scholarly journals A robust state of charge estimation for multiple models of lead acid battery using adaptive extended Kalman filter

2020 ◽  
Vol 9 (1) ◽  
pp. 1-11
Author(s):  
Maamar Souaihia ◽  
Bachir Belmadani ◽  
Rachid Taleb
Keyword(s):  
Kalman Filter ◽  
Management System ◽  
State Of Charge ◽  
Least Square ◽  
Accurate Estimation ◽  
Battery Management System ◽  
Battery Management ◽  
Lead Acid Battery ◽  
Soc Estimation ◽  
Lead Acid

An accurate estimation technique of the state of charge (SOC) of batteries is an essential task of the battery management system. The adaptive Kalman filter (AEKF) has been used as an obsever to investigate the SOC estimation effectiveness. Therefore, The SOC is a reflexion of the chemistry of the cell which it is the key parameter for the battery management system. It is very complex to monitor the SOC and control the internal states of the cell. Three battery models are proposed and their state space models have been established, their parameters were identified by applying the least square method. However, the SOC estimation accuracy of the battery depends on the model and the efficiency of the algorithm. In this paper, AEKF technique is presented to estimate the SOC of Lead acid battery. The experimental data is used to identify the parameters of the three models and used to build different open circuit voltage–state of charge (OCV-SOC) functions relationship. The results shows that the SOC estimation based-model which has been built by hight order RC model can effectively limit the error, hence guaranty the accuracy and robustness.

New lead-acid battery management system based on generator regulation

2012 ◽  
Vol 13 (4) ◽  
pp. 679-686 ◽  
Author(s):  
G. Y. Zhang ◽  
X. W. Zhao ◽  
J. X. Qiang ◽  
F. Tian ◽  
L. Yang
Keyword(s):  
Management System ◽  
Battery Management System ◽  
Battery Management ◽  
Lead Acid Battery ◽  
Lead Acid

scholarly journals SOC (STATE of CHARGE) THREE-CELL LEAD DYNAMIC BATTERY MODEL

2021 ◽  
Vol 13 (2) ◽  
pp. 49-59
Author(s):  
Kurriawan Budi Pranata ◽  
Freygion Ogiek Rizal Sukma ◽  
Muhammad Ghufron ◽  
Masruroh Masruroh
Keyword(s):  
State Of Charge ◽  
Battery Management System ◽  
Battery Management ◽  
Lead Acid Battery ◽  
Battery Model ◽  
Secondary Battery ◽  
Battery Technology ◽  
Battery Energy ◽  
Full Charge ◽  
Lead Acid

Three-cells dynamic lead-acid battery has been widely manufactured as the latest secondary battery technology. It is being carried out by 10 cycles of charge-discharge treatment with a various types of SoC, such as 100% (Full charge 5100 mAh), 50% (2550 mAh), 25% (1275 mAh) and discharge current of 0.8A. This experiment aims to analyze the treatment of SOC conditions on the performance of the lead-acid battery. The cyclicality test has performed using a Battery Management System (BMS) by applying an electric current at charging 1 A and discharging 0.8A. The results of the SOC charging conditions at 100%, 50%, 25% respectively gave a difference in the value of voltage efficiency of 84%, 87%, 88%, capacity efficiency values of 84%, 80%, 69%, energy efficiency values of 70%, 70%, 62%. The 100% and 50% SOC treatments showed better performance and battery energy the 25% SOC treatment. This research can be a recommendation to predict the performance of the lead-acid battery model during the charging and discharging process.

Study on Battery Management System Design of Electric Vehicle and Strategy of the SOC Estimation

2014 ◽  
Vol 945-949 ◽  
pp. 1500-1506
Author(s):  
Zhong An Yu ◽  
Jun Peng Jian
Keyword(s):  
Kalman Filter ◽  
Electric Vehicle ◽  
Digital Signal Processor ◽  
Management System ◽  
Estimation Accuracy ◽  
Space Representation ◽  
Battery Management System ◽  
Battery Management ◽  
Soc Estimation ◽  
Battery Model

In order to improve the efficiency and service life of Lithium batteries for electric vehicle. A structure diagram of battery management system with the digital signal processor as the main controller was designed; in addition, some design modules were expatiated clearly, including the sample circuits of the batterys voltage, current and equalization circuit. The state-space representation of the battery model was established based on Thevenin battery model and extended Kalman filter (EKF) algorithm.According to the estimates and performance characteristics of battery, a new improved-way by amending the Kalman filter gain with the actual situation for raised the SOC estimation accuracy was proposed. The simulation and test results under the condition of simulated driving show that this new way really can increase the SOC accuracy; the equalization scheme can effectively compensate the performance inconsistency of battery pack.

scholarly journals Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 446 ◽  
Author(s):  
Muhammad Umair Ali ◽  
Amad Zafar ◽  
Sarvar Hussain Nengroo ◽  
Sadam Hussain ◽  
Muhammad Junaid Alvi ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Management System ◽  
Lithium Ion ◽  
State Of Charge ◽  
Estimation Methods ◽  
Battery Management System ◽  
Battery Management ◽  
Li Ion Battery ◽  
Soc Estimation ◽  
Li Ion

Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium-ion (Li-ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart battery management system (BMS) is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life. The accurate estimation of the state of charge (SOC) of a Li-ion battery is a very challenging task because the Li-ion battery is a highly time variant, non-linear, and complex electrochemical system. This paper explains the workings of a Li-ion battery, provides the main features of a smart BMS, and comprehensively reviews its SOC estimation methods. These SOC estimation methods have been classified into four main categories depending on their nature. A critical explanation, including their merits, limitations, and their estimation errors from other studies, is provided. Some recommendations depending on the development of technology are suggested to improve the online estimation.

scholarly journals State of Charge Estimation of a Composite Lithium-Based Battery Model Based on an Improved Extended Kalman Filter Algorithm

Inventions ◽  
2019 ◽  
Vol 4 (4) ◽  
pp. 66 ◽  
Author(s):  
Ning Ding ◽  
Krishnamachar Prasad ◽  
Tek Tjing Lie ◽  
Jinhui Cui
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Open Circuit ◽  
State Of Charge ◽  
Battery Management System ◽  
Battery Management ◽  
Soc Estimation ◽  
Battery Model ◽  
Operation Conditions ◽  
Battery State Of Charge

The battery State of Charge (SoC) estimation is one of the basic and significant functions for Battery Management System (BMS) in Electric Vehicles (EVs). The SoC is the key to interoperability of various modules and cannot be measured directly. An improved Extended Kalman Filter (iEKF) algorithm based on a composite battery model is proposed in this paper. The approach of the iEKF combines the open-circuit voltage (OCV) method, coulomb counting (Ah) method and EKF algorithm. The mathematical model of the iEKF is built and four groups of experiments are conducted based on LiFePO4 battery for offline parameter identification of the model. The iEKF is verified by real battery data. The simulation results with the proposed iEKF algorithm under both static and dynamic operation conditions show a considerable accuracy of SoC estimation.

scholarly journals A New SOC Estimation for LFP Batteries: Application in a 10 Ah Cell (HW 38120 L/S) as a Hysteresis Case Study

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 705
Author(s):  
Younghwi Ko ◽  
Woojin Choi
Keyword(s):  
Lithium Iron Phosphate ◽  
Iron Phosphate ◽  
Least Square ◽  
Accurate Estimation ◽  
Hysteresis Phenomenon ◽  
Battery Management System ◽  
Battery Management ◽  
Recursive Least Square ◽  
Soc Estimation ◽  
Hysteresis Modeling

An accurate state of charge (SOC) estimation of the lithium iron phosphate battery (LiFePO4) is one of the most important functions for the battery management system (BMS) for electric vehicles (EVs) and energy storage systems (ESSs). However, an accurate estimation of the SOC of LiFePO4 is challenging due to the hysteresis phenomenon occurring during the charge and discharge. Therefore, an accurate modeling of the hysteresis phenomenon is essential for reliable SOC estimation. The conventional hysteresis modeling methods, such as one-state hysteresis modeling and parallelogram modeling, are not good enough to achieve high-accuracy SOC estimation due to their errors in the approximation of the hysteresis contour. This paper proposes a novel method for accurate hysteresis modeling, which can provide a significant improvement in terms of the accuracy of the SOC estimation compared with the conventional methods. The SOC estimation is performed by using an extended Kalman filter (EKF) and the parameters of the battery are estimated by using auto regressive exogenous (ARX) model and the recursive least square (RLS) filter. The experimental results with the conventional and proposed methods are compared to show the superiority of the proposed method.

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