An electric circuit based EV battery model for runtime prediction and state of charge tracking

2017 ◽  
Author(s):  
Kaveh Sarrafan ◽  
Danny Sutanto ◽  
Kashem M. Muttaqi
Keyword(s):  
Electric Circuit ◽  
State Of Charge ◽  
Runtime Prediction ◽  
Battery Model

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 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.

scholarly journals Parameter Identification and State of Charge Estimation of NMC Cells Based on Improved Ant Lion Optimizer

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Kai Zhang ◽  
Jian Ma ◽  
Xuan Zhao ◽  
Xiaodong Liu ◽  
Yixi Zhang
Keyword(s):  
Parameters Estimation ◽  
State Of Charge ◽  
Superior Performance ◽  
Model Parameters ◽  
Local Optimum ◽  
Soc Estimation ◽  
Chaotic Mapping ◽  
Battery Model ◽  
Ant Lion Optimizer ◽  
Ant Lion

For lithium battery, which is widely utilized as energy storage system in electric vehicles (EVs), accurate estimating of the battery parameters and state of charge (SOC) has a significant effect on the prediction of energy power, the estimation of remaining mileage, and the extension of usage life. This paper develops an improved ant lion optimizer (IALO) which introduces the chaotic mapping theory into the initialization and random walk processes to improve the population homogeneity and ergodicity. After the elite (best) individual is obtained, the individual mutant operator is conducted on the elite individual to further exploit the area around elite and avoid local optimum. Then the battery model parameters are optimized by IALO algorithm. As for the SOC estimation, unscented Kalman filter (UKF) is a common algorithm for SOC estimation. However, a disadvantage of UKF is that the noise information is always unknown, and it is usually tuned manually by “trial-and-error” method which is irregular and time-consuming. In this paper, noise information is optimized by IALO algorithm. The singular value decomposition (SVD) which is utilized in the process of unscented transformation to solve the problem of the covariance matrix may lose positive definiteness. The experiment results verify that the developed IALO algorithm has superior performance of battery model parameters estimation. After the noise information is optimized by IALO, the UKF can estimate the SOC accurately and the maximum errors rate is less than 1%.

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