A Compact Methodology Via a Recurrent Neural Network for Accurate Equivalent Circuit Type Modeling of Lithium-Ion Batteries

Zhao, Ruxiu; Kollmeyer, Phillip J.; Lorenz, Robert D.; Jahns, T.M.

doi:10.1109/tia.2018.2874588

Cited by 59 publications

(15 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was clear that the RMSE was the smallest at a higher temperature and the RMSE at a lower temperature was slightly higher than at other temperatures, indicating that the accuracy of the voltage prediction was affected by the temperature. The slightly higher RMSE at a lower temperature might have been due to the fact that the internal resistance of the cell increased and the nonlinear Butler-Volmer effects and diffusion were more prevalent [45]. In addition, the voltage fluctuations were more severe at low temperatures making the voltage harder to predict than at other temperatures.…”

Section: Voltage Prediction Resultsmentioning

confidence: 99%

A Fault Diagnosis and Prognosis Method for Lithium-Ion Batteries Based on a Nonlinear Autoregressive Exogenous Neural Network and Boxplot

et al. 2021

View full text Add to dashboard Cite

The frequent occurrence of electric vehicle fire accidents reveals the safety hazards of batteries. When a battery fails, its symmetry is broken, which results in a rapid degradation of its safety performance and poses a great threat to electric vehicles. Therefore, accurate battery fault diagnoses and prognoses are the key to ensuring the safe and durable operation of electric vehicles. Thus, in this paper, we propose a new fault diagnosis and prognosis method for lithium-ion batteries based on a nonlinear autoregressive exogenous (NARX) neural network and boxplot for the first time. Firstly, experiments are conducted under different temperature conditions to guarantee the diversity of the data of lithium-ion batteries and then to ensure the accuracy of the fault diagnosis and prognosis at different working temperatures. Based on the collected voltage and current data, the NARX neural network is then used to accurately predict the future battery voltage. A boxplot is then used for the battery fault diagnosis and early warning based on the predicted voltage. Finally, the experimental results (in a new dataset) and a comparative study with a back propagation (BP) neural network not only validate the high precision, all-climate applicability, strong robustness and superiority of the proposed NARX model but also verify the fault diagnosis and early warning ability of the boxplot. In summary, the proposed fault diagnosis and prognosis approach is promising in real electric vehicle applications.

show abstract

Section: Voltage Prediction Resultsmentioning

confidence: 99%

A Fault Diagnosis and Prognosis Method for Lithium-Ion Batteries Based on a Nonlinear Autoregressive Exogenous Neural Network and Boxplot

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Average Euclidean distance (AED) and stacked denoising autoencoder (SDA) methods are used as powerful DL prediction methods. Using a nonuniform sampling of power data and DNN, a noninvasive load monitoring method was proposed [83]. The schematic and framework of the LSTM-based state prediction network can be described as shown in Figure 8.…”

Section: Application Analysis From Other Studiesmentioning

confidence: 99%

A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries

et al. 2022

View full text Add to dashboard Cite

Lithium-ion batteries are widely used as effective energy storage and have become the main component of power supply systems. Accurate battery state prediction is key to ensuring reliability and has significant guidance for optimizing the performance of battery power systems and replacement. Due to the complex and dynamic operations of lithium-ion batteries, the state parameters change with either the working condition or the aging process. The accuracy of online state prediction is difficult to improve, which is an urgent issue that needs to be solved to ensure a reliable and safe power supply. Currently, with the emergence of artificial intelligence (AI), battery state prediction methods based on data-driven methods have high precision and robustness to improve state prediction accuracy. The demanding characteristics of test time are reduced, and this has become the research focus in the related fields. Therefore, the convolutional neural network (CNN) was improved in the data modeling process to establish a deep convolutional neural network ensemble transfer learning (DCNN-ETL) method, which plays a significant role in battery state prediction. This paper reviews and compares several mathematical DCNN models. The key features are identified on the basis of the modeling capability for the state prediction. Then, the prediction methods are classified on the basis of the identified features. In the process of deep learning (DL) calculation, specific criteria for evaluating different modeling accuracy levels are defined. The identified features of the state prediction model are taken advantage of to give relevant conclusions and suggestions. The DCNN-ETL method is selected to realize the reliable state prediction of lithium-ion batteries.

show abstract

“…When processing sequential data, traditional feedforward NN lacks the ability of incorporating historical information ( Shu et al., 2021b ). To enable that the feedforward NNs can process the sequence data with varying length, recurrent NN (RNN) is advanced to memorize the historical information with different lengths by using neurons with self-feedback ( Zhao et al., 2019 ). At time step t , the information from the previous layer

and the previous position

are inputted into the recurrent neuron.…”

Section: Machine-learning-based Soh Predictionmentioning

confidence: 99%

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

Shu

Shen

et al. 2021

iScience

View full text Add to dashboard Cite

Summary Accurate state of health (SOH) prediction is significant to guarantee operation safety and avoid latent failures of lithium-ion batteries. With the development of communication and artificial intelligence technologies, a body of researches have been performed toward precise and reliable SOH prediction method based on machine learning (ML) techniques. In this paper, the conception of SOH is defined, and the state-of-the-art prediction methods are classified based on their primary implementation procedure. As an essential step in ML-based SOH algorithms, the health feature extraction methods reported in the literature are comprehensively surveyed. Next, an exhausted comparison is conducted to elaborate the development of ML-based SOH prediction techniques. Not only their advantages and disadvantages of the application in SOH prediction are reviewed but also their accuracy and execution process are fully discussed. Finally, pivotal challenges and corresponding research directions are provided for more reliable and high-fidelity SOH prediction.

show abstract

A Compact Methodology Via a Recurrent Neural Network for Accurate Equivalent Circuit Type Modeling of Lithium-Ion Batteries

Cited by 59 publications

References 33 publications

A Fault Diagnosis and Prognosis Method for Lithium-Ion Batteries Based on a Nonlinear Autoregressive Exogenous Neural Network and Boxplot

A Fault Diagnosis and Prognosis Method for Lithium-Ion Batteries Based on a Nonlinear Autoregressive Exogenous Neural Network and Boxplot

A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

Contact Info

Product

Resources

About