scholarly journals Novel Technique for Estimation of Cell Parameters Using MATLAB/Simulink

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 117
Author(s):  
Sumukh Surya ◽  
Cifha Crecil Saldanha ◽  
Sheldon Williamson
Keyword(s):  
Parameter Estimation ◽  
Cell Model ◽  
State Of Charge ◽  
Matlab Simulink ◽  
Cell Parameters ◽  
Soc Estimation ◽  
Estimation And Prediction ◽  
Svm Algorithm ◽  
Machine Leaning ◽  
Li Ion

The main source of power in Electric Vehicles (EVs) is derived from batteries. An efficient cell model is extremely important for the development of complex algorithms like core temperature estimation, State of Health (SOH) estimation and State of Charge (SOC) estimation. In this paper, a new methodology for improving the SOC estimation using Equivalent Cell Model (ECM) approach is proposed. The modeling and simulations were performed using MATLAB/Simulink software. In this regard, a Li polymer cell was modeled as a single Resistor-Capacitor (RC) pair (R0, R1 and C1) model using PowerTrain blockset in MATLAB/Simulink software. To validate the developed model, a NASA dataset was used as the reference dataset. The cell model was tuned against the NASA dataset for different currents in such a way that the error in the terminal voltages (difference in terminal voltage between the dataset and the ECM) is <±0.2 V. The mean error and the standard deviation of the error were 0.0529 and 0.0310 respectively. This process was performed by tuning the cell parameters. It was found that the cell parameters were independent of the nominal capacity of the cell. The cell parameters of Li polymer and the Li ion cells (NASA dataset) were found be almost identical. These parameters showed dependence on SOC and temperature. The major challenge in a battery management system is the parameter estimation and prediction of SOC, this is because the degradation of battery is highly nonlinear in nature. This paper presents the parameter estimation and prediction of state of charge of Li ion batteries by implementing different machine learning techniques. The selection of the best suited algorithm is finalized through the performance indices mainly by evaluating the values of R- Squared. The parameters were trained using various Machine Leaning (ML) techniques for regression data analysis using Simulink. A study on Support Vector Machine (SVM) technique was carried out for the simulated and tuned data. It is concluded that the SVM algorithm was best suited. A detailed analysis on the errors associated with the algorithms was also carried out. Later, these parameters were trained using various Machine Leaning (ML) techniques for regression data analysis using Simulink. A study on SVM technique was carried out for the simulated and tuned data. It is concluded that the SVM algorithm was best suited. A detailed analysis on the errors associated with the algorithms was also carried out.

scholarly journals Real-Time State-of-Charge Estimation via Particle Swarm Optimization on a Lithium-Ion Electrochemical Cell Model

Batteries ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 4 ◽  
Author(s):  
Arun Chandra Shekar ◽  
Sohel Anwar
Keyword(s):  
Particle Swarm Optimization ◽  
Single Cell ◽  
Real Time ◽  
Cell Model ◽  
Particle Swarm ◽  
Lithium Ion ◽  
State Of Charge ◽  
Swarm Optimization ◽  
Soc Estimation ◽  
Electrochemical Model

With the ever-increasing usage of lithium-ion batteries, especially in transportation applications, accurate estimation of battery state of charge (SOC) is of paramount importance. A majority of the current SOC estimation methods rely on data collected and calibrated offline, which could lead to inaccuracies in SOC estimation under different operating conditions or when the battery ages. This paper presents a novel real-time SOC estimation of a lithium-ion battery by applying the particle swarm optimization (PSO) method to a detailed electrochemical model of a single cell. This work also optimizes both the single-cell model and PSO algorithm so that the developed algorithm can run on an embedded hardware with reasonable utilization of central processing unit (CPU) and memory resources while estimating the SOC with reasonable accuracy. A modular single-cell electrochemical model, as well as the proposed constrained PSO-based SOC estimation algorithm, was developed in Simulink©, and its performance was theoretically verified in simulation. Experimental data were collected for healthy and aged Li-ion battery cells in order to validate the proposed algorithm. Both simulation and experimental results demonstrate that the developed algorithm is able to accurately estimate the battery SOC for 1C charge and 1C discharge operations for both healthy and aged cells.

Online State of Charge Estimation for Battery of Electric Vehicle Using Sigma-Points Kalman Filters

2013 ◽  
Vol 427-429 ◽  
pp. 824-829
Author(s):  
Li Cun Fang ◽  
Gang Xu ◽  
Tian Li Li ◽  
Ke Min Zhu
Keyword(s):  
Electric Vehicle ◽  
Kalman Filters ◽  
Hybrid Electric Vehicle ◽  
Lithium Battery ◽  
Cell Model ◽  
Operating Conditions ◽  
State Of Charge ◽  
Battery Cell ◽  
Soc Estimation ◽  
Simulation And Experiment

An accurate state-of-charge (SOC) estimation of the hybrid electric vehicle (HEV) and electric vehicle (EV) battery pack is a difficult task to be performed online in a vehicle because of the noisy and low accurate measurements and the wide operating conditions in which the vehicle battery can operate. A Sigma-points Kalman Filters (SPKF) algorithm based on an improved Lithium battery cell model to estimate the SOC of a Lithium battery cell is proposed in this paper. The simulation and experiment results show the effectiveness and ease of implementation of the proposed technique.

A Strategy for Estimating State-of-Charge and State-of-Health of Li-Ion Batteries in Electric and Hybrid Electric Vehicles

2012 ◽  
Author(s):  
Xiaowei Zhao ◽  
Guoyu Zhang ◽  
Lin Yang
Keyword(s):  
Equivalent Circuit ◽  
Estimation Method ◽  
Equivalent Circuit Model ◽  
Circuit Model ◽  
State Of Charge ◽  
State Of Health ◽  
Li Ion Batteries ◽  
Soc Estimation ◽  
Kaiman Filtering ◽  
Li Ion

A task that has to be solved for the application of batteries in vehicles with an electric drive train is the determination of the actual state-of-health (SOH) and state-of-charge (SOC) of the battery cells. In this paper, an on board strategy for estimating SOC and SOH of Li-ion batteries is proposed. The equivalent circuit model is used for both SOC and SOH estimations. In SOH algorithm, the estimated value of battery capacity not only reflects the aging degree of battery pack, but also provides information for SOC estimation. Meanwhile, the extended Kaiman filtering is used in SOC estimation. Because the performance of the equivalent circuit model will be better at small currents than at high currents, extended Kaiman filtering is substituted by Ampere-Hour counting when the absolute value of current is greater than a calibration value. The Digatron battery tester was used to evaluate the proposed estimation method, and results show that the estimation method has high accuracy and efficiency at ordinary temperatures.

scholarly journals Real-Time SoC Estimation for Li-Ion Batteries using Kalman Filter based on SBC Raspberry-Pi

2021 ◽  
Vol 1 (02) ◽  
pp. 48-57
Author(s):  
Zaini Zaini ◽  
Dwi Mutiara Harfina ◽  
Agung P Iswar
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
External Noise ◽  
State Of Charge ◽  
Raspberry Pi ◽  
Filter Method ◽  
Discharge Data ◽  
Soc Estimation ◽  
Li Ion ◽  
Using Data

Measurement of electric charge on the battery in real-time cannot be separated from external noise and disturbances such as temperature and interference. An optimal State of Charge (SoC) estimator model is needed to make the estimation more accurate. To obtain the model, the battery was tested under room temperature conditions and at a temperature of 40oC to obtain a second-order RC model for the Li-Ion battery used. Based on the test data obtained, the data will be tested first using the Kalman Filter method to get an estimate of the State of Charge (SoC). Tests were carried out using MATLAB software. After the method was tested, the online SoC Estimator design began using the Raspberry Pi Single Board Computer (SBC). After that, the estimator will be tested first using data from offline measurements and then used in real-time (online) SoC estimation measurements. The Voc before the battery discharge test was 13.16 V and after the test, the measured Voc was 11.58 V. During the discharge the Voc was reduced by 1.58 V. While the discharge data from the battery manufacturer showed the reduced Voc during the discharge was 1.2V.

Online State-of-Charge Estimation of Li-Ion Battery Based on the Second-Order RC Model

2013 ◽  
Vol 805-806 ◽  
pp. 1659-1663 ◽  
Author(s):  
Ze Cheng ◽  
Qiu Yan Zhang ◽  
Yu Hui Zhang
Keyword(s):  
Estimation Method ◽  
Equivalent Circuit Model ◽  
Second Order ◽  
State Of Charge ◽  
Battery Management System ◽  
Battery Management ◽  
Li Ion Battery ◽  
Soc Estimation ◽  
Counting Approach ◽  
Li Ion

The real-timely estimation of the SOC (state of charge) is the key technology in Li-ion battery management system. In this paper, to overcome the error of the SOC estimation of Extended Kalman filter (EKF), a new estimation method based on modified-strong tracking filter (MSTF) is applied to SOC estimation of Li-ion battery, based on the second-order RC equivalent circuit model. Experiments are made to compare the new filter with the EKF and Coulomb counting approach (Ah). The simulation results demonstrate that the new filter algorithm MSTF used in this paper has higher filtering accuracy under the same conditions.

scholarly journals Real-Time SoC Estimation for Li-Ion Batteries using Kalman Filter based on SBC Raspberry-Pi

2021 ◽  
Vol 1 (2) ◽  
pp. 48-57
Author(s):  
Zaini Zaini ◽  
Dwi Mutiara Harfina ◽  
Agung P Iswar
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
External Noise ◽  
State Of Charge ◽  
Raspberry Pi ◽  
Filter Method ◽  
Discharge Data ◽  
Soc Estimation ◽  
Li Ion ◽  
Using Data

Measurement of electric charge on the battery in real-time cannot be separated from external noise and disturbances such as temperature and interference. An optimal State of Charge (SoC) estimator model is needed to make the estimation more accurate. To obtain the model, the battery was tested under room temperature conditions and at a temperature of 40oC to obtain a second-order RC model for the Li-Ion battery used. Based on the test data obtained, the data will be tested first using the Kalman Filter method to get an estimate of the State of Charge (SoC). Tests were carried out using MATLAB software. After the method was tested, the online SoC Estimator design began using the Raspberry Pi Single Board Computer (SBC). After that, the estimator will be tested first using data from offline measurements and then used in real-time (online) SoC estimation measurements. The Voc before the battery discharge test was 13.16 V and after the test, the measured Voc was 11.58 V. During the discharge the Voc was reduced by 1.58 V. While the discharge data from the battery manufacturer showed the reduced Voc during the discharge was 1.2V.

scholarly journals Towards Accurate State of Charge Estimation for Lithium-ion Batteries using Self-supervised Transformer Model: A Deep Learning Approach

2021 ◽  
Author(s):  
M Hannan ◽  
Dickson How ◽  
M. S. Hossain Lipu ◽  
M Mansor ◽  
Pin Ker ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lithium Ion ◽  
Absolute Error ◽  
State Of Charge ◽  
Training Data ◽  
Ambient Temperatures ◽  
Soc Estimation ◽  
Proposed Model ◽  
Li Ion ◽  
Transformer Model

Abstract Accurate state of charge (SOC) estimation of lithium-ion (Li-ion) batteries is crucial in prolonging cell lifespan and ensuring its safe operation for electric vehicle applications. In this article, we propose the deep learning-based transformer model trained with self-supervised learning (SSL) for end-to-end SOC estimation without the requirements of feature engineering or adaptive filtering. We demonstrate that with the SSL framework, the proposed deep learning-enabled transformer model achieves the lowest root-mean-square-error (RMSE) of 1.2% and a mean-absolute-error (MAE) of 0.7% on the test dataset at various ambient temperatures. With SSL, the proposed model can be trained with as few as 5 epochs using only 20% of the total training data and still achieves less than 1.9% RMSE on the test data. Finally, we also demonstrate that the learning weights during the SSL training can be transferred to a new Li-ion cell with different chemistry and still achieve on-par performance compared to the models trained from scratch on the new cell.

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