A Novel Internal Resistance Curve Based State of Health Method to Estimate Battery Capacity Fade and Resistance Rise

The effect of the charge/discharge profile on battery durability is a critical factor for the application of batteries and for the design of appropriate battery testing protocols. In this work, commercial high-power prismatic lithium ion cells for hybrid electric vehicles (HEVs) were cycled using a pulse-heavy profile and a simple square-wave profile to investigate the effect of cycle profile on battery durability. The pulse-heavy profile was designed to simulate on-road conditions for a typical HEV, while the simplified square-wave profile was designed to have the same total charge throughput, but with lower peak currents. The 5 Ah batteries were cycled for 100 kAh with periodic performance tests to monitor the state of the batteries. Results indicate that, for the batteries tested, the capacity fade for the two profiles was very similar and was 11±0.5% compared to beginning of life (BOL). The change in internal resistance of the batteries during testing was also monitored and found to increase 21% and 12% compared to BOL for the pulse-heavy and square-wave profiles, respectively. The results suggest that simplified testing protocols using square-wave cycling may provide adequate insight into capacity fade behavior for more complex hybrid vehicle drive cycles.

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Capacity Fade Estimation of a Lithium-Ion Battery Through an Integrated Electrochemical Battery Model and Empirical Cycle Aging Model

Volume 7A: Dynamics, Vibration, and Control ◽

10.1115/imece2020-24146 ◽

2020 ◽

Author(s):

Sohel Anwar

Keyword(s):

Electric Vehicle ◽

Constant Current ◽

Capacity Fade ◽

State Of Health ◽

Current Profile ◽

Combined Model ◽

Battery Model ◽

Electric Vehicle Battery ◽

Electrochemical Battery ◽

Constant Current Charge

Abstract An electrochemical model based capacity fade estimation method for a Li-Ion battery is investigated in this paper. An empirical capacity fade model for estimating the state of health of a LiFePO4 electric vehicle battery was integrated with electrochemical battery model in Matlab/Simulink platform. This combined model was then validated against experimental data reported in the literature for constant current charge / discharge cycling. An HPPC current profile was then applied to the validated electrochemical-empirical battery prognosis model which reflected a real-time operating condition for charge and discharge current fluctuations in an electric vehicle battery. The combined model was simulated under the two different HPPC current inputs for three different cycle times. Additionally temperature was taken in account in estimating the cycle aging under the applied current profile to assess the present capacity remaining in the battery. The simulation results provided the state of health (SOH) of the battery for these cycling times which were comparable to the published experimental SOH values for constant current charge/discharge profiles. Thus this model can potentially be used to predict the capacity fade status of an electric vehicle battery.

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State-of-Health Estimate for the Lithium-Ion Battery Using Chi-Square and ELM-LSTM

World Electric Vehicle Journal ◽

10.3390/wevj12040228 ◽

2021 ◽

Vol 12 (4) ◽

pp. 228

Author(s):

Jianfeng Jiang ◽

Shaishai Zhao ◽

Chaolong Zhang

Keyword(s):

Neural Network ◽

Lithium Ion Battery ◽

Short Term Memory ◽

Lithium Ion ◽

Constant Current ◽

State Of Health ◽

Chi Square ◽

Mean Temperature ◽

Battery Capacity ◽

Learning Machine

The state-of-health (SOH) estimation is of extreme importance for the performance maximization and upgrading of lithium-ion battery. This paper is concerned with neural-network-enabled battery SOH indication and estimation. The insight that motivates this work is that the chi-square of battery voltages of each constant current-constant voltage phrase and mean temperature could reflect the battery capacity loss effectively. An ensemble algorithm composed of extreme learning machine (ELM) and long short-term memory (LSTM) neural network is utilized to capture the underlying correspondence between the SOH, mean temperature and chi-square of battery voltages. NASA battery data and battery pack data are used to demonstrate the estimation procedures and performance of the proposed approach. The results show that the proposed approach can estimate the battery SOH accurately. Meanwhile, comparative experiments are designed to compare the proposed approach with the separate used method, and the proposed approach shows better estimation performance in the comparisons.

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Automated feature extaction and selection for data-driven models of rapid battery capacity fade and end of life

IEEE Transactions on Industrial Informatics ◽

10.1109/tii.2021.3106593 ◽

2021 ◽

pp. 1-1

Author(s):

Samuel Greenbank ◽

David Howey

Keyword(s):

End Of Life ◽

Data Driven ◽

Capacity Fade ◽

Battery Capacity ◽

Selection For

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Charge and Discharge Behaviour of Li-Ion Batteries at Various Temperatures Containing LiCoO2 Nanostructured Cathode Produced by CCSO

Eurasian Chemico-Technological Journal ◽

10.18321/ectj235 ◽

2015 ◽

Vol 15 (4) ◽

pp. 301 ◽

Cited By ~ 1

Author(s):

Y.Y. Mamyrbayeva ◽

R.E. Beissenov ◽

M.A. Hobosyan ◽

S.E. Kumekov ◽

K.S. Martirosyan

Keyword(s):

Lithium Ion Battery ◽

Active Material ◽

Lithium Ion ◽

Synthetic Approach ◽

Capacity Fade ◽

Li Ion Batteries ◽

Material Loss ◽

Method Performance ◽

Battery Capacity ◽

Li Ion

<p>There are technical barriers for penetration market requesting rechargeable lithium-ion battery packs for portable devices that operate in extreme hot and cold environments. Many portable electronics are used in very cold (-40 °C) environments, and many medical devices need batteries that operate at high temperatures. Conventional Li-ion batteries start to suffer as the temperature drops below 0 °C and the internal impedance of the battery increases. Battery capacity also reduced during the higher/lower temperatures. The present work describes the laboratory made lithium ion battery behaviour features at different operation temperatures. The pouch-type battery was prepared by exploiting LiCoO<sub>2</sub> cathode material synthesized by novel synthetic approach referred as Carbon Combustion Synthesis of Oxides (CCSO). The main goal of this paper focuses on evaluation of the efficiency of positive electrode produced by CCSO method. Performance studies of battery showed that the capacity fade of pouch type battery increases with increase in temperature. The experimental results demonstrate the dramatic effects on cell self-heating upon electrochemical performance. The study involves an extensive analysis of discharge and charge characteristics of battery at each temperature following 30 cycles. After 10 cycles, the battery cycled at RT and 45 °C showed, the capacity fade of 20% and 25% respectively. The discharge capacity for the battery cycled at 25 °C was found to be higher when compared with the battery cycled at 0 °C and 45 °C. The capacity of the battery also decreases when cycling at low temperatures. It was important time to charge the battery was only 2.5 hours to obtain identical nominal capacity under the charging protocol. The decrease capability of battery cycled at high temperature can be explained with secondary active material loss dominating the other losses.</p>

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Real-Time State-of-Health Estimation of Lithium-Ion Batteries Based on the Equivalent Internal Resistance

IEEE Access ◽

10.1109/access.2020.2979570 ◽

2020 ◽

Vol 8 ◽

pp. 56811-56822 ◽

Cited By ~ 4

Author(s):

Xiaojun Tan ◽

Yuqing Tan ◽

Di Zhan ◽

Ze Yu ◽

Yuqian Fan ◽

...

Keyword(s):

Lithium Ion Batteries ◽

Real Time ◽

Lithium Ion ◽

Internal Resistance ◽

State Of Health

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Coupling Analysis and Performance Study of Commercial 18650 Lithium-Ion Batteries under Conditions of Temperature and Vibration

Energies ◽

10.3390/en11102856 ◽

2018 ◽

Vol 11 (10) ◽

pp. 2856 ◽

Cited By ~ 11

Author(s):

Lijun Zhang ◽

Zhongqiang Mu ◽

Xiangyu Gao

Keyword(s):

Lithium Ion Batteries ◽

Influencing Factor ◽

Lithium Ion ◽

Internal Resistance ◽

Performance Study ◽

Vibration Direction ◽

Vibration Condition ◽

Coupling Conditions ◽

Battery Capacity ◽

Vibration Coupling

At present, a variety of standardized 18650 commercial cylindrical lithium-ion batteries are widely used in new energy automotive industries. In this paper, the Panasonic NCR18650PF cylindrical lithium-ion batteries were studied. The NEWWARE BTS4000 battery test platform is used to test the electrical performances under temperature, vibration and temperature-vibration coupling conditions. Under the temperature conditions, the discharge capacity of the same battery at the low temperature was only 85.9% of that at the high temperature. Under the vibration condition, mathematical statistics methods (the Wilcoxon Rank-Sum test and the Kruskal-Wallis test) were used to analyze changes of the battery capacity and the internal resistance. Changes at a confidence level of 95% in the capacity and the internal resistance were considered to be significantly different between the vibration conditions at 5 Hz, 10 Hz, 20 Hz and 30 Hz versus the non-vibration condition. The internal resistance of the battery under the Y-direction vibration was the largest, and the difference was significant. Under the temperature-vibration coupling conditions, the orthogonal table L9 (34) was designed. It was found out that three factors were arranged in order of temperature, vibration frequency and vibration direction. Among them, the temperature factor is the main influencing factor affecting the performance of lithium-ion batteries.

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A Comparative Study of Charging Voltage Curve Analysis and State of Health Estimation of Lithium-ion Batteries in Electric Vehicle

Automotive Innovation ◽

10.1007/s42154-019-00080-2 ◽

2019 ◽

Vol 2 (4) ◽

pp. 263-275 ◽

Cited By ~ 1

Author(s):

Xuebing Han ◽

Xuning Feng ◽

Minggao Ouyang ◽

Languang Lu ◽

Jianqiu Li ◽

...

Keyword(s):

Neural Network ◽

Electrode Materials ◽

Lithium Ion ◽

Curve Analysis ◽

Constant Current ◽

Battery Management ◽

State Of Health ◽

Voltage Curve ◽

Battery Capacity ◽

Li Ion

AbstractLithium-ion (Li-ion) cells degrade after repeated cycling and the cell capacity fades while its resistance increases. Degradation of Li-ion cells is caused by a variety of physical and chemical mechanisms and it is strongly influenced by factors including the electrode materials used, the working conditions and the battery temperature. At present, charging voltage curve analysis methods are widely used in studies of battery characteristics and the constant current charging voltage curves can be used to analyze battery aging mechanisms and estimate a battery’s state of health (SOH) via methods such as incremental capacity (IC) analysis. In this paper, a method to fit and analyze the charging voltage curve based on a neural network is proposed and is compared to the existing point counting method and the polynomial curve fitting method. The neuron parameters of the trained neural network model are used to analyze the battery capacity relative to the phase change reactions that occur inside the batteries. This method is suitable for different types of batteries and could be used in battery management systems for online battery modeling, analysis and diagnosis.

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A Combined Multiple Factor Degradation Model and Online Verification for Electric Vehicle Batteries

Energies ◽

10.3390/en12224376 ◽

2019 ◽

Vol 12 (22) ◽

pp. 4376 ◽

Cited By ~ 1

Author(s):

Yuan Chen ◽

Yigang He ◽

Zhong Li ◽

Liping Chen

Keyword(s):

Experimental Data ◽

Electric Vehicle ◽

Factor Model ◽

Capacity Fade ◽

State Of Health ◽

Degradation Model ◽

Multiple Factors ◽

Battery Degradation ◽

Total Capacity ◽

Multiple Factor

Battery state of health (SOH) is related to the reduction of total capacity due to complicated aging mechanisms known as calendar aging and cycle aging. In this study, a combined multiple factor degradation model was established to predict total capacity fade considering both calendar aging and cycle aging. Multiple factors including temperature, state of charge (SOC), and depth of discharge (DOD) were introduced into the general empirical model to predict capacity fade for electric vehicle batteries. Experiments were carried out under different aging conditions. By fitting the data between multiple factors and model parameters, battery degradation equations related to temperature, SOC, and DOD could be formulated. The combined multiple factor model could be formed based on the battery degradation equations. An online state of health estimation based on the multiple factor model was proposed to verify the correctness of the model. Predictions were in good agreement with experimental data for over 270 days, as the margin of error between the prediction data and the experimental data never exceeded 1%.

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