A novel feedback correction-adaptive Kalman filtering method for the whole-life-cycle state of charge and closed-circuit voltage prediction of lithium-ion batteries based on the second-order electrical equivalent circuit model

Wang, Shunli; Takyi‐Aninakwa, Paul; Fan, Yongcun; Yu, Chunmei; Jin, Siyu; Fernandez, Carlos; Stroe, Daniel‐Ioan

doi:10.1016/j.ijepes.2022.108020

Cited by 46 publications

(9 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gaining a comprehensive understanding of the internal performance conditionswhich is typically based on the equivalent circuit model-is the BMS's primary goal. The relationship between parameter identification results and their influencing factors, such as ambient temperature, charge-discharge rate, SOC, and SOH, has been the subject of extensive investigation under the proposed idea [26][27][28][29][30]. The results of the investigation in this work, however, show that the choice of the sample interval also has a significant impact on the results of parameter identification, which has been disregarded in recent research.…”

Section: Discussionmentioning

confidence: 99%

Effect of Sample Interval on the Parameter Identification Results of RC Equivalent Circuit Models of Li-ion Battery: An Investigation Based on HPPC Test Data

Zhang

Deng²,

Zong³

et al. 2022

Batteries

View full text Add to dashboard Cite

The validity of the equivalent circuit model (ECM), which is crucial for the development of lithium-ion batteries (LIBs) and state evaluation, is primarily dependent on the precision of the findings of parameter identification. In this study, the commonly used first-order RC (1-RC) circuit and second-order RC (2-RC) circuit models were selected for parameter identification. A time series of voltage with different sample intervals were used for function fitting based on the least square method, which were extracted from the hybrid pulse power characteristic (HPPC) test data of a commercial square punch LIB, and the sample intervals were set to be 0.1 s, 0.2 s, 0.5 s, and 1.0 s to evaluate the effect of sample interval on the parameter identification results. When the sample interval is more than 0.5 s, the results reveal that the 2-RC circuit model’s goodness of fit marginally declines, and for some data scenarios, the bias between the fitted terminal voltage curve and test curve increases obviously. With all of the sample intervals under consideration, the 1-RC circuit model’s imitative effect is satisfactory. This work demonstrates that the sample interval of data samples, in addition to the method itself, affects the accuracy and robustness of parameter identification, with the 1-RC circuit model showing larger advantages under low sample frequency compared to the 2-RC circuit model.

show abstract

Section: Discussionmentioning

confidence: 99%

Effect of Sample Interval on the Parameter Identification Results of RC Equivalent Circuit Models of Li-ion Battery: An Investigation Based on HPPC Test Data

Zhang

Deng²,

Zong³

et al. 2022

Batteries

View full text Add to dashboard Cite

show abstract

“…Then, the neural network only adopts the mapping relation of the system input to its output to develop a model, and yet it is trained based on large volumes of test data to predict the SOC of batteries precisely. [18,19] However, the model-based Kalman filtering series approach can estimate SOC accurately even if the initial SOC value is inaccurate, thus the approach is employed widely in battery SOC estimation. [20,21] Sun et al [22] propose a joint approach based on the Thevenin model for estimating SOC, which combines the forgetting factor recursive least squares (FFRLS) method with the EKF algorithm.…”

Section: Doi: 101002/ente202201364mentioning

confidence: 99%

State‐of‐Charge Estimation of Lithium‐Ion Battery Based on an Improved Dual‐Polarization Model

et al. 2023

View full text Add to dashboard Cite

This article proposes a state‐of‐charge (SOC) estimation method to eliminate the influence of the hysteresis effect and the ambient temperature. First, an improved dual‐polarization (DP) model considering the hysteresis effect and the ambient temperature is established. A hysteresis voltage source is connected in series with a couple of resistance–capacitance pairs in the improved DP model, all the parameters of which are related to the ambient temperature to depict the temperature characteristics of the battery. Second, the forgetting factor recursive least squares method is utilized to identify the parameters under the battery dynamic test data at different temperatures. The proposed model and parameterization scheme integrate the effects of hysteresis and temperature, greatly enhancing the performance of the proposed method at different temperatures. Finally, an extended Kalman filter algorithm for SOC estimation is adopted to verify the improved DP model and the simulation indicates that the error of SOC estimation is within 1.5% at different ambient temperatures. The proposed method can improve the precision of the SOC estimation even if the temperature is below −10 or above 50 °C.

show abstract

“…Common methods include the Ah integral, the OCV, the data-driven, and the model-based methods [9]. It must be taken into account that the lithium-ion battery is affected by other factors during the charging and discharging process, such as the ambient temperature, the magnitude of the current rate, and the aging level [10]. Therefore, a more accurate calculation expression is proposed, as shown in Equation (2).…”

Section: Overview Of the State-of-charge Predictionmentioning

confidence: 99%

“…Then, a full connection layer containing neurons is used on top of the future prediction network. This expression is described as a mathematical function, as shown in Equation (10).…”

Section: Mathematical Modeling Of Deep Learningmentioning

confidence: 99%

A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries

et al. 2022

Self Cite

View full text Add to dashboard Cite

Lithium-ion batteries are widely used as effective energy storage and have become the main component of power supply systems. Accurate battery state prediction is key to ensuring reliability and has significant guidance for optimizing the performance of battery power systems and replacement. Due to the complex and dynamic operations of lithium-ion batteries, the state parameters change with either the working condition or the aging process. The accuracy of online state prediction is difficult to improve, which is an urgent issue that needs to be solved to ensure a reliable and safe power supply. Currently, with the emergence of artificial intelligence (AI), battery state prediction methods based on data-driven methods have high precision and robustness to improve state prediction accuracy. The demanding characteristics of test time are reduced, and this has become the research focus in the related fields. Therefore, the convolutional neural network (CNN) was improved in the data modeling process to establish a deep convolutional neural network ensemble transfer learning (DCNN-ETL) method, which plays a significant role in battery state prediction. This paper reviews and compares several mathematical DCNN models. The key features are identified on the basis of the modeling capability for the state prediction. Then, the prediction methods are classified on the basis of the identified features. In the process of deep learning (DL) calculation, specific criteria for evaluating different modeling accuracy levels are defined. The identified features of the state prediction model are taken advantage of to give relevant conclusions and suggestions. The DCNN-ETL method is selected to realize the reliable state prediction of lithium-ion batteries.

show abstract

A novel feedback correction-adaptive Kalman filtering method for the whole-life-cycle state of charge and closed-circuit voltage prediction of lithium-ion batteries based on the second-order electrical equivalent circuit model

Cited by 46 publications

References 62 publications

Effect of Sample Interval on the Parameter Identification Results of RC Equivalent Circuit Models of Li-ion Battery: An Investigation Based on HPPC Test Data

Effect of Sample Interval on the Parameter Identification Results of RC Equivalent Circuit Models of Li-ion Battery: An Investigation Based on HPPC Test Data

State‐of‐Charge Estimation of Lithium‐Ion Battery Based on an Improved Dual‐Polarization Model

A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries

Contact Info

Product

Resources

About