A lead-acid battery's remaining useful life prediction by using electrochemical model in the Particle Filtering framework

Lyu, Chao; Lai, Qi; Ge, Tengfei; Yu, Honghai; Wang, Lixin; Ma, Na

doi:10.1016/j.energy.2016.12.004

Cited by 126 publications

(43 citation statements)

References 18 publications

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“…It can be seen that the testing profiles generally agree with the base models. In the light of this, λ in the (18) generally presents a value getting close to 1, as depicted in Fig. 2-(b) ∼ Fig.…”

Section: Resultsmentioning

confidence: 76%

“…After that, the battery degradation level at various timescale could be predicted through using the established model. One effective model type here is the physics-based models that use several partial differential equations to directly explain battery aging behaviors [17,18]. Although attractive electrochemical dynamics of battery aging can be analysed in the simulation environment, these physics models are generally highly memoryconsuming and complex to be fitted, making them overly expensive for real-time aging trajectory predictions [19].…”

Section: Introductionmentioning

confidence: 99%

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Real-time aging trajectory prediction using a base model-oriented gradient-correction particle filter for Lithium-ion batteries

Tang

Liu

Wang

et al. 2019

Journal of Power Sources

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Section: Resultsmentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Real-time aging trajectory prediction using a base model-oriented gradient-correction particle filter for Lithium-ion batteries

Tang

Liu

Wang

et al. 2019

Journal of Power Sources

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“…In essence, adaptive algorithms rely largely on electrochemical models and equivalent circuit models, and require additional closed-loop control and feedback to achieve reliable prediction. Reference [18] introduces an electrochemical model to simulate the battery charge and discharge process, and then a novel PF based framework is proposed to predict the RUL. Experimental results highlight that the proposed algorithm performs higher prediction precision, compared with conventional PFs.…”

Section: Introductionmentioning

confidence: 99%

State of Health Estimation for Lithium-Ion Batteries Based on Healthy Features and Long Short-Term Memory

et al. 2020

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Precise estimation of state of health (SOH) are of great importance for proper operation of lithium-ion batteries equipped in electric vehicles. For real applications, it is however difficult to estimate battery SOH due to stochastic operation, which in turn speeds up aging process of the battery. To attain the precise SOH estimation, an efficient estimation manner based on machine learning is proposed in this study. Firstly, the voltage profile during charging and discharging process and incremental capacity variation are acquired through the cycle life test, and the healthy features correlating to battery degradation are extracted. Secondly, the grey relation analysis and entropy weight method are employed to analyze the healthy features. Finally, the long short-term memory is established to achieve the SOH estimation of battery. The experimental results highlight that the proposed method can effectively predict the battery SOH with preferable accuracy, stability and robustness. INDEX TERMS Healthy features (HFs), grey relational analysis (GRA), entropy weight method (EWM), long short-term memory (LSTM), state of health (SOH). NOMENCLATURE A. ABBREVIATIONS

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“…To further improve the RUL prediction accuracy of LIBs, Guha et al established the electrochemical impedance spectrum of LIBs on the basis of a fractional‐order equivalent circuit method, which was combined with the PF model to estimate the battery life. Lyu et al introduced a PF framework based on electrochemical model to demonstrate the aging of LIB. The parameters of battery degradation method were reflected as state variables to achieve RUL prediction.…”

Section: Introductionmentioning

confidence: 99%

A method based on improved ant lion optimization and support vector regression for remaining useful life estimation of lithium‐ion batteries

Wang

Li³

et al. 2019

Energy Science & Engineering

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Remaining useful life (RUL) prediction of lithium‐ion batteries (LIBs) plays a very important role in the prognostics and health management (PHM). Accurately predicting RUL of batteries can maintain and replace the batteries in advance to guarantee the safety and stability of the energy storage system (ESS). A method based on improved ant lion optimization and support vector regression (IALO‐SVR) is proposed to accurately predict RUL of LIBs. The ALO algorithm easily falls into the local optimal solution, the levy flight algorithm is utilized to improve the shortcoming of the ALO algorithm. With the mathematical comparison of particle swarm optimization (PSO), differential evolution (DE), and ALO algorithms, the results indicate that the IALO algorithm has higher convergence accuracy. Experimental data simulations were performed using the battery datasets of NASA Prognostics Center of Excellence (PCoE) and the Center for Advanced Life Cycle Engineering (CALCE) to verify the proposed method. Through comparison with SVR, PSO‐LSSVM, and ALO‐SVR methods, the results indicate that the RUL prediction is more accurate based upon the IALO‐SVR method. Therefore, the proposed method can provide high prediction accuracy in battery health prognosis.

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A lead-acid battery's remaining useful life prediction by using electrochemical model in the Particle Filtering framework

Cited by 126 publications

References 18 publications

Real-time aging trajectory prediction using a base model-oriented gradient-correction particle filter for Lithium-ion batteries

Real-time aging trajectory prediction using a base model-oriented gradient-correction particle filter for Lithium-ion batteries

State of Health Estimation for Lithium-Ion Batteries Based on Healthy Features and Long Short-Term Memory

A method based on improved ant lion optimization and support vector regression for remaining useful life estimation of lithium‐ion batteries

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