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 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 Lithium ferro phosphate battery state of charge estimation using particle filter

2021 ◽  
Vol 12 (2) ◽  
pp. 975
Author(s):  
Noor Iswaniza Md Siam ◽  
Tole Sutikno ◽  
Mohd Junaidi Abdul Aziz
Keyword(s):  
Experimental Data ◽  
Particle Filter ◽  
Pulse Discharge ◽  
Electric Circuit ◽  
State Of Charge ◽  
Model Parameters ◽  
Battery Management System ◽  
Battery Model ◽  
Battery State Of Charge ◽  
Discharge Test

Lithium ferro phosphate (LiFePO<sub>4</sub>) has a promising battery technology with high charging/discharging behaviours make it suitable for electric vehicles (EVs) application. Battery state of charge (SOC) is a vital indicator in the battery management system (BMS) that monitors the charging and discharging operation of a battery pack. This paper proposes an electric circuit model for LiFePO<sub>4</sub> battery by using particle filter (PF) method to determine the SOC estimation of batteries precisely. The LiFePO<sub>4</sub> battery modelling is carried out using MATLAB software. Constant discharge test (CDT) is performed to measure the usable capacity of the battery and pulse discharge test (PDT) is used to determine the battery model parameters. Three parallel RC battery models have been chosen for this study to achieve high accuracy. The proposed PF implements recursive bayesian filter by Monte Carlo sampling which is robust for non-linear and/or non-Gaussian distributions. The accuracy of the developed electrical battery model is compared with experimental data for verification purpose. Then, the performance of the model is compared with experimental data and extended Kalman filter (EKF) method for validation purposed. A superior battery SOC estimator with higher accuracy compared to EKF method has been obtained.

scholarly journals Lithium-Ion Battery Parameters and State of Charge Joint Estimation Using Bias Compensation Least Squares and the Alternate Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Wei Xiong ◽  
Yimin Mo ◽  
Cong Yan
Keyword(s):  
Least Squares ◽  
Computational Efficiency ◽  
High Accuracy ◽  
State Of Charge ◽  
Joint Estimation ◽  
Model Parameters ◽  
Battery Management System ◽  
Battery Management ◽  
Soc Estimation ◽  
Bias Compensation

For safe and efficient operation of electric vehicles (EVs), battery management system is essential. Nevertheless, a challenge lying in battery management systems is how to obtain an algorithm for state of charge (SOC) estimation that has both high accuracy and low computational cost. For this purpose, the battery parameters and SOC joint estimation algorithm based on bias compensation least squares and alternate (BCLS-ALT) algorithm are proposed in this paper. The battery model parameters are identified online using the bias compensation least squares (BCLS), while the SOC is estimated applying the alternate (ALT) algorithm, which can switch the computational logic between H-infinity filter (HIF) and ampere-hour integral (AHI) to improve the computational efficiency and accuracy. The experimental results show that the accuracy of the SOC estimated by the BCLS-ALT algorithm is the highest, and the computational efficiency is also high, with the switching threshold SOCALT being set to 25%. Despite the 20% initial error and the 10% current drift, the proposed BCLS-ALT algorithm can obtain high accuracy and robustness of SOC estimation under different ambient temperatures and dynamic load profiles.

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.

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 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.

scholarly journals Thermal Analysis of LMO/Graphite Batteries Using Equivalent Circuit Models

Batteries ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 58
Author(s):  
Nadjiba Mahfoudi ◽  
M’hamed Boutaous ◽  
Shihe Xin ◽  
Serge Buathier
Keyword(s):  
Thermal Behavior ◽  
Equivalent Circuit ◽  
Thermal Management ◽  
Dynamic Models ◽  
Operating Time ◽  
Operating Conditions ◽  
State Of Charge ◽  
Model Parameters ◽  
Battery Model ◽  
Equivalent Circuit Models

An efficient thermal management system (TMS) of electric vehicles requires a high-fidelity battery model. The model should be able to predict the electro-thermal behavior of the battery, considering the operating conditions throughout the battery’s lifespan. In addition, the model should be easy to handle for the online monitoring and control of the TMS. Equivalent circuit models (ECMs) are widely used because of their simplicity and suitable performance. In this paper, the electro-thermal behavior of a prismatic 50 Ah LMO/Graphite cell is investigated. A dynamic model is adopted to describe the battery voltage, current, and heat generation. The battery model parameters are identified for a single cell, considering their evolution versus the state of charge and temperature. The needed experimental data are issued from the measurements carried out, thanks to a special custom electrical bench able to impose a predefined current evolution or driving cycles, controllable by serial interface. The proposed battery parameters, functions of state of charge (SOC), and temperature (T) constitute a set of interesting and complete data, not available in the literature, and suitable for further investigations. The thermal behavior and the dynamic models are validated using the New European Driving Cycle (NEDC), with a large operating time, higher than 3 h. The measurement and model prediction exhibit a temperature difference less than 1.2 °C and a voltage deviation less than 3%, showing that the proposed model accurately predicts current, voltage, and temperature. The combined effects of temperature and SOC provides a more efficient modeling of the cell behavior. Nevertheless, the simplified model with only temperature dependency remains acceptable. Hence, the present modeling constitutes a confident prediction and a real step for an online control of the complete thermal management of electrical vehicles.

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