Aging mechanisms under different state-of-charge ranges and the multi-indicators system of state-of-health for lithium-ion battery with Li(NiMnCo)O2 cathode

Gao, Yang; Jiang, Jiuchun; Zhang, Caiping; Zhang, Weige; Jiang, Yan

doi:10.1016/j.jpowsour.2018.07.018

Cited by 107 publications

(40 citation statements)

References 44 publications

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“…As pointed out by Reference [5], SoC (or DoD) influence on ageing is not simple to analyse, because in some situations low SoC levels (high DoD) can be beneficial while it could be harmful in other cases. Finally, a very non linear SoC dependence of cycling ageing was identified experimentally by Reference [8]. In that work, five cycling tests of the same SoC amplitude (20%) at different average SoC levels were carried out.…”

Section: Main Ageing Mechanisms In Lithium-ion Batteriesmentioning

confidence: 99%

Modelling Lithium-Ion Battery Ageing in Electric Vehicle Applications—Calendar and Cycling Ageing Combination Effects

2020

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Battery ageing is an important issue in e-mobility applications. The performance degradation of lithium-ion batteries has a strong influence on electric vehicles’ range and cost. Modelling capacity fade of lithium-ion batteries is not simple: many ageing mechanisms can exist and interact. Because calendar and cycling ageings are not additive, a major challenge is to model battery ageing in applications where the combination of cycling and rest periods are variable as, for example, in the electric vehicle application. In this work, an original approach to capacity fade modelling based on the formulation of reaction rate of a two-step reaction is proposed. A simple but effective model is obtained: based on only two differential equations and seven parameters, it can reproduce the capacity evolution of lithium-ion cells subjected to cycling profiles similar to those found in electric vehicle applications.

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Section: Main Ageing Mechanisms In Lithium-ion Batteriesmentioning

confidence: 99%

Modelling Lithium-Ion Battery Ageing in Electric Vehicle Applications—Calendar and Cycling Ageing Combination Effects

2020

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“…In this paper, four indicators including 95% confidence interval, relative error, maximum absolute error (MAE), and RMS error are used to assess the accuracy and reliability of the SOH estimation results. The 95% confidence interval and RMS error are calculated by Eqs (28) and (29), respectively.…”

Section: Joint Estimation Designmentioning

confidence: 99%

“…Yang et al [27] extracted health indicators (HIs) from the charging curve, used gray relational analysis to evaluate the relational degree, and applied Gaussian progress regression (GPR) to estimate the SOH. Through incremental capacity (IC) and differential voltage (DV) analysis, more aging features could be extracted [28,29]. Weng et al [30] tracked the peak values of the IC curve by support vector regression and estimated the battery pack SOH.…”

Section: Introductionmentioning

confidence: 99%

Joint Estimation of Inconsistency and State of Health for Series Battery Packs

et al. 2021

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Battery packs are applied in various areas (e.g., electric vehicles, energy storage, space, mining, etc.), which requires the state of health (SOH) to be accurately estimated. Inconsistency, also known as cell variation, is considered a significant evaluation index that greatly affects the degradation of battery pack. This paper proposes a novel joint inconsistency and SOH estimation method under cycling, which fills the gap of joint estimation based on the fast-charging process for electric vehicles. First, fifteen features are extracted from current change points during the partial charging process. Then, a joint estimation system is designed, where fusion weights are obtained by the analytic hierarchy process and multi-scale sample entropy to evaluate inconsistency. A wrapper is used to select the optimal feature subset, and Gaussian process regression is implemented to estimate the SOH. Finally, the estimation performance is assessed by the test data. The results show that the inconsistency evaluation can reflect the aging conditions, and the inconsistency does affect the aging process. The wrapper selection method improves the accuracy of SOH estimation by about 75.8% compared to the traditional filter method when only 10% of data is used for model training. The maximum absolute error and root mean square error are 2.58% and 0.93%, respectively.

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“…The real battery usage in these applications suffers degradation from different temperatures and working SOC ranges due to environmental temperature, device design, and user's habit that makes life evaluation difficult [1][2][3]. There have been massive researches on battery aging behavior under different temperature and working SOC ranges owing to their significant impact on battery degradation mechanisms, which applies huge uncertainty and variation in battery lifespan and safety [4][5][6][7][8]. Thus, there has been numerous inspection methods developed to evaluate the cell aging status.…”

Section: Introductionmentioning

confidence: 99%

Combining machine learning algorithms and an incremental capacity analysis on 18650 cell under different cycling temperature and SOC range

Lai¹,

Chao²,

Wu³

et al. 2020

E3S Web Conf.

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A novel way to apply machine learning algorithms on the incremental capacity analysis (dQ/dV) is developed to identify battery cycling conditions under different temperatures and working SOC ranges. Batteries are cycled under each combination of temperatures (-10oC, 25oC, 60oC) and SOC ranges (0-10%, 25-75%, 90-100%, 0-100%) up to 60 equivalent cycles. The discharge data is transformed into dQ/dV-V curve and its features of the peaks and valleys are further taken for machine learning. Both supervised and unsupervised machine learning algorithms (PCA and LDA) are applied to classify batteries in terms of temperature or SOC range. The results reveal that batteries cycled under different temperatures can be identified separately regardless of the working SOC range. When splitting 60 samples with a ratio of training set equals to 0.85, the remaining test set gives an identification accuracy of 89% in temperature and 67% in working SOC range.

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Aging mechanisms under different state-of-charge ranges and the multi-indicators system of state-of-health for lithium-ion battery with Li(NiMnCo)O2 cathode

Cited by 107 publications

References 44 publications

Modelling Lithium-Ion Battery Ageing in Electric Vehicle Applications—Calendar and Cycling Ageing Combination Effects

Modelling Lithium-Ion Battery Ageing in Electric Vehicle Applications—Calendar and Cycling Ageing Combination Effects

Joint Estimation of Inconsistency and State of Health for Series Battery Packs

Combining machine learning algorithms and an incremental capacity analysis on 18650 cell under different cycling temperature and SOC range

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