407 research outputs found
Cerberus: A Deep Learning Hybrid Model for Lithium-Ion Battery Aging Estimation and Prediction Based on Relaxation Voltage Curves
The degradation process of lithium-ion batteries is intricately linked to
their entire lifecycle as power sources and energy storage devices,
encompassing aspects such as performance delivery and cycling utilization.
Consequently, the accurate and expedient estimation or prediction of the aging
state of lithium-ion batteries has garnered extensive attention. Nonetheless,
prevailing research predominantly concentrates on either aging estimation or
prediction, neglecting the dynamic fusion of both facets. This paper proposes a
hybrid model for capacity aging estimation and prediction based on deep
learning, wherein salient features highly pertinent to aging are extracted from
charge and discharge relaxation processes. By amalgamating historical capacity
decay data, the model dynamically furnishes estimations of the present capacity
and forecasts of future capacity for lithium-ion batteries. Our approach is
validated against a novel dataset involving charge and discharge cycles at
varying rates. Specifically, under a charging condition of 0.25C, a mean
absolute percentage error (MAPE) of 0.29% is achieved. This outcome underscores
the model's adeptness in harnessing relaxation processes commonly encountered
in the real world and synergizing with historical capacity records within
battery management systems (BMS), thereby affording estimations and
prognostications of capacity decline with heightened precision.Comment: 3 figures, 1 table, 9 page
AdaER: An Adaptive Experience Replay Approach for Continual Lifelong Learning
Continual lifelong learning is an machine learning framework inspired by
human learning, where learners are trained to continuously acquire new
knowledge in a sequential manner. However, the non-stationary nature of
streaming training data poses a significant challenge known as catastrophic
forgetting, which refers to the rapid forgetting of previously learned
knowledge when new tasks are introduced. While some approaches, such as
experience replay (ER), have been proposed to mitigate this issue, their
performance remains limited, particularly in the class-incremental scenario
which is considered natural and highly challenging. In this paper, we present a
novel algorithm, called adaptive-experience replay (AdaER), to address the
challenge of continual lifelong learning. AdaER consists of two stages: memory
replay and memory update. In the memory replay stage, AdaER introduces a
contextually-cued memory recall (C-CMR) strategy, which selectively replays
memories that are most conflicting with the current input data in terms of both
data and task. Additionally, AdaER incorporates an entropy-balanced reservoir
sampling (E-BRS) strategy to enhance the performance of the memory buffer by
maximizing information entropy. To evaluate the effectiveness of AdaER, we
conduct experiments on established supervised continual lifelong learning
benchmarks, specifically focusing on class-incremental learning scenarios. The
results demonstrate that AdaER outperforms existing continual lifelong learning
baselines, highlighting its efficacy in mitigating catastrophic forgetting and
improving learning performance.Comment: 18 pages, 26 figure
The Application of Time-Delay Dependent H
This paper uses a time-delay dependent H∞ control model to analyze the effect of manufacturing decisions on the process of transmission from resources to capability. We establish a theoretical framework of manufacturing management process based on three terms: resource, manufacturing decision, and capability. Then we build a time-delay H∞ robust control model to analyze the robustness of manufacturing management. With the state feedback controller between manufacturing resources and decision, we find that there is an optimal decision to adjust the process of transmission from resources to capability under uncertain environment. Finally, we provide an example to prove the robustness of this model
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