A Surface Temperature Estimation Method for Lithium-Ion Battery Using Enhanced GRU-RNN

Yao, Qi; Lu, Dylan Dah-Chuan; Lei, Gang

doi:10.1109/tte.2022.3197927

Cited by 25 publications

(4 citation statements)

References 28 publications

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“…In this study, the ML algorithm is used to estimate the battery surface temperature based on multiple time-series signals and features. The estimation itself is a time-series task since the temperature at the current time step can be affected by the previous status and inputs [21]. Meanwhile, there also exist underlying relationships between the input signals such as current, voltage, and SOC.…”

Section: The Cnn-lstm Modelmentioning

confidence: 99%

“…Therefore, tracking the SOT of those cells by taking advantage of non-temperature signals such as current and voltage is of paramount importance to the safety and performance of the whole battery pack in EVs, which makes this work different from existing SOT estimation/prediction studies relying on a surface temperature sensor [9]- [12]. Generally, there are three main methods to achieve sensorless SOT estimation in the existing literature, based on battery impedance [13]- [15], battery thermal models [16]- [18], and machine learning (ML) algorithms [19]- [21]. In impedance-based estimation, the relationship between battery temperature and impedance parameters (e.g., real part, imaginary part, and phase) will be calibrated offline by selecting an optimal frequency under which the impedance parameters are sensitive to battery temperature while insensitive to the state of charge (SOC) and state of health (SOH) [22], [23].…”

mentioning

confidence: 99%

“…Closed-loop observers are designed based on simplified thermal models to estimate battery temperature through voltage or impedance feedback [17], [24]. Regarding data-driven estimations, mature ML algorithms like artificial neural networks (ANNs) can be applied to recognize the underlying data pattern between measurements and battery temperatures by ignoring the complicated thermal dynamics of LIBs [19]- [21]. Nevertheless, the aforementioned three methods have limitations.…”

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confidence: 99%

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Sensorless Temperature Monitoring of Lithium-Ion Batteries by Integrating Physics With Machine Learning

Zheng

Che

et al. 2024

IEEE Trans. Transp. Electrific.

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The large-scale application of lithium-ion batteries in electric vehicles requires meticulous battery management to guarantee vehicular safety and performance. Temperatures play a significant role in the safety, performance, and lifetime of lithium-ion batteries. Therefore, the state of temperature (SOT) of batteries should be monitored timely by the battery management system. Due to limited onboard temperature sensors in electric vehicles, the SOT of most batteries must be estimated through other measured signals such as current and voltage. To this end, this paper develops an accurate method to estimate the surface temperature of batteries by combing the physics-based thermal model with machine learning. A lumped-mass thermal model is applied to provide prior knowledge of battery temperatures for machine learning. Temperature-related feature, such as internal resistance, is extracted in real-time and fed into the machine learning framework as supplementary inputs to enhance the accuracy of the estimation. A machine learning model, which combines a convolutional neural network with a long short-term memory neural network, is sequentially integrated with the thermal model to learn the mismatch between the model outputs and the real temperature values. The proposed method has been verified against experimental results, with accuracy improvement of 79.37% and 86.24% compared to conventional pure thermal model-based and pure data-driven approaches respectively.

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Section: The Cnn-lstm Modelmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Sensorless Temperature Monitoring of Lithium-Ion Batteries by Integrating Physics With Machine Learning

Zheng

Che

et al. 2024

IEEE Trans. Transp. Electrific.

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“…However, the temperature sensing resolution in typical BMS is low due to the constraint of system complexity and cost. Therefore, the overheating diagnostic is dedicated to the accurate estimation of battery temperature with BMS measurements [89]. Hussein et al [90] proposed an artificial neural network (ANN) model with reduced complexity for sensor-less temperature estimation of LIB.…”

Section: A) Overheating Diagnosticmentioning

confidence: 99%

Multilevel Data-Driven Battery Management: From Internal Sensing to Big Data Utilization

Wei,

Liu,

Liu

et al. 2023

IEEE Trans. Transp. Electrific.

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Battery management system (BMS) is essential for the safety and longevity of lithium-ion battery (LIB) utilization. With the rapid development of new sensing techniques, artificial intelligence and the availability of huge amounts of battery operational data, data-driven battery management has attracted ever-widening attention as a promising solution. This review article overviews the recent progress and future trend of datadriven battery management from a multi-level perspective. The widely-explored data-driven methods relying on routine measurements of current, voltage, and surface temperature are reviewed first. Within a deeper understanding and at the microscopic level, emerging management strategies with multidimensional battery data assisted by new sensing techniques have been reviewed. Enabled by the fast growth of big data technologies and platforms, the efficient use of battery big data for enhanced battery management is further overviewed. This belongs to the upper and the macroscopic level of the data-driven BMS framework. With this endeavor, we aim to motivate new insights into the future development of next-generation data-driven battery management.

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Integrated thermal error modeling and compensation of machine tool feed system using subtraction-average-based optimizer-based CNN-GRU neural network

Yang,

Sun,

Yang

et al. 2024

Int J Adv Manuf Technol

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A Surface Temperature Estimation Method for Lithium-Ion Battery Using Enhanced GRU-RNN

Cited by 25 publications

References 28 publications

Sensorless Temperature Monitoring of Lithium-Ion Batteries by Integrating Physics With Machine Learning

Sensorless Temperature Monitoring of Lithium-Ion Batteries by Integrating Physics With Machine Learning

Multilevel Data-Driven Battery Management: From Internal Sensing to Big Data Utilization

Integrated thermal error modeling and compensation of machine tool feed system using subtraction-average-based optimizer-based CNN-GRU neural network

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