scholarly journals Tuning of Kalman Filter Parameters via Genetic Algorithm for State-of-Charge Estimation in Battery Management System

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
T. O. Ting ◽  
Ka Lok Man ◽  
Eng Gee Lim ◽  
Mark Leach
Keyword(s):  
Genetic Algorithm ◽  
Kalman Filter ◽  
Dynamical Behavior ◽  
State Of Charge ◽  
Battery Management System ◽  
Battery Management ◽  
Optimal Parameters ◽  
Online Application ◽  
Source Codes ◽  
Battery Model

In this work, a state-space battery model is derived mathematically to estimate the state-of-charge (SoC) of a battery system. Subsequently, Kalman filter (KF) is applied to predict the dynamical behavior of the battery model. Results show an accurate prediction as the accumulated error, in terms of root-mean-square (RMS), is a very small value. From this work, it is found that different sets ofQandRvalues (KF’s parameters) can be applied for better performance and hence lower RMS error. This is the motivation for the application of a metaheuristic algorithm. Hence, the result is further improved by applying a genetic algorithm (GA) to tuneQandRparameters of the KF. In an online application, a GA can be applied to obtain the optimal parameters of the KF before its application to a real plant (system). This simply means that the instantaneous response of the KF is not affected by the time consuming GA as this approach is applied only once to obtain the optimal parameters. The relevant workable MATLAB source codes are given in the appendix to ease future work and analysis in this area.

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.

Modeling and Simulation Research on Lithium-Ion Battery in Electric Vehicles Based on Genetic Algorithm

2014 ◽  
Vol 494-495 ◽  
pp. 246-249
Author(s):  
Cheng Lin ◽  
Xiao Hua Zhang
Keyword(s):  
Genetic Algorithm ◽  
Lithium Ion Battery ◽  
Stress Test ◽  
Lithium Ion ◽  
Model Parameters ◽  
Test Accuracy ◽  
Battery Management System ◽  
Battery Management ◽  
Simulation Research ◽  
Battery Model

Based on the genetic algorithm (GA), a novel type of parameters identification method on battery model was proposed. The battery model parameters were optimized by genetic optimization algorithm and the other parameters were identified through the hybrid pulse power characterization (HPPC) test. Accuracy and efficiency of the battery model were validated with the dynamic stress test (DST). Simulation and experiment results shows that the proposed model of the lithium-ion battery with identified parameters was accurate enough to meet the requirements of the state of charge (SoC) estimation and battery management system.

SoC Evaluation of Power Battery Remaining Capacity Based on Battery Model and Extended Kalman Filter

2014 ◽  
Vol 971-973 ◽  
pp. 1152-1155
Author(s):  
Min Wu ◽  
Hai Pu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Management System ◽  
New Method ◽  
Battery Management System ◽  
Battery Management ◽  
Traditional Methods ◽  
Battery Model ◽  
Power Battery ◽  
Remaining Capacity

The estimate of the remaining capacity of power battery is one of the most important function of battery management system. Using traditional methods is difficult to estamate the remaining capacity, this paper put forward a new method which is based on the battery model and extended Kalman filter.Then carries on the concrete analysis and discussion.

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.

Comprehensive Battery Equivalent Circuit Based Model for Battery Management Application

2013 ◽  
Author(s):  
Shijie Tong ◽  
Matthew P. Klein ◽  
Jae Wan Park
Keyword(s):  
Equivalent Circuit ◽  
Life Time ◽  
State Of Charge ◽  
Model Parameters ◽  
Battery Management System ◽  
Battery Management ◽  
Computationally Efficient ◽  
Battery Model ◽  
Battery State Of Charge ◽  
Dependent Model

This paper presents a comprehensive control oriented battery model. Described first is an equivalent circuit based battery model which captures particular battery characteristics of control interest. Then, the model categorizes the battery dynamics based on their different time constants (transient, long-term, life-time). This model uses a 2-D map representing the temperature and state-of-charge dependent model parameters. Also, the model uses new battery state-of-charge and state-of-health definitions that are more practical for a real battery management system. Battery testing and simulation on various types of batteries and use scenarios was completed to validate that the model is easy to parameterize, computationally efficient and of adequate accuracy.

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.

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