State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis

Li, Xiaoyu; Wang, Zhenpo; Zhang, Lei; Zou, Changfu; Dorrell, David G.

doi:10.1016/j.jpowsour.2018.10.069

Cited by 272 publications

(98 citation statements)

References 31 publications

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“…Among these problems, accurately online monitoring battery external characteristics and evaluating state of health (SOH) are huge challenging issues of the battery management system (BMS). The precise results of battery SOH estimation not only indicate battery aging level but also provide valuable guidance for reasonable using batteries [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…The IC curve has high resolution for the charging/discharging voltage plateau region, and what's more, aging mechanisms can be extracted from the peak amplitude and position of the curve. Otherwise, the ICA studies have been validated for on-line SOH estimation for various perspectives such as area, position, and gradient [5,28]. In [29], based on IC curve, feasibility and accuracy of the degradation model is established using Gaussian function.…”

Section: Introductionmentioning

confidence: 99%

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State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression

Yuan

et al. 2020

Energy

Self Cite

254

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression

Yuan

et al. 2020

Energy

Self Cite

254

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“…In recent years, such methods have been widespread concerned due to their model-free characteristics and high flexibility. The HIs can be obtained through various methods, such as battery capacity, ohmic resistance, 23 model parameters, 10,13 charging/discharging parameters, 17,18,24 and incremental capacity/differential voltage analysis (ICA/DVA) 21,25 . To reduce the data size for aging feature extraction, Hu et al 17 tried to approximate the relationship between the sample entropy of discharge voltage and capacity through the sparse Bayesian predictive modelling (SBPM).…”

Section: Introductionmentioning

confidence: 99%

“…To date, the main obstacles to the data-driven approach are how to extract effective health indicators (HIs) to quantify battery capacity degeneration and how to find a simple and suitable online estimation algorithm using the microprocessor. The HIs can be obtained through various methods, such as battery capacity, ohmic resistance, 23 model parameters, 10,13 charging/discharging parameters, 17,18,24 and incremental capacity/differential voltage analysis (ICA/DVA) 21,25 . The effective HIs can significantly reduce the computing effort and improve estimation accuracy.…”

Section: Introductionmentioning

confidence: 99%

A model‐based and data‐driven joint method for state‐of‐health estimation of lithium‐ion battery in electric vehicles

Lyu

Gao

2019

Int J Energy Res

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Summary Lithium‐ion battery state‐of‐health estimation is one of the vital issues for electric vehicle safety. In this work, a joint model‐based and data‐driven estimator is developed to achieve accurate and reliable state‐of‐health estimation. In the estimator, an increase in ohmic resistance extracted from the Thevenin model is defined as the health indicator to quantify the capacity degradation. Then, a linear state‐space representation is constructed based on the data‐driven linear regression. Furthermore, the Kalman filter is introduced to trace capacity degradation based on the novel state space representation. A series of battery aging datasets with different dynamic loading profiles and temperatures are obtained to demonstrate the accuracy and robustness of the proposed method. Results show that the maximum error of the Kalman filter is 2.12% at different temperatures, which proves the effectiveness of the proposed method.

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“…Nowadays, the development of electric vehicles (EVs) cannot be independent of advanced battery technologies, [1][2][3][4][5] and lithium-ion batteries seem to be the most competitive candidate due to their reasonable power and energy density, long life cycle, and memoryless effect. 6,7 To ensure safe and healthy battery operation and to extend battery life to the greatest extent, a variety of research has been carried out to estimate internal battery status, eg, state of charge (SOC), [8][9][10] state of energy (SOE), 11 state of power (SOP), 12 and state of health (SOH). [13][14][15] In addition, balancing techniques used for cells, 16 thermal management, 17 and charging strategies 18,19 are also critical to ensure battery operation with high performance.…”

Section: Introductionmentioning

confidence: 99%

Optimal charging strategy design for lithium‐ion batteries considering minimization of temperature rise and energy loss

et al. 2019

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Summary Battery charging techniques are critical to enhance battery operation performance. Charging temperature rise, energy loss, and charging time are three key indicators to evaluate charging performance. It is imperative to decrease temperature rise and energy loss without extending the charging time during the charging process. To this end, an equivalent circuit electrical model, a power loss model, and a thermal model are built in this study for lithium‐ion batteries. Then, an integrated objective function is formulated to minimize energy loss and temperature increment during battery charging. To further validate the generality and feasibility of the proposed charging strategy, experiments are conducted with respect to different current, operating temperatures, battery types, and aging status. Comparison results demonstrate that the devised charging strategy is capable of achieving the intended effect under any operating temperature and with different aging status.

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State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis

Cited by 272 publications

References 31 publications

State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression

State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression

A model‐based and data‐driven joint method for state‐of‐health estimation of lithium‐ion battery in electric vehicles

Optimal charging strategy design for lithium‐ion batteries considering minimization of temperature rise and energy loss

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