A comparative study of data-driven battery capacity estimation based on partial charging curves

With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.

Design and Application of Automatic Test System of Soil Water Characteristic Curve

[Objective] In order to better meet the requirement of crops on a more and more accurate water content under various planting environment of modern agri-culture, an automatic test system of soil water characteristic curve was designed by combining the conceptions of soil moisture content and soil water potential. [Method] Electronic soil moisture tension meter was used to determine the real-time tension value of soil moisture in the tested container, and the electronic Weigh sensor was used to determine soil Weigh. Minusing method was used to calculate soil moisture content, based on which the soil water characteristic curve was plotted. [Result] Through the filed survey of 2 different kinds of soil in Jiangsu Province, the results were as fol ows： soil of different composition showed different trend in soil water characteristic curve that the soil water characteristic relation of the sandy soil in the old course of the Yel ow River in Xuzhou was Y=-0.000 2X3＋0.027 7X2-1.644 5X＋38.161, R2=0.991 9; while the soil water characteristic relation of the saline-alkali soil in Jinhai Farm of Dafeng was Y=-0.00 2X2-0.426X＋39.905, R2=0.991 3. [Con-clusion] The automatic test system of soil water characteristic curve soil water char-acteristics curve could reflect soil moisture content and soil water potential, as wel as reflect the effectiveness of soil water to plant growth, providing basis for the sci-entific irrigation.

A Comparative Study of Charging Voltage Curve Analysis and State of Health Estimation of Lithium-ion Batteries in Electric Vehicle

Lithium-ion(Li-ion)cells degrade after repeated cycling and the cell capacity fades while its resistance increases.Degra-dation of Li-ion cells is caused by a variety of physical and chemical mechanisms and it is strongly influenced by factors including the electrode materials used,the working conditions and the battery temperature.At present,charging voltage curve analysis methods are widely used in studies of battery characteristics and the constant current charging voltage curves can be used to analyze battery aging mechanisms and estimate a battery’s state of health(SOH)via methods such as incremental capacity(IC)analysis.In this paper,a method to fit and analyze the charging voltage curve based on a neural network is proposed and is compared to the existing point counting method and the polynomial curve fitting method.The neuron parameters of the trained neural network model are used to analyze the battery capacity relative to the phase change reactions that occur inside the batteries.This method is suitable for different types of batteries and could be used in battery management systems for online battery modeling,analysis and diagnosis.

Modeling and Simulations in Symmetrical Supercapacitors Using Time Domain Mathematical Expressions

This study presents the deduction of time domain mathematical equations to simulate the curve of the charging process of a symmetrical electrochemical supercapacitor with activated carbon electrodes fed by a source of constant electric potential in time ε and the curve of the discharge process through two fixed resistors. The first resistor RCo is a control that aims to prevent sudden variations in the intensity of the electric current i1(t) present at the terminals of the electrochemical supercapacitor at the beginning of the charging process. The second resistor is the internal resistance RA of the ammeter used in the calculation of the intensity of the electric current i1(t) over time in the charging and discharging processes. The mathematical equations generated were based on a 2R(C + kUC(t)) electrical circuit model and allowed to simulate the effects of the potential-dependent capacitance (kUC(t)) on the charge and discharge curves and hence on the calculated values of the fixed capacitance C, the equivalent series resistance (ESR), the equivalent parallel resistance (EPR) and the electrical potential dependent capacitance index k.