A simple and upgradable autonomous battery aging evaluation and test system with capacity fading and AC impedance spectroscopy measurement

Xia, Zhiyong; Qahouq, Jaber A. Abu; Phillips, Evan; Gentry, Rachel H.

doi:10.1109/apec.2017.7930811

Cited by 15 publications

(6 citation statements)

References 12 publications

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“…After the initial capacity calibration, the battery cell is subjected to 30 ageing cycles using the developed autonomous ageing platform. The details about this ageing platform can be found in [2] . During each ageing cycle, the battery cell goes through one full CC discharging operation and one complete CC—CV charging operation.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Ageing characterization data of lithium-ion battery with highly deteriorated state and wide range of state-of-health

Xia

Qahouq

2022

Data in Brief

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This paper presents ageing characterization data of two lithium-ion battery cells that have been put through a deep ageing process. At the end of ageing process, the values of state-of-health (SOH) of the battery cells drop down to around 15%. The battery cells are aged using a developed autonomous ageing platform which performs functions such as constant current (CC) discharging, CC charging, and constant voltage (CV) charging. Each time the battery cell completes 30 ageing cycles, battery performance tests including dc impedance measurement, minimum impedance measurement, and capacity calibration are conducted to characterize the ageing or health status of the battery cell. The collected battery dc impedance data, minimum impedance data, capacity data, and CC—CV charging time for the deeply aged battery cells are presented in this paper. The presented data has the potential to help in identifying battery ageing behavior patterns. It can also be utilized to investigate the correlation or relationship between different battery ageing characterization data and to develop SOH estimation methods for lithium-ion batteries with high degradation conditions, for examples, for second-use battery and when battery health exhibits unexpected faster deterioration.

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Ageing characterization data of lithium-ion battery with highly deteriorated state and wide range of state-of-health

Xia

Qahouq

2022

Data in Brief

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“…The initial capacity of the battery cell is calibrated at the beginning before it goes through the ageing process. Then the battery is subjected to 30 ageing cycles using the ageing platform presented in [10]. The adopted ageing protocol is illustrated in Fig.…”

Section: Battery Ageing Protocol and Testing Proceduresmentioning

confidence: 99%

“…Each time the battery completes 30 ageing cycles, the battery is put into rest state for 12 hours in order to reach the equilibrium state before capacity calibration. More details about the ageing platform can be found in [10]. The reason why battery capacity is calibrated every 30 ageing cycles is to avoid fluctuations in the battery capacity data as the developed SOH estimators aim to predict the long-term degradation of battery instead of per cycle local variation in battery health.…”

Section: Battery Ageing Protocol and Testing Proceduresmentioning

confidence: 99%

“…Currently, because EVs are becoming increasingly popular all over the world, the performance requirements of Lithium-ion battery system are becoming more stringent since it is a critical part of EVs. In general, EVs require batteries with lower maintenance cost and higher reliability in addition to higher density [3][4][5][6][7][8][9][10]. Developing high performance and advanced battery management systems (BMS) is an important step toward meeting the increasing performance demands from the applications of battery systems .…”

Section: Introductionmentioning

confidence: 99%

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Lithium-Ion Battery Ageing Behavior Pattern Characterization and State-of-Health Estimation Using Data-Driven Method

Xia

Qahouq

2021

IEEE Access

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This paper presents a study on Lithium-ion battery aging behaviors/patterns and related Stateof-Health (SOH) indicators before presenting the development of data-driven based SOH estimators. Battery charge/discharge cycling experiments are conducted in order to obtain needed data for this work. The battery ageing behavior patterns until the battery cell reaches highly deteriorated health conditions are investigated and characterized in this paper by analyzing the aggregated battery ageing data. The observed battery ageing behavior patterns include: (1) the rate at which the battery voltage decreases during discharging increases as the battery ages, (2) the speed at which the battery terminal voltage increases during Constant Current (CC) charging increases as the battery's health deteriorates, (3) the time period for CC charging operation decreases as the battery ages, (4) the rate at which the battery current decreases during Constant Voltage (CV) charging increases as the battery ages, and (5) the speed at which the battery temperature drops during CV charging increases as the battery ages. Corresponding SOH indicators are developed to quantify these battery ageing behavior patterns for the development of SOH estimators. Deep Neural Network (DNN) is utilized to extract and model the nonlinear and complex correlation between the defined SOH indicators and SOH values of the Lithium-ion battery. Multiple DNN-based SOH estimators are developed in this paper. The SOH estimation results from different DNN-based SOH estimators indicate that the diversity of SOH indicators used for the development of SOH estimator can substantially improve the estimation performance.

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“…A model is obtained by approximating parameters using a lookup table for 29 SOC values for both the original and the filtered data and with the same initial conditions, obtaining greater similarity between the filtered data modeled and the original data than with the original data modeled with its unmodeled equivalent. To adjust the value of the consumption or discharge current, an electronic load such as the one developed in [26] is used, to determine the characteristics of a solar panel, this idea can also be used to adjust the consumption of a battery to a certain value in an experimental process [27]. Improvements in the estimation of the Kalman model are achieved by using a filter that considers system and sensor noise, since the modeled and filtered signal is closer to the original signal than the unfiltered modeled signal.…”

Section: Introductionmentioning

confidence: 99%

Kalman filter applied to Thevenin’s modeling of a lead-acid battery

Palacio-Fernádez

Quintero

2022

IJECE

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This article determines the internal parameters of a battery analyzed from its circuit equivalent, reviewing important information that can help to identify the battery’s state of charge (SOC) and its state of health (SOH). Although models that allow the dynamics of different types of batteries to be identified have been developed, few have defined the lead-acid battery model from the analysis of a filtered signal by applying a Kalman filter, particularly taking into account the measurement of noise not just at signal output but also at its input (this is a novelty raised from the experimental). This study proposes a model for lead-acid batteries using tools such as MATLAB® and Simulink®. First, a method of filtering the input and output signal is presented, and then a method for identifying parameters from 29 charge states is used for a lead-acid battery. Different SOCs are related to different values of open circuit voltage (OCV). Ultimately, improvements in model estimation are shown using a filter that considers system and sensor noise since the modeled and filtered signal is closer to the original signal than the unfiltered modeled signal.

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A simple and upgradable autonomous battery aging evaluation and test system with capacity fading and AC impedance spectroscopy measurement

Cited by 15 publications

References 12 publications

Ageing characterization data of lithium-ion battery with highly deteriorated state and wide range of state-of-health

Ageing characterization data of lithium-ion battery with highly deteriorated state and wide range of state-of-health

Lithium-Ion Battery Ageing Behavior Pattern Characterization and State-of-Health Estimation Using Data-Driven Method

Kalman filter applied to Thevenin’s modeling of a lead-acid battery

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