Correlation between Battery Voltage under Loaded Condition and Estimated State of Charge at Valve-Regulated Lead Acid Battery on Discharge Condition using Open Circuit Voltage Method

2018 ◽  
Vol 9 (1) ◽  
pp. 357
Author(s):  
Ahmad Qurthobi ◽  
Anggita Bayu Krisna Pambudi ◽  
Dudi Darmawan ◽  
Reza Fauzi Iskandar
Keyword(s):  
Open Circuit Voltage ◽  
Open Circuit ◽  
State Of Charge ◽  
Lead Acid Battery ◽  
Battery Voltage ◽  
Measured Voltage ◽  
The Common ◽  
Loaded Voltage ◽  
Vrla Battery ◽  
Lead Acid

One of the common methods that developed to predict state of charge is open circuit voltage (OCV) method. The problem which commonly occurs is to find the correction parameter between open circuit voltage and loaded voltage of the battery. In this research, correlation between state of charge measurement at loaded condition of a Panasonic LC-VA1212NA1, which is a valve-regulated lead acid (VRLA) battery, and open circuit voltage had been analyzed. Based on the results of research, correlation between battery’s measured voltage under loaded condition and open circuit voltage could be approached by two linearization area. It caused by K v ’s values tend to increase when measured voltage under loaded condition V M < 11.64 volt. However, K v values would be relatively stable for every V M ≥ 11.64 volts. Therefore, estimated state of charge value, in respect to loaded battery voltage, would increase slower on V M < 11.64 volts and faster on other range.

scholarly journals Kalman filter applied to Thevenin’s modeling of a lead-acid battery

2022 ◽  
Vol 12 (2) ◽  
pp. 1350
Author(s):  
Jose Alfredo Palacio-Fernádez ◽  
Edwin García Quintero
Keyword(s):  
Kalman Filter ◽  
Open Circuit Voltage ◽  
Open Circuit ◽  
State Of Charge ◽  
Model Estimation ◽  
Lead Acid Battery ◽  
Battery Model ◽  
Internal Parameters ◽  
Different Types ◽  
Lead Acid

<span>This article determines the internal parameters of a battery analyzed from its circuit equivalent, reviewing important information that can help to identify the battery’s state of charge (SOC) and its state of health (SOH). Although models that allow the dynamics of different types of batteries to be identified have been developed, few have defined the lead-acid battery model from the analysis of a filtered signal by applying a Kalman filter, particularly taking into account the measurement of noise not just at signal output but also at its input (this is a novelty raised from the experimental). This study proposes a model for lead-acid batteries using tools such as MATLAB<sup>®</sup> and Simulink<sup>®</sup>. First, a method of filtering the input and output signal is presented, and then a method for identifying parameters from 29 charge states is used for a lead-acid battery. Different SOCs are related to different values of open circuit voltage (OCV). Ultimately, improvements in model estimation are shown using a filter that considers system and sensor noise since the modeled and filtered signal is closer to the original signal than the unfiltered modeled signal.</span>

High Temperature VLRA Lead Acid Battery SOH Characterization Based on the Evolution of Open Circuit Voltage at Different States of Charge

JOM ◽  
2021 ◽  
Author(s):  
Javier Olarte ◽  
Jaione Martínez de Ilarduya ◽  
Ekaitz Zulueta ◽  
Raquel Ferret ◽  
Erol Kurt ◽  
...  
Keyword(s):  
High Temperature ◽  
Open Circuit Voltage ◽  
Open Circuit ◽  
Lead Acid Battery ◽  
Lead Acid

scholarly journals Arduino-Based Battery Voltage Monitoring and SMS Gateway

2020 ◽  
Vol 2 (3) ◽  
pp. 111
Author(s):  
Muhammad Setiawan ◽  
Riky Dwi Puriyanto
Keyword(s):  
Standard Deviation ◽  
The State ◽  
Voltage Sensor ◽  
State Of Charge ◽  
Solar Panels ◽  
Battery Voltage ◽  
Voltage Sensors ◽  
Measured Voltage ◽  
Monitoring Process ◽  
Vrla Battery

This study aims to monitor the value of the voltage on the battery using a voltage sensor and using the State of Charge method to estimate the charged power of the VRLA battery remotely by utilizing SMS Gateway-based technology so that checking is no longer necessary. The results obtained are displayed on the smartphone in the form of an SMS. To determine the SOC in a 12V VRLA battery, it is calculated based on the number of each cell. VRLA 12V has 6 cells, each cell consisting of 2V to 2.4Volt. The capacity of a VRLA battery in 1 cell is declared 100% full at a voltage of 2.3 volts. So that data is obtained to determine the full percentage of the VRLA 12V 6 cell battery with a capacity of 7.2Ah, namely 13.8V. Experiments were carried out using solar panels, VRLA batteries, voltage sensors, Arduino UNO, and GSM SIM900A modules. This study succeeded in reading the measured voltage value with the sensor, and obtained an error value of 0.20 and a standard deviation of 0.02, and for the monitoring process to run smoothly without problems.

scholarly journals Parameter Identification and State-of-Charge Estimation for Lithium-Ion Batteries Using Separated Time Scales and Extended Kalman Filter

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1054
Author(s):  
Kuo Yang ◽  
Yugui Tang ◽  
Zhen Zhang
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Soc Estimation ◽  
Battery Model

With the development of new energy vehicle technology, battery management systems used to monitor the state of the battery have been widely researched. The accuracy of the battery status assessment to a great extent depends on the accuracy of the battery model parameters. This paper proposes an improved method for parameter identification and state-of-charge (SOC) estimation for lithium-ion batteries. Using a two-order equivalent circuit model, the battery model is divided into two parts based on fast dynamics and slow dynamics. The recursive least squares method is used to identify parameters of the battery, and then the SOC and the open-circuit voltage of the model is estimated with the extended Kalman filter. The two-module voltages are calculated using estimated open circuit voltage and initial parameters, and model parameters are constantly updated during iteration. The proposed method can be used to estimate the parameters and the SOC in real time, which does not need to know the state of SOC and the value of open circuit voltage in advance. The method is tested using data from dynamic stress tests, the root means squared error of the accuracy of the prediction model is about 0.01 V, and the average SOC estimation error is 0.0139. Results indicate that the method has higher accuracy in offline parameter identification and online state estimation than traditional recursive least squares methods.

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