EPE Association: EPE 2023 - LS3d: Battery Management Systems, Monitoring and Life-time Prediction

EPE 2023 - LS3d: Battery Management Systems, Monitoring and Life-time Prediction
You are here: EPE Documents > 01 - EPE & EPE ECCE Conference Proceedings > EPE 2023 ECCE Europe - Conference > EPE 2023 - Topic 08: Electric Vehicle Propulsion Systems and their Energy Storage > EPE 2023 - LS3d: Battery Management Systems, Monitoring and Life-time Prediction

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   Adaptive configuration of generalized nonlinear ECM of Li-ion batteries based on impedance measurements and DRT analysis
By Jussi SIHVO, Vaclav KNAP, Tomi ROINILA, Daniel-Ioan STROE [View]
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Abstract: An adaptive approach for configuration of generalized battery nonlinear equivalent-circuit-model (ECM) is proposed. In the approach, the distribution-relaxation-times (DRT) analysis is used to configure and initialize the ECM to be fitted to the impedance data. The performance of the approach is validated and analyzed by using experimental battery impedance measurements.

   Data-Driven Methods for Robust Battery Capacity Estimation based on Electrochemical Impedance Spectroscopy
By Zhansheng NING, Prasanth VENUGOPAL, Gert RIETVELD, Thiago BATISTA SOEIRO [View]
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Abstract: To ensure accurate battery capacity estimation over the battery life time, it is important to extract those features from battery data sets that give a good indication of battery capacity degradation. Data obtained from electrochemical impedance spectroscopy (EIS) are a promising route for detecting different aging effects. Many present methods for extracting battery aging features from EIS data are unsuitable for cells that have very different aging behaviour, which leads to low robustness in the battery capacity estimation. To improve battery capacity estimation of cells with significantly different aging behaviour, two methods for feature detection and consistency analysis are proposed for finding the high aging-correlated features in EIS data of these cells. A novel feature-consistency coefficient is proposed to assess whether the detected features are suitable for use in capacity determination. Based on the two new features that are found using this approach on a published data set of 8 battery cells with significantly inconsistent aging behavior, a capacity estimation is subsequently carried out using several advanced machine learning (ML) techniques, using Gaussian process regression (GPR) and Support vector machine (SVM) models. It appears that a third ML method based on automatic feature extraction and capacity estimation using convolution neural networks (CNNs) gives the best, most robust capacity estimation result. All methods presented in this paper significantly outperform GPR-based estimations published in the literature.

   Online Measurement Method of Electrochemical Impedance of Electric Vehicle Battery Based on Three-Phase Motor Drive Inverter
By Boyang LI, Hongyan QU, Dong JIANG, Min ZHOU [View]
[Download]

Abstract: This paper introduces a novel method to identify electrochemical impedance spectrum (EIS) for electric vehicle batteries. The drive inverter generates AC signal required for EIS identification while the motor is operated unaffected. Flexibility and low cost are the biggest advantages. Experiments have verified the effectiveness of this method.

EPE 2023 - LS3d: Battery Management Systems, Monitoring and Life-time Prediction
You are here: EPE Documents > 01 - EPE & EPE ECCE Conference Proceedings > EPE 2023 ECCE Europe - Conference > EPE 2023 - Topic 08: Electric Vehicle Propulsion Systems and their Energy Storage > EPE 2023 - LS3d: Battery Management Systems, Monitoring and Life-time Prediction
	[return to parent folder]

	Adaptive configuration of generalized nonlinear ECM of Li-ion batteries based on impedance measurements and DRT analysis By Jussi SIHVO, Vaclav KNAP, Tomi ROINILA, Daniel-Ioan STROE	[View] [Download]
Abstract: An adaptive approach for configuration of generalized battery nonlinear equivalent-circuit-model (ECM) is proposed. In the approach, the distribution-relaxation-times (DRT) analysis is used to configure and initialize the ECM to be fitted to the impedance data. The performance of the approach is validated and analyzed by using experimental battery impedance measurements.

	Data-Driven Methods for Robust Battery Capacity Estimation based on Electrochemical Impedance Spectroscopy By Zhansheng NING, Prasanth VENUGOPAL, Gert RIETVELD, Thiago BATISTA SOEIRO	[View] [Download]
Abstract: To ensure accurate battery capacity estimation over the battery life time, it is important to extract those features from battery data sets that give a good indication of battery capacity degradation. Data obtained from electrochemical impedance spectroscopy (EIS) are a promising route for detecting different aging effects. Many present methods for extracting battery aging features from EIS data are unsuitable for cells that have very different aging behaviour, which leads to low robustness in the battery capacity estimation. To improve battery capacity estimation of cells with significantly different aging behaviour, two methods for feature detection and consistency analysis are proposed for finding the high aging-correlated features in EIS data of these cells. A novel feature-consistency coefficient is proposed to assess whether the detected features are suitable for use in capacity determination. Based on the two new features that are found using this approach on a published data set of 8 battery cells with significantly inconsistent aging behavior, a capacity estimation is subsequently carried out using several advanced machine learning (ML) techniques, using Gaussian process regression (GPR) and Support vector machine (SVM) models. It appears that a third ML method based on automatic feature extraction and capacity estimation using convolution neural networks (CNNs) gives the best, most robust capacity estimation result. All methods presented in this paper significantly outperform GPR-based estimations published in the literature.

	Online Measurement Method of Electrochemical Impedance of Electric Vehicle Battery Based on Three-Phase Motor Drive Inverter By Boyang LI, Hongyan QU, Dong JIANG, Min ZHOU	[View] [Download]
Abstract: This paper introduces a novel method to identify electrochemical impedance spectrum (EIS) for electric vehicle batteries. The drive inverter generates AC signal required for EIS identification while the motor is operated unaffected. Flexibility and low cost are the biggest advantages. Experiments have verified the effectiveness of this method.