Battery Electrode Mass Loading Prognostics and Analysis for Lithium-Ion Battery–Based Energy Storage Systems

Batteries & Supercaps www.batteries-supercaps.org Review doi.org/10.1002/batt.202200224 Lithium-ion battery (LIB) manufacturing requires a pilot stage that optimizes its characteristics. However, this process is costly and time-consuming. One way to overcome this is to use a set of computational models that act as a digital twin of the pilot line, exchanging information in real-time that can be compared with measurements to correct parameters. Here we discuss the parameters involved in each step of LIB manufacturing, show available computational modeling approaches, and discuss details about practical implementation in terms of software. Then, we analyze these parameters regarding their criticality for modeling set-up and validation, measurement accuracy, and rapidity. Presenting this in an understandable format allows identifying missing aspects, remaining challenges, and opportunities for the emergence of pilot lines integrating digital twins. Finally, we present the challenges of managing the data produced by these models. As a snapshot of the state-of-theart, this work is an initial step towards digitalizing battery manufacturing pilot lines, paving the way toward autonomous optimization.

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“…Westermeier et al [91] Severson et al [81] Duquesnoy et al [47] Liu et al [92] Rynne et al [6] Chen et al [93] Azure Machine Learning (Microsoft)…”

Section: Knime (Konstanz Information Miner)mentioning

confidence: 99%

Data Specifications for Battery Manufacturing Digitalization: Current Status, Challenges, and Opportunities

Zanotto

Dominguez

Ayerbe

et al. 2022

Batteries & Supercaps

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“…𝜇 t ch , 𝜇 t dis , 𝜇 t net are binary variables indicating the status of battery charging/discharging and system net power. It is worthy note that the turn-on/off status of P2G system and SOFC stack is dependent on the system net power status 𝜇 t net , which also determines the inclusion of different cost part C P2G and C SOFC in the objective function (9).…”

Section: Overall Operation Costmentioning

confidence: 99%

“…electrolyzer, battery, photovoltaic cell) to create a renewables-based hybrid energy system. [9,10] Therefore, SOFC has attracted more and more research interests across the industry and academia, becoming a popular research trend in recent years, especially with the huge advances in the new-generation fuel cell stack development and SOFC materials [11,12]. For example, by using the prospective cobaltbased cathode materials with the A-site elements of the perovskite (ABO 3 ) structure, the energy conversion efficiency of SOFC can be up to 65% and further improved up to 85% with heat co-generation [13].…”

Section: Introductionmentioning

confidence: 99%

Optimal control strategy for solid oxide fuel cell‐based hybrid energy system using deep reinforcement learning

Chen

Gao

Song

2022

IET Renewable Power Gen

Self Cite

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This paper proposes a self‐adaptive control strategy for solid oxide fuel cell (SOFC) based hybrid energy system using deep reinforcement learning (DRL) techniques. Highly efficient use of hydrogen in a hybrid energy system with the aid of SOFC could create a new paradigm of renewable energy ecosystem and a series of operation principles. Instead of modeling the energy system operation decision‐making process as an optimization problem, a DRL framework is used to seek the optimal control strategy with consideration of various physical constraints in the SOFC components and hybrid energy system operation. Specifically, a deep deterministic policy gradient (DDPG) algorithm is used to solve the operation problem and provide the optimal policy guiding the control actions of a tubular SOFC stack, which involves various dynamic characteristics besides the electric measurement. The learnt control strategy may not produce the best result every time, but can guarantee the ultimate benefit in a non‐deterministic way in the long‐term operation.

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“…As for the algorithm selection in model construction, in recent years, based on the large amount of data generated by the operation of the power system and the development of artificial intelligence algorithms, traditional line loss calculation methods have gradually developed to intelligent processing algorithms represented by artificial neural networks (Zhang et al, 2018). The least squares support vector machine (LSSVM) can be used for both classification and prediction Chen et al, 2021;Xia et al, 2021). The support vector regression (SVR) improves the LSSVM, which significantly increases the speed of operation (Liu et al, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Loss Prediction of Ultrahigh Voltage Transmission Lines Based on EEMD–LSTM–SVR Algorithm

et al. 2022

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Line loss prediction of ultrahigh voltage transmission lines is the key for ensuring the safe, reliable, and economical operation of the power system. However, the strong volatility of line loss brings challenges to the prediction of transmission line loss. For more accurate prediction, this article uses ensemble empirical mode decomposition (EEMD) to decompose the line loss and proposes the EEMD–LSTM–SVR prediction model. First of all, this article performs feature engineering on power flow, electric energy, and meteorological data and extracts the exponentially weighted moving average (EWMA) feature from the line loss. After the integration of the time dimension, this article mines the curve characteristics from the time series and constructs a multidimensional input dataset. Then, through ensemble empirical mode decomposition, the line loss is decomposed into high-frequency, low-frequency, and random IMFs. These IMFs and the standardized multidimensional dataset together constitute the final input dataset. In this article, each IMF fusion dataset is sent to LSTM and support vector regression models for training. In the training process, the incremental cross-validation method is used for model evaluation, and the grid search method is used for hyperparameter optimization. After evaluation, the LSTM algorithm predicts high-frequency IMF1 and 2 and random IMF4 and 5; the SVR algorithm predicts low-frequency IMF6 and 7 and random IMF3. Finally, the output value of each model is superimposed to obtain the final line loss prediction value. Also, the comparative predictions were performed using EEMD–LSTM, EEMD–SVR, LSTM, and SVR. Compared with the independent prediction models EEMD–LSTM and EEMD–SVR, the combined EEMD–LSTM–SVR algorithm has a decrease in mean absolute performance error% by 2.2 and 25.37, respectively, which fully demonstrates that the combined model has better prediction effect than the individual models. Compared with that of SVR, the MAPE% of EEMD–SVR decreases by 11.16. Compared with that of LSTM, the MAPE% of EEMD–LSTM is reduced by 32.72. The results show that EEMD decomposition of line loss series can effectively improve the prediction accuracy and reduce the strong volatility of line loss. Compared with that of the other four algorithms, EEMD–LSTM–SVR has the highest R-square of 0.9878. Therefore, the algorithm proposed in this article has the best effectiveness, accuracy, and robustness.

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Battery Electrode Mass Loading Prognostics and Analysis for Lithium-Ion Battery–Based Energy Storage Systems

Cited by 9 publications

References 37 publications

Data Specifications for Battery Manufacturing Digitalization: Current Status, Challenges, and Opportunities

Data Specifications for Battery Manufacturing Digitalization: Current Status, Challenges, and Opportunities

Optimal control strategy for solid oxide fuel cell‐based hybrid energy system using deep reinforcement learning

Loss Prediction of Ultrahigh Voltage Transmission Lines Based on EEMD–LSTM–SVR Algorithm

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