Remaining useful life prediction of lithium-ion battery using a hybrid model-based filtering and data-driven approach

Zheng, Xiujuan; Wu, Huaiyu; Yang, Chen

doi:10.1109/ascc.2017.8287603

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…In the literature, two different methods are used to solve the above problem. One way of doing this is to use the data-driven algorithms (e.g., ND-AR 147 and RVM 148 ) as the output equation to provide a future observation data series. With the future observation data series, the filtering algorithm can update the state variables (i.e., model parameters) and predict battery RUL.…”

Section: Improving the Performance Of Filtering Methodsmentioning

confidence: 99%

Battery Lifetime Prognostics

Lin

et al. 2020

Joule

642

244

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Lithium-ion batteries have been widely used in many important applications. However, there are still many challenges facing lithium-ion batteries, one of them being degradation. Battery degradation is a complex problem, which involves many electrochemical side reactions in anode, electrolyte, and cathode. Operating conditions affect degradation significantly and therefore the battery lifetime. It is of extreme importance to achieve accurate predictions of the remaining battery lifetime under various operating conditions. This is essential for the battery management system to ensure reliable operation and timely maintenance and is also critical for battery second-life applications. After introducing the degradation mechanisms, this paper provides a timely and comprehensive review of the battery lifetime prognostic technologies with a focus on recent advances in model-based, data-driven, and hybrid approaches. The details, advantages, and limitations of these approaches are presented, analyzed, and compared. Future trends are presented, and key challenges and opportunities are discussed.

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Section: Improving the Performance Of Filtering Methodsmentioning

confidence: 99%

Battery Lifetime Prognostics

Lin

et al. 2020

Joule

642

244

View full text Add to dashboard Cite

show abstract

“…An adaptive hybrid model is constructed in [16], which is a combination of an empirical model and a long short-term memory (LSTM) NN model to characterize the battery capacity degradation. Similarly, a hybrid algorithm that combines model-based Kalman filtering (KF) and a data-driven relevance vector machine (RVM) is proposed in [17] to offer capacity prognostic results. A number of other combinations for hybrid models are also available [18][19][20], which claim to provide accurate predictions of the battery's behavior.…”

Section: State Of the Research: Hybrid Modeling Of Lithium-ion Batteriesmentioning

confidence: 99%

“…At the beginning of each new check-up cycle, the concentration value must be reset to the output value of the previous cycle. SOH is calculated by determining the maximum available capacity with respect to the concentration of Li + in the anode, as per Equations ( 16) and (17). Figure 9 shows the measured and predicted SOH plots for cell number 5 and cell number 7.…”

Section: State Of Health Estimationmentioning

confidence: 99%

Hybrid Modeling of Lithium-Ion Battery: Physics-Informed Neural Network for Battery State Estimation

Singh,

Ebongue,

Rezaei

et al. 2023

Batteries

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Accurate forecasting of the lifetime and degradation mechanisms of lithium-ion batteries is crucial for their optimization, management, and safety while preventing latent failures. However, the typical state estimations are challenging due to complex and dynamic cell parameters and wide variations in usage conditions. Physics-based models need a tradeoff between accuracy and complexity due to vast parameter requirements, while machine-learning models require large training datasets and may fail when generalized to unseen scenarios. To address this issue, this paper aims to integrate the physics-based battery model and the machine learning model to leverage their respective strengths. This is achieved by applying the deep learning framework called physics-informed neural networks (PINN) to electrochemical battery modeling. The state of charge and state of health of lithium-ion cells are predicted by integrating the partial differential equation of Fick’s law of diffusion from a single particle model into the neural network training process. The results indicate that PINN can estimate the state of charge with a root mean square error in the range of 0.014% to 0.2%, while the state of health has a range of 1.1% to 2.3%, even with limited training data. Compared to conventional approaches, PINN is less complex while still incorporating the laws of physics into the training process, resulting in adequate predictions, even for unseen situations.

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“…Maio et al [ 30 ] combined relevance and exponential regression to realize the RUL prediction. Zheng et al [ 31 ] proposed a hybrid model that combines Kalman filtering and relevance vector machine to predict the degradation trend of batteries.…”

Section: Introductionmentioning

confidence: 99%

A Double-Channel Hybrid Deep Neural Network Based on CNN and BiLSTM for Remaining Useful Life Prediction

Zhao

Huang

et al. 2020

Sensors

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In recent years, prognostic and health management (PHM) has played an important role in industrial engineering. Efficient remaining useful life (RUL) prediction can ensure the development of maintenance strategies and reduce industrial losses. Recently, data-driven based deep learning RUL prediction methods have attracted more attention. The convolution neural network (CNN) is a kind of deep neural network widely used in RUL prediction. It shows great potential for application in RUL prediction. A CNN is used to extract the features of time-series data according to the spatial feature method. This way of processing features without considering the time dimension will affect the prediction accuracy of the model. On the contrary, the commonly used long short-term memory (LSTM) network considers the timing of the data. However, compared with CNN, it lacks spatial data extraction capabilities. This paper proposes a double-channel hybrid prediction model based on the CNN and a bidirectional LSTM network to avoid those drawbacks. The sliding time window is used for data preprocessing, and an improved piece-wise linear function is used for model validating. The prediction model is evaluated using the C-MAPSS dataset provided by NASA. The predicted results show the proposed prediction model to have a better prediction performance compared with other state-of-the-art models.

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Remaining useful life prediction of lithium-ion battery using a hybrid model-based filtering and data-driven approach

Cited by 7 publications

References 14 publications

Battery Lifetime Prognostics

Battery Lifetime Prognostics

Hybrid Modeling of Lithium-Ion Battery: Physics-Informed Neural Network for Battery State Estimation

A Double-Channel Hybrid Deep Neural Network Based on CNN and BiLSTM for Remaining Useful Life Prediction

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