoof samples since the shape of continuous X-rays is almost the same for nail and hoof taken from these ungulate animals. Accuracy and sensitivity of the present standard method were examined by comparing the results with those obtained by an internal-standard method combined with a chemical-ashing method, and it is confirmed that the method is applicable to hoof samples taken from domestic animals of many species. The method allows us to quantitatively analyze untreated hoof samples and to prepare the targets without complicated preparation technique which often brings ambiguous factors such as elemental loss from the sample and contamination of the sample during preparation procedure. It is also confirmed that halogens, which are important elements for estimating the state of health and are mostly lost during chemical-ashing, can be analyzed without problem by the present method. It is found that elemental concentration of more than twenty elements can be constantly analyzed and it is expected to be quite useful in order to estimate the state of health and to make diagnosis of domestic animals. It is also confirmed that elemental concentration of essential elements in hoof is not so changed depending on the positions in the sliced sample along both horizontal and vertical axis.

The accurate health status evaluation of lithium-ion batteries is crucial for preemptive identification of potential battery failures and averting hazardous incidents, given its essential role in indicating the extent of battery degradation. The challenge in determining the State of Health (SOH) arises from the absence of a precise and standardized definition, as well as the difficulty in measuring essential input variables. Therefore, this paper utilizes current and voltage data during the charge and discharge process as direct inputs for SOH estimation and proposes a deep learning-based lithium-ion battery SOH estimation approach. Specifically, it leverages Bayesian optimized Convolutional Neural Network (CNN) within a data-driven framework. Experimental results demonstrate that the proposed deep learning method achieves a Mean Absolute Error (MAE) of 1% and a Maximum Error (MAX) below 4% in estimation accuracy, highlighting its enhanced precision and robustness.