Numerical Simulations for Lithium-Ion Battery Pack Cooled by Different Minichannel Cold Plate Arrangements

Li, Yulong; Bai, Minli; Zhou, Zhifu; Wu, Wei‐Tao; Gao, Linsong; Li, Yang; Yang, Yunjie; Li, Yubai; Song, Yongchen

doi:10.1155/2023/8207527

Cited by 8 publications

(2 citation statements)

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“…The three primary modeling methods used are physical, numerical, and composite. Numerical models (NM) refer to the usage of computer codes (commercial, open source, or home-produced software) [11], and physical models (PM) refer to the usage of laboratory models at a suitable scale (micro, small, medium, and large-scale models) to study the process of interest, while composite models (CM) refer to the combined and proportional use of physical and numerical models [12]. At this phase, the conventional method of forecasting waves by oceanographers is numerical models [6].…”

Section: Introductionmentioning

confidence: 99%

“…The most commonly used models nowadays are Simulating Waves Nearshore (SWAN) [6][7][8][9][10][11][12][13], which the Delft University of Technology developed and introduced by Booij et al in 1999 [14], and WaveWatch III (WW3) was introduced by Tolman et al in 2009 [15]. WaveWatch III (WW3) is a thirdgeneration wave model developed by National Oceanic and Atmospheric Administration (NOAA)/ National Centers for Environmental Prediction (NCEP) [16].…”

Section: Introductionmentioning

confidence: 99%

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Coastal Wave Modeling and Forecasting with LSTM Optimization For Sustainable Energy Harvesting

Lawal¹,

Yassin²,

Lai³

2023

Preprint

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Coastal wave modeling and forecasting are essential in oceanography, sustainable marine energy, and ocean engineering. Precise forecasting of wave speed and direction are crucial for offshore operations, marine energy, risk management, environmental management, coastal and sustainable maritime management. The coastline of Brunei Darussalam can generate between 15 and 126 Giga Watt of wave energy. In this experimental research, we used two numerical approaches, the finite difference, and spectral element methods, to model and simulate wave speed and direction. We calculated the mean error between numerical and analytical solutions. We proposed a novel, promising, univariate time series forecasting model by combining the Long Short-Term (LSTM) with KerasTuner hyperparameters tuning and optimization techniques. This method helps us to improve the accuracy and efficiency of time-series forecasting.. The experimental data was computed from high-precision Acoustic Doppler Current Profiler (ADCP) sensor data. This research is part of the preliminary feasibility analysis of wave energy production in Brunei Darussalam and net zero commitment for a sustainable environment. Seven independent forecast experiments were performed for wave speed and direction in degree and radian units for 1, 3, 6, 8, 10, 12, and 24 hours. Mean squared error (MSE) was adopted as a metric for both training and testing. The experimental results reveal that the wave speed forecast has the lowest MSE compared to direction, regardless of the unit of measure, but has a longer duration. In addition, the direction forecast in the degree unit has the lowest errors compared to the unit of radians; the latter has a longer running time than the former. The model has delivered optimal results throughout the experiments with minor training and test errors. We conducted a thorough evaluations on two benchmark time series datasets, which include the study dataset and air quality index dataset, to validate the performance of the proposed model with other models. The proposed model outperforms cutting-edge forecasting models, such as the conventional LSTM, ARIMA, and Prophet. The model has the slightest forecast error compared to the existing literature’s result.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Coastal Wave Modeling and Forecasting with LSTM Optimization For Sustainable Energy Harvesting

Lawal¹,

Yassin²,

Lai³

2023

Preprint

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Numerical simulations on hybrid thermal management of mini-channel cold plate and PCM for lithium-ion batteries

Liu,

Zhou,

et al. 2024

Applied Thermal Engineering

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Performance analysis of reinforcement learning algorithms on intelligent closed-loop control on fluid flow and convective heat transfer

Wang

Aubry

et al. 2023

Physics of Fluids

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This paper investigates the performance of several most popular deep reinforcement learning (DRL) algorithms applied to fluid flow and convective heat transfer systems, providing credible guidance and evaluation on their characteristics and performance. The studied algorithms are selected by considering the popularity, category, and advancement for guaranteeing the significance of the current study. The effectiveness and feasibility of all DRL algorithms are first demonstrated by studying a two-dimensional multi-heat-source cooling problem. Compared with the best manually optimized control, all DRL algorithms can find better control strategies that realize a further temperature reduction of 3–7 K. For problems with complex control objectives and environments, PPO (proximal policy optimization) shows an outstanding performance that accurately and dynamically constrains the oscillation of the solid temperature within 0.5 K around the target value, which is far beyond the capability of the manually optimized control. With the presented performance and the supplemented generalization test, the characteristic and specialty of the DRL algorithms are analyzed. The value-based methods have better training efficiency on simple cooling tasks with linear reward, while the policy-based methods show remarkable convergence on demanding tasks with nonlinear reward. Among the algorithms studied, the single-step PPO and prioritized experience replay deep Q-networks should be highlighted: the former has the advantage of considering multiple control targets and the latter obtains the best result in all generalization testing tasks. In addition, randomly resetting the environment is confirmed to be indispensable for the trained agent executing long-term control, which is strongly recommended to be included in follow-up studies.

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Numerical Simulations for Lithium-Ion Battery Pack Cooled by Different Minichannel Cold Plate Arrangements

Cited by 8 publications

References 50 publications

Coastal Wave Modeling and Forecasting with LSTM Optimization For Sustainable Energy Harvesting

Coastal Wave Modeling and Forecasting with LSTM Optimization For Sustainable Energy Harvesting

Numerical simulations on hybrid thermal management of mini-channel cold plate and PCM for lithium-ion batteries

Performance analysis of reinforcement learning algorithms on intelligent closed-loop control on fluid flow and convective heat transfer

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