Online Estimation of Battery Model Parameters and State of Health in Electric and Hybrid Aircraft Application

Energy ◽  
2021 ◽  
pp. 120699
Author(s):  
Seyed Reza Hashemi ◽  
Ajay Mohan Mahajan ◽  
Siamak Farhad
Keyword(s):  
Model Parameters ◽  
State Of Health ◽  
Online Estimation ◽  
Battery Model ◽  
Aircraft Application

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.

A GPU-accelerated parallel Jaya algorithm for efficiently estimating Li-ion battery model parameters

2018 ◽  
Vol 65 ◽  
pp. 12-20 ◽  
Author(s):  
Long Wang ◽  
Zijun Zhang ◽  
Chao Huang ◽  
Kwok Leung Tsui
Keyword(s):  
Model Parameters ◽  
Jaya Algorithm ◽  
Li Ion Battery ◽  
Battery Model ◽  
Li Ion

Capacity Fade Estimation of a Lithium-Ion Battery Through an Integrated Electrochemical Battery Model and Empirical Cycle Aging Model

2020 ◽  
Author(s):  
Sohel Anwar
Keyword(s):  
Electric Vehicle ◽  
Constant Current ◽  
Capacity Fade ◽  
State Of Health ◽  
Current Profile ◽  
Combined Model ◽  
Battery Model ◽  
Electric Vehicle Battery ◽  
Electrochemical Battery ◽  
Constant Current Charge

Abstract An electrochemical model based capacity fade estimation method for a Li-Ion battery is investigated in this paper. An empirical capacity fade model for estimating the state of health of a LiFePO4 electric vehicle battery was integrated with electrochemical battery model in Matlab/Simulink platform. This combined model was then validated against experimental data reported in the literature for constant current charge / discharge cycling. An HPPC current profile was then applied to the validated electrochemical-empirical battery prognosis model which reflected a real-time operating condition for charge and discharge current fluctuations in an electric vehicle battery. The combined model was simulated under the two different HPPC current inputs for three different cycle times. Additionally temperature was taken in account in estimating the cycle aging under the applied current profile to assess the present capacity remaining in the battery. The simulation results provided the state of health (SOH) of the battery for these cycling times which were comparable to the published experimental SOH values for constant current charge/discharge profiles. Thus this model can potentially be used to predict the capacity fade status of an electric vehicle battery.

scholarly journals Constructing Accurate Equivalent Electrical Circuit Models of Lithium Iron Phosphate and Lead–Acid Battery Cells for Solar Home System Applications

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2305 ◽  
Author(s):  
Yunhe Yu ◽  
Nishant Narayan ◽  
Victor Vega-Garita ◽  
Jelena Popovic-Gerber ◽  
Zian Qin ◽  
...  
Keyword(s):  
Lithium Iron Phosphate ◽  
Electrical Circuit ◽  
Design Stage ◽  
Model Parameters ◽  
Equivalent Electrical Circuit ◽  
Cell Level ◽  
Electrical Equivalent Circuit ◽  
Battery Model ◽  
Vrla Battery ◽  
Lead Acid

The past few years have seen strong growth of solar-based off-grid energy solutions such as Solar Home Systems (SHS) as a means to ameliorate the grave problem of energy poverty. Battery storage is an essential component of SHS. An accurate battery model can play a vital role in SHS design. Knowing the dynamic behaviour of the battery is important for the battery sizing and estimating the battery behaviour for the chosen application at the system design stage. In this paper, an accurate cell level dynamic battery model based on the electrical equivalent circuit is constructed for two battery technologies: the valve regulated lead–acid (VRLA) battery and the LiFePO 4 (LFP) battery. Series of experiments were performed to obtain the relevant model parameters. This model is built for low C-rate applications (lower than 0.5 C-rate) as expected in SHS. The model considers the non-linear relation between the state of charge ( S O C ) and open circuit voltage ( V OC ) for both technologies. Additionally, the equivalent electrical circuit model for the VRLA battery was improved by including a 2nd order RC pair. The simulated model differs from the experimentally obtained result by less than 2%. This cell level battery model can be potentially scaled to battery pack level with flexible capacity, making the dynamic battery model a useful tool in SHS design.

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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