A Novel Multi-Phase Stochastic Model for Lithium-Ion Batteries’ Degradation with Regeneration Phenomena

Zhang, Jianxun; He, Xiao; Si, Xiaosheng; Hu, Changhua; Zhou, Donghua

doi:10.3390/en10111687

Cited by 21 publications

(10 citation statements)

References 32 publications

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“…Zhang et al [53] proposed a RUL prediction method with recovery in storage, which has a higher accuracy for the lithium-ion batteries with state recovery. Zhang et al [54] proposed a RUL prediction method with consideration of the relaxation effect, which uses the random jumps to describe the regeneration phenomena caused by the rest time. This method can better describe the recovery process caused by the relaxation effect.…”

Section: Literature Reviewmentioning

confidence: 99%

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect

et al. 2019

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Remaining useful life (RUL) prediction has great importance in prognostics and health management (PHM). Relaxation effect refers to the capacity regeneration phenomenon of lithium-ion batteries during a long rest time, which can lead to a regenerated useful time (RUT). This paper mainly studies the influence of the relaxation effect on the degradation law of lithium-ion batteries, and proposes a novel RUL prediction method based on Wiener processes. This method can simplify the modeling complexity by using the RUT to model the recovery process. First, the life cycle of a lithium-ion battery is divided into the degradation processes that eliminate the relaxation effect and the recovery processes caused by relaxation effect. Next, the degradation model, after eliminating the relaxation effect, is established based on linear Wiener processes, and the model for RUT is established by using normal distribution. Then, the prior parameters estimation method based on maximum likelihood estimation and online updating method under the Bayesian framework are proposed. Finally, the experiments are carried out according to the degradation data of lithium-ion batteries published by NASA. The results show that the method proposed in this paper can effectively improve the accuracy of RUL prediction and has a strong engineering application value.

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Section: Literature Reviewmentioning

confidence: 99%

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect

et al. 2019

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“…Thus far, this issue has not been solved well, and only a few researchers have focused on it [23]. Some researchers have presented that capacity recovery has a certain regularity over time.…”

Section: Introductionmentioning

confidence: 99%

A Method for Interval Prediction of Satellite Battery State of Health Based on Sample Entropy

Cao

Zhang

et al. 2019

IEEE Access

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Satellites need batteries to provide energy when operating in shadow regions, and lithium-ion batteries have become the batteries of choice for most satellites due to their high energy density, low self-discharge rate, and long cycle life. When a satellite battery is working in outer space, its capacity will gradually decrease as the number of cycles increases, and a certain degree of capacity recovery will occur. Due to the excellent mapping relationship between the discharge cutoff voltage and the capacity degradation of lithium-ion batteries and the fact that the sample entropy (SampEn) can sensitively capture local fluctuations, such as the recovery effect during lithium-ion battery capacity degradation, a method for interval prediction of the satellite battery state of health (SOH) based on SampEn was proposed. This method adopts a neural network model based on lower upper bound estimation (LUBE). The method uses the discharge cutoff voltage and the discharge voltage SampEn as the inputs and the battery SOH as the output for the neural network model. To improve the prediction interval coverage and reduce the prediction interval width, especially considering that the lower bound of the interval prediction often determines whether the satellite battery output power reaches the warning threshold, a modified comprehensive indicator function, the coverage width-based criterion (CWC), was constructed. Additionally, based on the nondifferentiability of this indicator function, a simulated annealing algorithm was used to optimize the neural network; at the same time, the optimal values of the interval coverage and interval width were taken into account, resulting in the lower bound of the prediction interval being closer to the actual value. Finally, test data from a NASA #18 battery were used to validate, analyze and verify the interval prediction algorithm proposed in this paper. The results were compared with those obtained from a support vector machine (SVM)-based interval prediction method.INDEX TERMS Battery state of health (SOH), sample entropy (SampEn), lower upper bound estimation (LUBE), neural network, simulated annealing.

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“…Most literatures regarding two‐stage degradation modeling adopt a same stochastic process or degradation path for both stages, where different parameters are involved as distinction. Among them, Zhang proposed a multiphase stochastic model with random jumps based on Wiener process for the lithium‐ion battery degradation; Bae constructed a hierarchal Bayesian change point regression model to fit the PDPs' degradation which has a two‐stage pattern; Wang proposed a change point Wiener process model to fit the degradation paths of OLEDs, which also showed a two‐phase regularity; Feng applied a novel three‐phase path degradation model to high‐voltage‐pulse capacitor deterioration problem; more literature can be referred to Wen, Sheng, and Kong . Meanwhile, some researchers also have adopted different stochastic processes for specific stages according to their degradation mechanisms.…”

Section: Introductionmentioning

confidence: 99%

A two‐stage degradation model considering the stage‐varying of dispersity regulation

Wang

Wei

et al. 2019

Quality & Reliability Eng

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In reliability analysis, degradation test has been recognized as an effective method for high reliable products and complex systems when key performance indicators can be observed. Then, a reasonable degradation model becomes a key issue to guarantee a reasonable reliability assessment. Motivated by practical needs, this paper proposes a novel two‐stage degradation model considering the different dispersity regulations corresponding to the two stages. A maximum likelihood estimation (MLE) method for unknown parameters is established, and an initial guess procedure is given to improve the efficiency of optimization algorithm. Then, the reliability inference regarding the product population is discussed. A comprehensive simulation study is conducted to validate the proposed approach where the two‐stage Wiener process model is adopted as a reference for a better understanding. Finally, the constructed model is further verified by two real applications. Comparative results clearly demonstrate the reasonability and effectiveness of the proposed model.

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A Novel Multi-Phase Stochastic Model for Lithium-Ion Batteries’ Degradation with Regeneration Phenomena

Cited by 21 publications

References 32 publications

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect

A Method for Interval Prediction of Satellite Battery State of Health Based on Sample Entropy

A two‐stage degradation model considering the stage‐varying of dispersity regulation

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