scholarly journals An Open Circuit Voltage Model Fusion Method for State of Charge Estimation of Lithium-Ion Batteries

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1797
Author(s):  
Quanqing Yu ◽  
Changjiang Wan ◽  
Junfu Li ◽  
Lixin E ◽  
Xin Zhang ◽  
...  
Keyword(s):  
Open Circuit Voltage ◽  
Stress Test ◽  
Lithium Ion ◽  
Open Circuit ◽  
State Of Charge ◽  
Battery Management System ◽  
Fusion Method ◽  
Battery Management ◽  
Model Fusion ◽  
Cubature Kalman Filter

The mapping between open circuit voltage (OCV) and state of charge (SOC) is critical to the lithium-ion battery management system (BMS) for electric vehicles. In order to solve the poor accuracy in the local SOC range of most OCV models, an OCV model fusion method for SOC estimation is proposed. According to the characteristics of the experimental OCV–SOC curve, the method divides SOC interval (0, 100%) into several sub-intervals, and respectively fits the OCV curve segments in each sub-interval to obtain a corresponding number of OCV sub-models with local high precision. After that, the OCV sub-models are fused through the continuous weight function to obtain fusional OCV model. Regarding the OCV curve obtained from low-current OCV test as the criterion, the fusional OCV models of LiNiMnCoO2 (NMC) and LiFePO4 (LFP) are compared separately with the conventional OCV models. The comparison shows great fitting accuracy of the fusional OCV model. Furthermore, the adaptive cubature Kalman filter (ACKF) is utilized to estimate SOC and capacity under a dynamic stress test (DST) at different temperatures. The experimental results show that the fusional OCV model can effectively track the performance of the OCV–SOC curve model.

scholarly journals Development of Life-cycle Detection and Extending Function for Reusing Automotive Lithium-ion Battery – Verified in 52V LiMnNiCoO2 Platform

2020 ◽  
Author(s):  
Wu-Yang Sean ◽  
Ana Pacheco
Keyword(s):  
Life Cycle ◽  
Lithium Ion Battery ◽  
Management System ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
Battery Pack ◽  
Battery Management System ◽  
Battery Management ◽  
Cycle Detection

Abstract For reusing automotive lithium-ion battery, an in-house battery management system is developed. To overcome the issues of life cycle and capacity of reused battery, an online function of estimating battery’s internal resistance and open-circuit voltage based on adaptive control theory are applied for monitoring life cycle and remained capacity of battery pack simultaneously. Furthermore, ultracapacitor is integrated in management system for sharing peak current to prolong life span of reused battery pack. The discharging ratio of ultracapacitor is adjusted manually under Pulse-Width-Modulation signal in battery management system. In case study in 52V LiMnNiCoO2 platform, results of estimated open-circuit voltage and internal resistances converge into stable values within 600(s). These two parameters provide precise estimation for electrical capacity and life cycle. It also shows constrained voltage drop both in the cases of 25% to 75% of ultracapacitors discharging ratio compared with single battery. Consequently, the Life-cycle detection and extending functions integrated in battery management system as a total solution for reused battery are established and verified.

An Improved Test Method of LiFePO4/Graphene Hybrid Cathode Lithium-Ion Battery and the State of Charge Estimation

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Meiying Li ◽  
Zhiping Guo ◽  
Yuan Li ◽  
Wenliang Wu
Keyword(s):  
Lithium Ion Battery ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Open Circuit ◽  
The State ◽  
State Of Charge ◽  
Least Square ◽  
Test Method ◽  
Battery Management System ◽  
Battery Management

Abstract The state of charge (SoC) of the battery is a typical characterization of the operating state of the battery and criterion for the battery management system (BMS) control strategy, which must be evaluated precisely. The establishment of an accurate algorithm of SoC estimation is of great significance for BMS, which can help the driver judge the endurance mileage of electric vehicle (EV) correctly. In this paper, a second-order resistor-capacity (RC) equivalent circuit model is selected to characterize the electrical characteristics based on the electrochemical model of the LiFePO4/graphene (LFP/G) hybrid cathode lithium-ion battery. Moreover, seven open circuit voltage (OCV) models are compared and the best one of them is used to simulate the dynamic characteristics of the battery. It is worth mentioning that an improved test method is proposed, which is combined with least square for parameters identification. In addition, the extended Kalman filter (EKF) algorithm is selected to estimate the SoC during the charging and discharging processes. The simulation results show that the EKF algorithm has the higher accuracy and rapidity than the KF algorithm.

scholarly journals A State of Charge Estimation Method of Lithium-Ion Battery Based on Fused Open Circuit Voltage Curve

2020 ◽  
Vol 10 (4) ◽  
pp. 1264
Author(s):  
Yipeng Wang ◽  
Lin Zhao ◽  
Jianhua Cheng ◽  
Junfeng Zhou ◽  
Shuo Wang
Keyword(s):  
Polarization Effect ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
State Of Charge ◽  
Model Parameters ◽  
Fusion Method ◽  
Test Results ◽  
Soc Estimation ◽  
Current Test

The open circuit voltage (OCV) and model parameters are critical reference variables for a lithium-ion battery management system estimating the state of charge (SOC) accurately. However, the polarization effect reduces the accuracy of the OCV test, and the model parameters coupled to the polarization voltage increase the non-linearity of the cell model, all challenging SOC estimation. This paper presents an OCV curve fusion method based on the incremental and low-current test. Fusing the incremental test results without polarization effect and the low current test results with non-linear characteristics of electrodes, the fusion method improves the OCV curve’s accuracy. In addition, we design a state observer with model parameters and SOC, and the unscented Kalman filter (UKF) method is employed for co-estimation of SOC and model parameters to eliminate the drift noise effects. The SOC estimation root mean square error (RMSE) of the proposed method achieves 0.99% and 1.67% in the pulse constant current test and dynamic discharge test, respectively. Experimental results and comparisons with other methods highlight the SOC estimation accuracy and robustness of the proposed method.

scholarly journals FEED FORWARD NEURAL NETWORK SEBAGAI ALGORITMA ESTIMASI STATE OF CHARGE BATERAI LITHIUM POLYMER

2020 ◽  
Vol 7 (1) ◽  
pp. 13
Author(s):  
Mohammad Imron Dwi Prasetyo ◽  
Anang Tjahjono ◽  
Novie Ayub Windarko
Keyword(s):  
Neural Network ◽  
Smart Grid ◽  
Management System ◽  
Open Circuit Voltage ◽  
Open Circuit ◽  
State Of Charge ◽  
Battery Management System ◽  
Battery Management ◽  
Feed Forward Neural Network ◽  
Feed Forward

<p><em>Estimasi State Of Charge (SOC) baterai merupakan parameter terpenting dalam Battery Management System (BMS), terlebih sebagai aplikasi dari mobil listrik dan smart grid. SOC tidak dapat dilakukan pengukuran secara langsung, sehingga diperlukan metode estimasi untuk mendapatkan nilai tersebut. Beberapa metode yang pernah diusulkan adalah coloumb counting dan open circuit voltage. Akan tetapi coloumb counting memiliki kelemahan dalam hal inisialisasi SOC awal dan memiliki ketergantungan terhadap sensor arus. Sedangkan metode open circuit voltage hanya dapat digunakan pada baterai dalam kondisi idel. Pada penelitian ini diusulkan metode algoritma Feed Forward Neural Network (FFNN) untuk estimasi SOC baterai lithium polymer. Algoritma ini dapat menyelesaikan sistem nonlinier seperti yang dimiliki oleh baterai lithium polymer. Arsitektur FFNN dibangun dua kali (dual neural) untuk estimasi OCV dan SOC. FFNN pertama dengan input tegangan, arus,  dan waktu charging maupun discharging untuk estimasi OCV. OCV hasil training neural pertama digunakan sebagai input FFNN kedua untuk estimasi SOC. Hasil dari estimasi ini didapatkan dengan nilai hidden neuron 11 pada neural pertama dan hidden neuron 4 pada neural kedua.</em></p><p><strong>Keywords:</strong><em> </em><em>SOC, BMS, Coloumb Counting, OCV, FFNN</em></p><p><em>Estimasi State Of Charge (SOC) baterai merupakan parameter terpenting dalam Battery Management System (BMS), terlebih sebagai aplikasi dari mobil listrik dan smart grid. SOC tidak dapat dilakukan pengukuran secara langsung, sehingga diperlukan metode estimasi untuk mendapatkan nilai tersebut. Beberapa metode yang pernah diusulkan adalah coloumb counting dan open circuit voltage. Akan tetapi coloumb counting memiliki kelemahan dalam hal inisialisasi SOC awal dan memiliki ketergantungan terhadap sensor arus. Sedangkan metode open circuit voltage hanya dapat digunakan pada baterai dalam kondisi idel. Pada penelitian ini diusulkan metode algoritma Feed Forward Neural Network (FFNN) untuk estimasi SOC baterai lithium polymer. Algoritma ini dapat menyelesaikan sistem nonlinier seperti yang dimiliki oleh baterai lithium polymer. Arsitektur FFNN dibangun dua kali (dual neural) untuk estimasi OCV dan SOC. FFNN pertama dengan input tegangan, arus,  dan waktu charging maupun discharging untuk estimasi OCV. OCV hasil training neural pertama digunakan sebagai input FFNN kedua untuk estimasi SOC. Hasil dari estimasi ini didapatkan dengan nilai hidden neuron 11 pada neural pertama dan hidden neuron 4 pada neural kedua.</em></p><p><strong>Kata kunci</strong><em>: </em><em>SOC, BMS, Coloumb Counting, OCV, FFNN</em></p><p><em><br /></em></p><p><em><br /></em></p>

scholarly journals A Novel Temperature–Hysteresis Model for Power Battery of Electric Vehicles with an Adaptive Joint Estimator on State of Charge and Power

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3621
Author(s):  
Xu Lei ◽  
Xi Zhao ◽  
Guiping Wang ◽  
Weiyu Liu
Keyword(s):  
Lithium Ion Batteries ◽  
Management System ◽  
Stress Test ◽  
Lithium Ion ◽  
State Of Charge ◽  
Temperature Hysteresis ◽  
Battery Management System ◽  
Battery Management ◽  
Hysteresis Model ◽  
Ambient Temperatures

The battery state of charge (SOC) and state of power (SOP) are two essential parameters in the battery management system. For power lithium-ion batteries, temperature variation and the hysteresis effect are two of the main negative contributions to the accuracy of model-based SOC and SOP estimation. Thereby, a reliable circuit model is established herein to accurately estimate the working state of batteries. Considering the effect that temperature and hysteresis have on the electrical system, a unique fully-coupled temperature–hysteresis model is proposed to describe the interrelationship among capacity, hysteresis voltage, and temperature comprehensively. The key parameters of the proposed model are identified by experiments operated on lithium-ion batteries under varying ambient temperatures. Then we build a multi-state joint estimator to calculate the SOC and SOP on the basis of the temperature–hysteresis model. The effectiveness of the advanced model is verified by experiments at different temperatures. Moreover, the proposed joint estimator is verified by the improved dynamic stress test. The experimental results indicate that the proposed estimator making use of the temperature–hysteresis model can estimate SOC and SOP accurately and robustly. Our results also prove invaluable in terms of the construction of a flexible battery management system for applications in the actual industrial field.

scholarly journals Online Parameters Identification and State of Charge Estimation for Lithium-ion Battery Using Adaptive Cubature Kalman Filter

2021 ◽  
Vol 12 (3) ◽  
pp. 123
Author(s):  
Wei Li ◽  
Maji Luo ◽  
Yaqian Tan ◽  
Xiangyu Cui
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Stress Test ◽  
Model Identification ◽  
Lithium Ion ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Battery Management System ◽  
Soc Estimation ◽  
Cubature Kalman Filter

The state of charge (SOC) of a lithium-ion battery plays a key role in ensuring the charge and discharge energy control strategy, and SOC estimation is the core part of the battery management system for safe and efficient driving of electric vehicles. In this paper, a model-based SOC estimation strategy based on the Adaptive Cubature Kalman filter (ACKF) is studied for lithium-ion batteries. In the present study, the dual polarization (DP) model is employed for SOC estimation and the vector forgetting factor recursive least squares (VRLS) method is utilized for model parameter online identification. The ACKF is then designed to estimate the battery’s SOC. Finally, the Urban Dynamometer Driving Schedule and Dynamic Stress Test are utilized to evaluate the performance of the proposed method by comparing with results obtained using the extended Kalman filter (EKF) and the cubature Kalman filter (CKF) algorithms. The simulation and experimental results show that the proposed ACKF algorithm combined with VRLS-based model identification is a promising SOC estimation approach. The proposed algorithm is found to provide more accurate SOC estimation with satisfying stability than the extended EKF and CKF algorithms.

scholarly journals Online State-of-Charge Estimation Based on the Gas–Liquid Dynamics Model for Li(NiMnCo)O2 Battery

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 324
Author(s):  
Haobin Jiang ◽  
Xijia Chen ◽  
Yifu Liu ◽  
Qian Zhao ◽  
Huanhuan Li ◽  
...  
Keyword(s):  
Stress Test ◽  
Robustness Analysis ◽  
Open Circuit ◽  
Combined Cycle ◽  
State Of Charge ◽  
Constant Current ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Battery Management System ◽  
Liquid Dynamics

Accurately estimating the online state-of-charge (SOC) of the battery is one of the crucial issues of the battery management system. In this paper, the gas–liquid dynamics (GLD) battery model with direct temperature input is selected to model Li(NiMnCo)O2 battery. The extended Kalman Filter (EKF) algorithm is elaborated to couple the offline model and online model to achieve the goal of quickly eliminating initial errors in the online SOC estimation. An implementation of the hybrid pulse power characterization test is performed to identify the offline parameters and determine the open-circuit voltage vs. SOC curve. Apart from the standard cycles including Constant Current cycle, Federal Urban Driving Schedule cycle, Urban Dynamometer Driving Schedule cycle and Dynamic Stress Test cycle, a combined cycle is constructed for experimental validation. Furthermore, the study of the effect of sampling time on estimation accuracy and the robustness analysis of the initial value are carried out. The results demonstrate that the proposed method realizes the accurate estimation of SOC with a maximum mean absolute error at 0.50% in five working conditions and shows strong robustness against the sparse sampling and input error.

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 Estimation of State of Charge (SoC) Using Modified Coulomb Counting Method With Open Circuit Compensation For Battery Management System (BMS)

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Puspita Ningrum ◽  
Novie Ayub Windarko ◽  
Suhariningsih Suhariningsih
Keyword(s):  
Management System ◽  
Voltage Divider ◽  
Open Circuit ◽  
Voltage Sensor ◽  
State Of Charge ◽  
Battery Management System ◽  
Battery Management ◽  
Li Ion Battery ◽  
Control Center ◽  
Li Ion

Abstract— Battery is one of the important components in the development of renewable energy technology. This paper presents a method for estimating the State of Charge (SoC) for a 4Ah Li-ion battery. State of Charge (SoC) is the status of the capacity in the battery in the form of a percentage which makes it easier to monitor the battery during use. Coulomb calculations are widely used, but this method still contains errors during integration. In this paper, SoC measurement using Open Circuit Voltage Compensation is used for the determination of the initial SoC, so that the initial SoC reading is more precise, because if the initial SoC reading only uses a voltage sensor, the initial SoC reading is less precise which affects the next n second SoC reading. In this paper, we present a battery management system design or commonly known as BMS (Battery Management System) which focuses on the monitoring function. BMS uses a voltage sensor in the form of a voltage divider circuit and an ACS 712 current sensor to send information about the battery condition to the microcontroller as the control center. Besides, BMS is equipped with a protection relay to protect the battery. The estimation results of the 12volt 4Ah Li-ion battery SoC with the actual reading show an error of less than 1%.Keywords—Battery Management System, Modified Coulomb Counting, State of Charge.

Sign in / Sign up

Export Citation Format

Share Document