Remaining useful life prediction for lithium-ion batteries using particle filter and artificial neural network

Qin, Wei; Lv, Huichun; Liu, Chengliang; Datta, Nirmalya; Jahanshahi, Peyman

doi:10.1108/imds-03-2019-0195

Cited by 47 publications

(21 citation statements)

References 20 publications

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“…In the literature [16], a new support vector regression-based SOH state-space model for batteries was developed using the battery capacity as the state variable, the estimated impedance as the output variable, and the particle filter to estimate the impedance decay parameter. The literature [17] used the particle filter algorithm as the core, the double exponential model as the state equation, and the artificial neural network as the observation equation to fit the degradation curve of the battery well, and the prediction accuracy will gradually improve with the increase of cycles. Although this method can achieve more accurate battery SOH estimation, the estimation results are highly dependent on the quality of the model.…”

Section: Model-based Estimation Methodsmentioning

confidence: 99%

Estimation methods for States of Charge and Health of Lithium-ion battery

Zhang²,

Wang

et al. 2022

J. Phys.: Conf. Ser.

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A battery energy storage system is one of the practical and effective ways to achieve carbon neutrality. Lithium-ion batteries are widely used because of their long cycle life, high energy density, high rated voltage, and low self-discharge rate, etc. Their large-scale application has put forward higher requirements for the battery management system (BMS) to ensure the reliable operation of batteries. However, due to the electrochemical reactions and complex operating conditions inside the battery, it is difficult to accurately assess the internal state of the battery. The state of charge (SOC) and state of health (SOH) are two important parameters in the battery management system. This paper systematically summarizes the current commonly used SOC and SOH estimation methods and analyzes the characteristics of each method. The problems and future research trends of battery SOC and SOH estimation techniques are also described, which are expected to provide a reference for further research in this field.

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Section: Model-based Estimation Methodsmentioning

confidence: 99%

Estimation methods for States of Charge and Health of Lithium-ion battery

Zhang²,

Wang

et al. 2022

J. Phys.: Conf. Ser.

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“…Primarily, the NASA dataset is utilized as a common reference for RUL prediction of battery [17,39,42]. The SCI-based model is trained by employing a conventional 70:30 ratio for each battery [43].…”

Section: Data Split Methodsmentioning

confidence: 99%

Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries

et al. 2021

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Remaining useful life (RUL) is a crucial assessment indicator to evaluate battery efficiency, robustness, and accuracy by determining battery failure occurrence in electric vehicle (EV) applications. RUL prediction is necessary for timely maintenance and replacement of the battery in EVs. This paper proposes an artificial neural network (ANN) technique to predict the RUL of lithium-ion batteries under various training datasets. A multi-channel input (MCI) profile is implemented and compared with single-channel input (SCI) or single input (SI) with diverse datasets. A NASA battery dataset is utilized and systematic sampling is implemented to extract 10 sample values of voltage, current, and temperature at equal intervals from each charging cycle to reconstitute the input training profile. The experimental results demonstrate that MCI profile-based RUL prediction is highly accurate compared to SCI profile under diverse datasets. It is reported that RMSE for the proposed MCI profile-based ANN technique is 0.0819 compared to 0.5130 with SCI profile for the B0005 battery dataset. Moreover, RMSE is higher when the proposed model is trained with two datasets and one dataset, respectively. Additionally, the importance of capacity regeneration phenomena in batteries B0006 and B0018 to predict battery RUL is investigated. The results demonstrate that RMSE for the testing battery dataset B0005 is 3.7092, 3.9373 when trained with B0006, B0018, respectively, while it is 3.3678 when trained with B0007 due to the effect of capacity regeneration in B0006 and B0018 battery datasets.

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“…An improved PF algorithm, untracked PF, is introduced into the prediction of battery RUL in the study by Miao et al (2013). The PF algorithm is used as the main method; double exponential model is used as the equation of state; and artificial neural network is used for resampling to reduce the particle degradation problem in the study by Qin et al (2020). Jiao et al (2020) propose a PF framework based on conditional variational automatic encoder and resampling strategy to predict the RUL of battery.…”

Section: Introductionmentioning

confidence: 99%

Lithium Ion Battery Health Prediction via Variable Mode Decomposition and Deep Learning Network With Self-Attention Mechanism

2022

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Battery health prediction is very important for the safety of lithium batteries. Due to the factors such as capacity regeneration and random fluctuation in the use of lithium ion battery, the accuracy and generalization ability are poor when using a single scale feature to predict the health state of lithium ion battery. To solve these problems, we propose a comprehensive prediction method based on variational mode decomposition, integrated particle filter, and long short-term memory network with self-attention mechanism. Firstly, the capacity data of lithium ion battery is decomposed by variational mode decomposition to obtain the residual component which can reflect the global degradation trend of lithium ion battery and intrinsic mode functions component that can reflect the local random fluctuation. Then, the particle filter algorithm is employed to predict the residual component, and the long short-term memory network with self-attention mechanism is proposed to predict the intrinsic mode functions component. Finally, the prediction results of each subcomponent are reconstructed to obtain the final prediction value of lithium ion battery health state. The experimental results show that the prediction method proposed in this article has good prediction accuracy and stability.

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Remaining useful life prediction for lithium-ion batteries using particle filter and artificial neural network

Cited by 47 publications

References 20 publications

Estimation methods for States of Charge and Health of Lithium-ion battery

Estimation methods for States of Charge and Health of Lithium-ion battery

Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries

Lithium Ion Battery Health Prediction via Variable Mode Decomposition and Deep Learning Network With Self-Attention Mechanism

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