Zobrazeno 1 - 10
of 106
pro vyhledávání: '"Lu, Qiugang"'
Lithium-ion batteries are pivotal to technological advancements in transportation, electronics, and clean energy storage. The optimal operation and safety of these batteries require proper and reliable estimation of battery capacities to monitor the
Externí odkaz:
http://arxiv.org/abs/2407.16036
Optimizing charging protocols is critical for reducing battery charging time and decelerating battery degradation in applications such as electric vehicles. Recently, reinforcement learning (RL) methods have been adopted for such purposes. However, R
Externí odkaz:
http://arxiv.org/abs/2406.12309
Fast charging has attracted increasing attention from the battery community for electrical vehicles (EVs) to alleviate range anxiety and reduce charging time for EVs. However, inappropriate charging strategies would cause severe degradation of batter
Externí odkaz:
http://arxiv.org/abs/2304.04195
Autor:
Chowdhury, Myisha A., Lu, Qiugang
Proportional-integral-derivative (PID) controllers have been widely used in the process industry. However, the satisfactory control performance of a PID controller depends strongly on the tuning parameters. Conventional PID tuning methods require ext
Externí odkaz:
http://arxiv.org/abs/2210.02381
Autor:
Lu, Qiugang, Al-Wahaibi, Saif S. S.
Convolutional neural network (CNN) models have been widely used for fault diagnosis of complex systems. However, traditional CNN models rely on small kernel filters to obtain local features from images. Thus, an excessively deep CNN is required to ca
Externí odkaz:
http://arxiv.org/abs/2210.01727
Autor:
Al-Wahaibi, Saif S. S., Lu, Qiugang
Deep learning techniques have become prominent in modern fault diagnosis for complex processes. In particular, convolutional neural networks (CNNs) have shown an appealing capacity to deal with multivariate time-series data by converting them into im
Externí odkaz:
http://arxiv.org/abs/2210.01077
Autor:
Li, Yun, Wang, Yixiu, Chen, Yifu, Hua, Kaixun, Ren, Jiayang, Mozafari, Ghazaleh, Lu, Qiugang, Cao, Yankai
This paper proposes a deep learning-based optimal battery management scheme for frequency regulation (FR) by integrating model predictive control (MPC), supervised learning (SL), reinforcement learning (RL), and high-fidelity battery models. By takin
Externí odkaz:
http://arxiv.org/abs/2201.01166
PID control has been the dominant control strategy in the process industry due to its simplicity in design and effectiveness in controlling a wide range of processes. However, traditional methods on PID tuning often require extensive domain knowledge
Externí odkaz:
http://arxiv.org/abs/2112.15187
We present a Bayesian optimization (BO) framework for tuning model predictive controllers (MPC) of central heating, ventilation, and air conditioning (HVAC) plants. This approach treats the functional relationship between the closed-loop performance
Externí odkaz:
http://arxiv.org/abs/2009.14175
Autor:
Lu, Qiugang, Zavala, Victor M.
We present a data-driven model predictive control (MPC) framework for systems with high state-space dimensionalities. This work is motivated by the need to exploit sensor data that appears in the form of images (e.g., 2D or 3D spatial fields reported
Externí odkaz:
http://arxiv.org/abs/2006.06727