A new hybrid method for the prediction of the remaining useful life of a lithium-ion battery

Chang, Yang Lang; Fang, Huajing; Zhang, Yong

doi:10.1016/j.apenergy.2017.09.106

Cited by 186 publications

(61 citation statements)

References 39 publications

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“…This method proves to be exhibiting more strength than its predecessors since it narrows down their weaknesses. Thus, this method increases process reliability and robustness by combining the complementary information from different prognostic methods in intelligent ways compared to a single model-based method [77,78,81,82]. However, this method has a limitation of offering reliable validity only for certain given conditions and periods of time.…”

Section: Hybrid Methodsmentioning

confidence: 99%

Review on Health Management System for Lithium-Ion Batteries of Electric Vehicles

2018

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The battery is the most ideal power source of the twenty-first century, and has a bright future in many applications, such as portable consumer electronics, electric vehicles (EVs), military and aerospace systems, and power storage for renewable energy sources, because of its many advantages that make it the most promising technology. EVs are viewed as one of the novel solutions to land transport systems, as they reduce overdependence on fossil energy. With the current growth of EVs, it calls for innovative ways of supplementing EVs power, as overdependence on electric power may add to expensive loads on the power grid. However lithium-ion batteries (LIBs) for EVs have high capacity, and large serial/parallel numbers, when coupled with problems like safety, durability, uniformity, and cost imposes limitations on the wide application of lithium-ion batteries in EVs. These LIBs face a major challenge of battery life, which research has shown can be extended by cell balancing. The common areas under which these batteries operate with safety and reliability require the effective control and management of battery health systems. A great deal of research is being carried out to see that this technology does not lead to failure in the applications, as its failure may lead to catastrophes or lessen performance. This paper, through an analytical review of the literature, gives a brief introduction to battery management system (BMS), opportunities, and challenges, and provides a future research agenda on battery health management. With issues raised in this review paper, further exploration is essential.

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Section: Hybrid Methodsmentioning

confidence: 99%

Review on Health Management System for Lithium-Ion Batteries of Electric Vehicles

2018

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“…In this paper, the CR refers to the ohmic resistance Rn. According to Ohm's Law, it can be obtained as (1) where ΔUn is the voltage change caused by ohmic resistance of cycle n; In is the load current in cycle n; and N is the total cycle life of the battery.…”

Section: Calculation Of Crmentioning

confidence: 99%

“…This phenomenon will reduce the equipment's reliability and even lead to catastrophic failures. Therefore, it is crucial to timely forecast Li-ion battery capacity as well as its remaining useful life (RUL).For Li-ion battery, its RUL can be defined as the number of remaining charge-discharge cycles before the battery capacity deteriorates to a predetermined failure threshold [1]. The prediction methods for battery RUL can be categorized into three types, i.e.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Remaining Useful Life for Lithium-Ionbattery with Multiple Health Indicators

Yan¹,

Chen²,

Su³

2020

Preprint

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The remaining capacity can only be measured with offline method. This brings great challenge for the online prediction of Li-ion battery’s RUL. A novel online prediction method for Li-ion battery’s RUL was proposed, which is based on multiple health indicators (HIs) and can be derived from the batteries’ historical operation data. Firstly, four indirect HIs were built according to the battery’s operation current, voltage and temperature data respectively. On that basis, a generalized regression neural network (GRNN) was developed to estimate the battery’s remaining capacity, and the non-linear autoregressive approach (NAR) was utilized to predict the battery’s RUL based on the estimated capacity value. Furthermore, to reduce the interference, twice wavelet denoising were performed with different thresholds. A case study is conducted with a NASA battery dataset to demonstrate the effectiveness of the method. The result shows that the proposed method can obtain Li-ion batteries’ RUL effectively.

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“…The existing RUL prediction methods for lithium-ion batteries mainly include the adaptive filtering method, the artificial intelligence method, and the stochastic process modeling method. The adaptive filtering mainly includes Kalman filtering [14], extended Kalman filtering [15], unscented Kalman filtering [16,17], particle filtering [18][19][20][21][22][23], and unscented particle filtering [24,25], etc. Although the adaptive filtering has higher accuracy for RUL prediction, the accuracy can be easily influenced by time-varying current and ambient temperature [3].…”

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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A new hybrid method for the prediction of the remaining useful life of a lithium-ion battery

Cited by 186 publications

References 39 publications

Review on Health Management System for Lithium-Ion Batteries of Electric Vehicles

Review on Health Management System for Lithium-Ion Batteries of Electric Vehicles

Prediction of Remaining Useful Life for Lithium-Ionbattery with Multiple Health Indicators

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

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