scholarly journals Determination of Optimal Indicators Based on Statistical Analysis for the State of Health Estimation of a Lithium-Ion Battery

2021 ◽  
Vol 9 ◽  
Author(s):  
Gaoya Shi ◽  
Siqi Chen ◽  
Hao Yuan ◽  
Heze You ◽  
Xueyuan Wang ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Ambient Temperature ◽  
Estimation Method ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Least Square ◽  
Model Parameters ◽  
Battery Management System ◽  
State Of Health ◽  
Recursive Least Square

Online state of health (SOH) estimation is essential for lithium-ion batteries in a battery management system. As the conventional SOH indicator, the capacity is challenging to be estimated online. Apart from the capacity, various indicators related to the internal resistance are proposed as indicators for the SOH estimation. However, research gaps still exist in terms of optimal resistance-related indicators, online acquisition of indicators, temperature disturbance elimination, and state of charge (SOC) disturbance elimination. In this study, the equivalent circuit model parameters are identified based on recursive least square method in dynamic working conditions in the life span. Statistical analysis methods including multiple stepwise regression analysis and path analysis are introduced to characterize the sensitivity of the parameters to SOH estimation. Based on the above approach, the coupling relationship between the parameters is comprehensively analyzed. Results indicate that the ohmic resistance R0 and the diffusion capacitance Cd are the most suitable parameters for the SOH indication. Furthermore, R0 and Cd are proved to be exponentially correlated to the ambient temperature, while SOC demonstrates a quadratic trend on them. To eliminate the disturbance caused by the ambient temperature and SOC, a compensating method is further proposed. Finally, a mapping relationship between SOH and the indicators under normal operations is established. SOH can be estimated with the maximum error of 2.301%, which proves the reliability and feasibility of the proposed indicators and estimation method.

scholarly journals Adaptive Forgetting Factor Recursive Least Square Algorithm for Online Identification of Equivalent Circuit Model Parameters of a Lithium-Ion Battery

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2242 ◽  
Author(s):  
Xiangdong Sun ◽  
Jingrun Ji ◽  
Biying Ren ◽  
Chenxue Xie ◽  
Dan Yan
Keyword(s):  
Lithium Ion Battery ◽  
Equivalent Circuit ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Circuit Model ◽  
Least Square ◽  
Model Parameters ◽  
Recursive Least Square ◽  
Forgetting Factor ◽  
Online Identification

With the popularity of electric vehicles, lithium-ion batteries as a power source are an important part of electric vehicles, and online identification of equivalent circuit model parameters of a lithium-ion battery has gradually become a focus of research. A second-order RC equivalent circuit model of a lithium-ion battery cell is modeled and analyzed in this paper. An adaptive expression of the variable forgetting factor is constructed. An adaptive forgetting factor recursive least square (AFFRLS) method for online identification of equivalent circuit model parameters is proposed. The equivalent circuit model parameters are identified online on the basis of the dynamic stress testing (DST) experiment. The online voltage prediction of the lithium-ion battery is carried out by using the identified circuit parameters. Taking the measurable actual terminal voltage of a single battery cell as a reference, by comparing the predicted battery terminal voltage with the actual measured terminal voltage, it is shown that the proposed AFFRLS algorithm is superior to the existing forgetting factor recursive least square (FFRLS) and variable forgetting factor recursive least square (VFFRLS) algorithms in accuracy and rapidity, which proves the feasibility and correctness of the proposed parameter identification algorithm.

scholarly journals A State of Health Estimation Method for Lithium-Ion Batteries Based on Voltage Relaxation Model

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1349 ◽  
Author(s):  
Qiaohua Fang ◽  
Xuezhe Wei ◽  
Tianyi Lu ◽  
Haifeng Dai ◽  
Jiangong Zhu
Keyword(s):  
Estimation Error ◽  
Estimation Method ◽  
Lithium Ion ◽  
The State ◽  
Estimation Methods ◽  
Model Parameters ◽  
Battery Management System ◽  
Relaxation Model ◽  
State Of Health ◽  
Capacity Estimation

The state of health estimation for lithium-ion battery is a key function of the battery management system. Unlike the traditional state of health estimation methods under dynamic conditions, the relaxation process is studied and utilized to estimate the state of health in this research. A reasonable and accurate voltage relaxation model is established based on the linear relationship between time coefficient and open circuit time for a Li1(NiCoAl)1O2-Li1(NiCoMn)1O2/graphite battery. The accuracy and effectiveness of the model is verified under different states of charge and states of health. Through systematic experiments under different states of charge and states of health, it is found that the model parameters monotonically increase with the aging of the battery. Three different capacity estimation methods are proposed based on the relationship between model parameters and residual capacity, namely the α-based, β-based, and parameter–fusion methods. The validation of the three methods is verified with high accuracy. The results indicate that the capacity estimation error under most of the aging states is less than 1%. The largest error drops from 3% under the α-based method to 1.8% under the parameter–fusion method.

scholarly journals Influence of Sampling Delay on the Estimation of Lithium-Ion Battery Parameters and an Optimized Estimation Method

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1878
Author(s):  
Bing Jiang ◽  
Zeqi Chen ◽  
Feifan Chen
Keyword(s):  
Estimation Method ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Test System ◽  
Recursive Least Squares ◽  
Battery Management System ◽  
Battery Management ◽  
First Order ◽  
The Mathematical Model ◽  
Synchronization Accuracy

The equivalent-circuit model (ECM) is widely used in online estimating the parameters and states of lithium-ion batteries. However, the sampling delay between the voltage and current of a battery is generally overlooked, which is unavoidable in a modular battery management system (BMS) and would lead to wrong results in the estimation of battery parameters and states. In this paper, with the first-order resistor–capacitor (RC) model as our battery model, we analyze the influence mechanism of sampling delay and then propose an optimized method for online estimating battery parameters. The mathematical model derived from the first-order RC model and the approximation method of first-order derivative are optimized. The recursive least squares (RLS) algorithm is used for identifying the parameters of the model. In order to verify the proposed method, a modular battery test system with high sampling frequency and high synchronization accuracy is developed. The experiment results indicate that the sampling delay would cause the estimation process to fluctuate, and the optimized method effectively improves the tolerance range of sampling delay.

scholarly journals Online State of Charge Estimation for Lithium-Ion Battery by Combining Incremental Autoregressive and Moving Average Modeling with Adaptive H-Infinity Filter

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Zheng Liu ◽  
Xuanju Dang ◽  
Hanxu Sun
Keyword(s):  
Moving Average ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
State Of Charge ◽  
Parameters Identification ◽  
Estimation Methods ◽  
Model Parameters ◽  
Battery Management System ◽  
H Infinity ◽  
Soc Estimation

The state of charge (SOC) estimation is one of the most important features in battery management system (BMS) for electric vehicles (EVs). In this article, a novel equivalent-circuit model (ECM) with an extra noise sequence is proposed to reduce the adverse effect of model error. Model parameters identification method with variable forgetting factor recursive extended least squares (VFFRELS), which combines a constructed incremental autoregressive and moving average (IARMA) model with differential measurement variables, is presented to obtain the ECM parameters. The independent open circuit voltage (OCV) estimator with error compensation factors is designed to reduce the OCV error of OCV fitting model. Based on the IARMA battery model analysis and the parameters identification, an SOC estimator by adaptive H-infinity filter (AHIF) is formulated. The adaptive strategy of the AHIF improves the numerical stability and robust performance by synchronous adjusting noise covariance and restricted factor. The results of experiment and simulation have verified that the proposed approach has superior advantage of parameters identification and SOC estimation to other estimation methods.

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.

State-of-Health Online Estimation for Li-Ion Battery

2020 ◽  
Vol 9 (2) ◽  
pp. 185-196
Author(s):  
Liu Fang ◽  
◽  
Liu Xinyi ◽  
Su Weixing ◽  
Chen Hanning ◽  
...  
Keyword(s):  
Estimation Method ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Estimation Methods ◽  
Battery Management ◽  
State Of Health ◽  
Li Ion Battery ◽  
Improved Genetic Algorithm ◽  
Battery Management Systems ◽  
Li Ion

To realize a fast and high-precision online state-of-health (SOH) estimation of lithium-ion (Li-Ion) battery, this article proposes a novel SOH estimation method. This method consists of a new SOH model and parameters identification method based on an improved genetic algorithm (Improved-GA). The new SOH model combines the equivalent circuit model (ECM) and the data-driven model. The advantages lie in keeping the physical meaning of the ECM while improving its dynamic characteristics and accuracy. The improved-GA can effectively avoid falling into a local optimal problem and improve the convergence speed and search accuracy. So the advantages of the SOH estimation method proposed in this article are that it only relies on battery management systems (BMS) monitoring data and removes many assumptions in some other traditional ECM-based SOH estimation methods, so it is closer to the actual needs for electric vehicle (EV). By comparing with the traditional ECM-based SOH estimation method, the algorithm proposed in this article has higher accuracy, fewer identification parameters, and lower computational complexity.

scholarly journals Online Parameter Identification of Lithium-Ion Batteries Using a Novel Multiple Forgetting Factor Recursive Least Square Algorithm

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3180 ◽  
Author(s):  
Bizhong Xia ◽  
Rui Huang ◽  
Zizhou Lao ◽  
Ruifeng Zhang ◽  
Yongzhi Lai ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Lithium Ion ◽  
Least Square ◽  
Estimation Methods ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Recursive Least Square ◽  
Forgetting Factor ◽  
Temperature State

The model parameters of the lithium-ion battery are of great importance to model-based battery state estimation methods. The fact that parameters change in different rates with operation temperature, state of charge (SOC), state of health (SOH) and other factors calls for an online parameter identification algorithm that can track different dynamic characters of the parameters. In this paper, a novel multiple forgetting factor recursive least square (MFFRLS) algorithm was proposed. Forgetting factors were assigned to each parameter, allowing the algorithm to capture the different dynamics of the parameters. Particle swarm optimization (PSO) was utilized to determine the optimal forgetting factors. A state of the art SOC estimator, known as the unscented Kalman filter (UKF), was combined with the online parameter identification to create an accurate estimation of SOC. The effectiveness of the proposed method was verified through a driving cycle under constant temperature and three different driving cycles under varied temperature. The single forgetting factor recursive least square (SFFRLS)-UKF and UKF with fixed parameter were also tested for comparison. The proposed MFFRLS-UKF method obtained an accurate estimation of SOC especially when the battery was running in an environment of changing temperature.

scholarly journals Evaluation and Comparative Study of Cell Balancing Methods for Lithium-Ion Batteries Used in Electric Vehicles

2021 ◽  
Vol 10 (3) ◽  
pp. 471-479
Author(s):  
Thiruvonasundari Duraisamy ◽  
Kaliyaperumal Deepa
Keyword(s):  
Electric Vehicles ◽  
Lithium Ion ◽  
Least Square ◽  
Battery Life ◽  
Model Parameters ◽  
Battery Management System ◽  
Battery Management ◽  
Renewable Energy Resources ◽  
Voltage Deviation ◽  
Cell Balancing

Vehicle manufacturers positioned electric vehicles (EVs) and hybrid electric vehicles (HEVs) as reliable, safe and environmental friendly alternative to traditional fuel based vehicles. Charging EVs using renewable energy resources reduce greenhouse emissions. The Lithium-ion (Li-ion) batteries used in EVs are susceptible to failure due to voltage imbalance when connected to form a pack. Hence, it requires a proper balancing system categorised into passive and active systems based on the working principle. It is the prerogative of a battery management system (BMS) designer to choose an appropriate system depending on the application. This study compares and evaluates passive balancing system against widely used inductor based active balancing system in order to select an appropriate balancing scheme addressing battery efficiency and balancing speed for E-vehicle segment (E-bike, E-car and E-truck). The balancing systems are implemented using “top-balancing” algorithm which balance the cells voltages near the end of charge for better accuracy and effective balancing. The most important characteristics of the balancing systems such as degree of imbalance, power loss and temperature variation are determined by their influence on battery performance and cost. To enhance the battery life, Matlab-Simscape simulation-based analysis is performed in order to fine tune the cell balancing system for the optimal usage of the battery pack. For the simulation requirements, the battery model parameters are obtained using least-square fitting algorithm on the data obtained through electro chemical impedance spectroscopy (EIS) test. The achieved balancing time of the passive and active cell balancer for fourteen cells were 48 and 20 min for the voltage deviation of 30 mV. Also, the recorded balancing time was 215 and 42 min for the voltage deviation of 200 mV.

scholarly journals Evaluation and Comparative Study of Cell Balancing Methods for Lithium-Ion Batteries Used in Electric Vehicles

2021 ◽  
Vol 10 (3) ◽  
pp. 471-479
Author(s):  
Thiruvonasundari Duraisamy ◽  
Kaliyaperumal Deepa
Keyword(s):  
Electric Vehicles ◽  
Lithium Ion ◽  
Least Square ◽  
Battery Life ◽  
Model Parameters ◽  
Battery Management System ◽  
Battery Management ◽  
Renewable Energy Resources ◽  
Voltage Deviation ◽  
Cell Balancing

Vehicle manufacturers positioned electric vehicles (EVs) and hybrid electric vehicles (HEVs) as reliable, safe and environmental friendly alternative to traditional fuel based vehicles. Charging EVs using renewable energy resources reduce greenhouse emissions. The Lithium-ion (Li-ion) batteries used in EVs are susceptible to failure due to voltage imbalance when connected to form a pack. Hence, it requires a proper balancing system categorised into passive and active systems based on the working principle. It is the prerogative of a battery management system (BMS) designer to choose an appropriate system depending on the application. This study compares and evaluates passive balancing system against widely used inductor based active balancing system in order to select an appropriate balancing scheme addressing battery efficiency and balancing speed for E-vehicle segment (E-bike, E-car and E-truck). The balancing systems are implemented using “top-balancing” algorithm which balance the cells voltages near the end of charge for better accuracy and effective balancing. The most important characteristics of the balancing systems such as degree of imbalance, power loss and temperature variation are determined by their influence on battery performance and cost. To enhance the battery life, Matlab-Simscape simulation-based analysis is performed in order to fine tune the cell balancing system for the optimal usage of the battery pack. For the simulation requirements, the battery model parameters are obtained using least-square fitting algorithm on the data obtained through electro chemical impedance spectroscopy (EIS) test. The achieved balancing time of the passive and active cell balancer for fourteen cells were 48 and 20 min for the voltage deviation of 30 mV. Also, the recorded balancing time was 215 and 42 min for the voltage deviation of 200 mV.

Sign in / Sign up

Export Citation Format

Share Document