scholarly journals Estimating the state of charge on lead acid battery using the open circuit voltage method

2019 ◽  
Vol 1367 ◽  
pp. 012077 ◽  
Author(s):  
A. Muh. Rifqa Al Hadi ◽  
Cahyantari Ekaputri ◽  
Muhamad Reza
Keyword(s):  
Open Circuit Voltage ◽  
Open Circuit ◽  
The State ◽  
State Of Charge ◽  
Lead Acid Battery ◽  
Lead Acid

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>

scholarly journals A Nonlinear-Model-Based Observer for a State-of-Charge Estimation of a Lithium-Ion Battery in Electric Vehicles

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3383 ◽  
Author(s):  
Woo-Yong Kim ◽  
Pyeong-Yeon Lee ◽  
Jonghoon Kim ◽  
Kyung-Soo Kim
Keyword(s):  
Lithium Ion Battery ◽  
Nonlinear Model ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
The State ◽  
State Of Charge ◽  
Battery Cell ◽  
Model Based ◽  
State Of Charge Estimation

This paper presents a nonlinear-model-based observer for the state of charge estimation of a lithium-ion battery cell that always exhibits a nonlinear relationship between the state of charge and the open-circuit voltage. The proposed nonlinear model for the battery cell and its observer can estimate the state of charge without the linearization technique commonly adopted by previous studies. The proposed method has the following advantages: (1) The observability condition of the proposed nonlinear-model-based observer is derived regardless of the shape of the open circuit voltage curve, and (2) because the terminal voltage is contained in the state vector, the proposed model and its observer are insensitive to sensor noise. A series of experiments using an INR 18650 25R battery cell are performed, and it is shown that the proposed method produces convincing results for the state of charge estimation compared to conventional SOC estimation methods.

Photometrical Determination of the State-of-Charge in Vanadium Redox Flow Batteries Part II: In Combination with Open-Circuit-Voltage

2019 ◽  
Vol 233 (12) ◽  
pp. 1695-1711 ◽  
Author(s):  
Jan Geiser ◽  
Harald Natter ◽  
Rolf Hempelmann ◽  
Bernd Morgenstern ◽  
Kaspar Hegetschweiler
Keyword(s):  
Open Circuit Voltage ◽  
Open Circuit ◽  
The State ◽  
State Of Charge ◽  
Vanadium Redox Flow Battery ◽  
Term Operation ◽  
Partial State ◽  
Set Up ◽  
Vanadium Redox

AbstractBy means of in-situ UV/Vis/NIR spectrometry, separately both in the anolyte as well as in the catholyte of a vanadium redox flow battery (single cell) partial state-of-charge values are determined online. The UV/Vis/NIR spectroscopic experimental set-up is calibrated using the state-of-charge value determined from measurements of the open-circuit-voltage (OCV) in the pristine state of the battery which is related to Nernst’s equation taking into account also H+ formation/consumption during the V4+/V5+ redox process. The comparison of both partial state-of-charge values indicates a possible imbalance of the battery, which can occur after long-term operation.

Optimal Power Management for Seismic Nodes

2021 ◽  
Vol 56 ◽  
pp. 162-181
Author(s):  
Dauda Duncan ◽  
Adamu Murtala Zungeru ◽  
Mmoloki Mangwala ◽  
Bakary Diarra ◽  
Joseph Chuma ◽  
...  
Keyword(s):  
Neural Network ◽  
Historical Data ◽  
The State ◽  
State Of Charge ◽  
Lead Acid Battery ◽  
Point Tracking ◽  
The Neural Network ◽  
Key Factor ◽  
Hidden Layer ◽  
Lead Acid

Estimating the state-of-charge of a lead-acid battery at remote seismic nodes is a key factor in managing the available power. Optimal management enables the continuous acquisition of seismic data of an area. This paper presents the management of lead-acid batteries at remote seismic nodes, using the Neural Network model's historical data to estimate the battery's state-of-charge. Powersim (PSIM) simulation tool was used to implement photovoltaic energy harvesting system with a buck mode converter and maximum power point tracking algorithm to acquire historical data. A backpropagation neural network technique for training the historical dataset of hourly points in 500 days on the Matlab platform is adopted, and a feedforward neural network is employed due to the irregularities of the input data. The neural network model's hidden layer contains the transfer function of the Tansig Function to produce the model output of state-of-charge estimations. Besides, this paper is based on the management of estimating the state-of-charge of the lead-acid battery near-realtime instead of relying on the vendor's lifecycle information. The simulated results show the simplicity and optimal estimations of state-of-charge of the lead-acid battery with RMSE of 0.023%.

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