A novel combined multi-battery dataset based approach for enhanced prediction accuracy of data driven prognostic models in capacity estimation of lithium ion batteries

Nagulapati, Vijay Mohan; Lee, Hyunjun; Jung, Dawoon; Paramanantham, SalaiSargunan S; Brigljević, Boris; Choi, Yoo Seong; Lim, Hankwon

doi:10.1016/j.egyai.2021.100089

Cited by 39 publications

(5 citation statements)

References 42 publications

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“…GPR has the advantage of low computational complexity in solving high-complexity problems. Many researchers have constructed GPR battery states estimation models to solve complex nonlinear problems during the process of electric state changes [22,23,[66][67][68][69][70][71][72][73][74][75][76][77]. Wang et al [22] established a battery states estimation model based on GPR, which has prominent accuracy and robustness, with a maximum relative error within 2%.…”

Section: ) Svm Modelmentioning

confidence: 99%

Research Progress on Data-Driven Methods for Battery States Estimation of Electric Buses

Zhao

Zhou

et al. 2023

WEVJ

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Battery states are very important for the safe and reliable use of new energy vehicles. The estimation of power battery states has become a research hotspot in the development of electric buses and transportation safety management. This paper summarizes the basic workflow of battery states estimation tasks, compares, and analyzes the advantages and disadvantages of three types of data sources for battery states estimation, summarizes the characteristics and research progress of the three main models used for estimating power battery states such as machine learning models, deep learning models, and hybrid models, and prospects the development trend of estimation methods. It can be concluded that there are many data sources used for battery states estimation, and the onboard sensor data under natural driving conditions has the characteristics of objectivity and authenticity, making it the main data source for accurate power battery states estimation; Artificial neural network promotes the rapid development of deep learning methods, and deep learning models are increasingly applied in power battery states estimation, demonstrating advantages in accuracy and robustness; Hybrid models estimate the states of power batteries more accurately and reliably by comprehensively utilizing the characteristics of different types of models, which is an important development trend of battery states estimation methods. Higher accuracy, real-time performance, and robustness are the development goals of power battery states estimation methods.

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Section: ) Svm Modelmentioning

confidence: 99%

Research Progress on Data-Driven Methods for Battery States Estimation of Electric Buses

Zhao

Zhou

et al. 2023

WEVJ

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“…Data-driven approaches use patterns found in the data to build mathematical models that forecast the system's future states. The volume and caliber of accessible data that can be employed to infer model parameters determines how well data-driven techniques, such as statistical and machine learning techniques, perform [6].…”

Section: Introductionmentioning

confidence: 99%

“…The advantage of data-driven approaches is that they may be used to big, complicated systems; satisfactory results can be obtained without knowing every process or interaction. Data-driven approaches have a cheap creation cost and are simple to use [6]. Compared to physics-based models, models may be constructed quite rapidly after the data have been collected.…”

Section: Introductionmentioning

confidence: 99%

Prognostic Model of the State of GNSU Using Big Data Analysis and Neural Networks

Turchyn

2024

cit

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This research aims at creation of a prognostic model for Global Navigation Satellite Systems (GNNS) by combination of big data analysis and refined neural networks. Problem solving comes to the front where we plan to improve the reliability and resiliency of satellite navigation systems with the help of data analytics and using both historical and real-time data. Methodology: Research methodologies cover the integrating of datasets on satellites telemetry, environment and historical system, involving processing and collection of the data systematically. Analyses of Big Data use to reveal hidden patterns inside GNSS data, moreover neural networks, and particularly deep learning networks are trained to recognize complex, nonlinear patterns which may possibly be symptoms of a problem. Result: The model exhibits a degree of disruption early on thus allowing us to take a proactive approach to the problem which reduces downtime and optimizes the system overall. The qualitative assessment involves criteria like accuracy, accuracy rate, recall, and F1 score which demonstrate the models capacity of non-GNSS prediction. By its design, the model is aligning with the development of GNSS technologies and raising new obstacles to be solved, so it will remain useful in changed environments. Conclusion: This research clearly shows that gap filling can be a possible solution whereby satellite navigation systems will be embedded with predictive capabilities regardless of the fast evolving technology environment. The results also demonstrate the ways GNSS may be applied for the development of new navigation service technologies, such as in the transport, mining and agriculture sectors. The model should be monitored and improved hardly, and adaptation to new problems is extremely important for the constant scalability and reliability of GNSS

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“…The remaining service life of a Li-ion battery is defined as the number of remaining charge-discharge cycles before the battery capacity drops to the rated failure threshold (Zhao et al, 2019). Scholars have conducted a lot of research on RUL prediction of Li-ion batteries, and great progress has been made in battery modeling and state estimation methods, which can be broadly classified into three categories: mechanistic model-based methods, data-driven methods, and model-and data-driven fusion-based methods (Liu et al, 2019b;Feng et al, 2020;Nagulapati et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Remaining Useful Life of the Lithium-Ion Battery Based on Improved Particle Filtering

Zhao

2022

Front. Energy Res.

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Remaining useful life (RUL) prediction of lithium-ion batteries plays an important role in battery failure prediction and health management (PHM). By accurately predicting the RUL of the battery, the battery can be replaced accordingly, thereby effectively avoiding the occurrence of an accident and ensuring the normal operation of the entire system. In the prediction of the remaining service life of lithium-ion batteries, it is difficult to ensure accuracy due to the problem of particle degradation and the influence of singular values in the particle filter algorithm. In view of these problems, this article introduces the unscented Kalman algorithm to improve the particle filter algorithm from the perspective of re-weighting the particles, so as to improve the accuracy of the prediction results of the remaining service life of lithium-ion batteries. The improved particle filter is simulated and verified using the battery sample data in the Arbin experimental test platform. Comparing the simulation results with the traditional particle filter method, when the number of reference samples is the same, the PDF width of the prediction results of the improved particle filter algorithm is slightly smaller than that of the particle filter algorithm, indicating that the fluctuation of the prediction result is more accurate. It is proved that the improved particle filter method proposed in this article can provide more accurate battery RUL prediction results and can effectively improve the accuracy and robustness of the remaining service life prediction of lithium-ion batteries.

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A novel combined multi-battery dataset based approach for enhanced prediction accuracy of data driven prognostic models in capacity estimation of lithium ion batteries

Cited by 39 publications

References 42 publications

Research Progress on Data-Driven Methods for Battery States Estimation of Electric Buses

Research Progress on Data-Driven Methods for Battery States Estimation of Electric Buses

Prognostic Model of the State of GNSU Using Big Data Analysis and Neural Networks

Prediction of Remaining Useful Life of the Lithium-Ion Battery Based on Improved Particle Filtering

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