A Zero-Space algorithm for Negative Cost Cycle Detection in networks

Engine knock determination has been conducted in various ways for spark timing calibration. In the present study, a knock classification model was developed using a machine learning algorithm. Wavelet packet decomposition (WPD) and ensemble empirical mode decomposition (EEMD) were employed for the characterization of the in-cylinder pressure signals from the experimental engine. The WPD was used to calculate 255 features from seven decomposition levels. EEMD provided total 70 features from their intrinsic mode functions (IMF). The experimental engine was operated at advanced spark timings to induce knocking under various engine speeds and load conditions. Three knock intensity metrics were employed to determine that the dataset included 4158 knock cycles out of a total of 66,000 cycles. The classification model trained with 66,000 cycles achieved an accuracy of 99.26% accuracy in the knock cycle detection. The neighborhood component analysis revealed that seven features contributed significantly to the classification. The classification model retrained with the seven significant features achieved an accuracy of 99.02%. Although the misclassification rate increased in the normal cycle detection, the feature selection decreased the model size from 253 to 8.25 MB. Finally, the compact classification model achieved an accuracy of 99.95% with the second dataset obtained at the knock borderline (KBL) timings, which validates that the model is sufficient for the KBL timing determination.

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Sub-cycle detection of incipient cable splice faults to prevent cable damage

2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134) ◽

10.1109/pess.2000.867545 ◽

2002 ◽

Cited By ~ 15

Author(s):

L.A. Kojovic ◽

C.W. Williams

Keyword(s):

Cable Damage ◽

Cycle Detection

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Wearable PVDF transducer for biomechanical energy harvesting and gait cycle detection

2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES) ◽

10.1109/iecbes.2016.7843415 ◽

2016 ◽

Cited By ~ 5

Author(s):

Antonino Proto ◽

Benish Fida ◽

Ivan Bernabucci ◽

Daniele Bibbo ◽

Silvia Conforto ◽

...

Keyword(s):

Energy Harvesting ◽

Gait Cycle ◽

Cycle Detection

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Development of Life-cycle Detection and Extending Function for Reusing Automotive Lithium-ion Battery – Verified in 52V LiMnNiCoO2 Platform

10.21203/rs.3.rs-20563/v1 ◽

2020 ◽

Author(s):

Wu-Yang Sean ◽

Ana Pacheco

Keyword(s):

Life Cycle ◽

Lithium Ion Battery ◽

Management System ◽

Open Circuit Voltage ◽

Lithium Ion ◽

Open Circuit ◽

Battery Pack ◽

Battery Management System ◽

Battery Management ◽

Cycle Detection

Abstract For reusing automotive lithium-ion battery, an in-house battery management system is developed. To overcome the issues of life cycle and capacity of reused battery, an online function of estimating battery’s internal resistance and open-circuit voltage based on adaptive control theory are applied for monitoring life cycle and remained capacity of battery pack simultaneously. Furthermore, ultracapacitor is integrated in management system for sharing peak current to prolong life span of reused battery pack. The discharging ratio of ultracapacitor is adjusted manually under Pulse-Width-Modulation signal in battery management system. In case study in 52V LiMnNiCoO2 platform, results of estimated open-circuit voltage and internal resistances converge into stable values within 600(s). These two parameters provide precise estimation for electrical capacity and life cycle. It also shows constrained voltage drop both in the cases of 25% to 75% of ultracapacitors discharging ratio compared with single battery. Consequently, the Life-cycle detection and extending functions integrated in battery management system as a total solution for reused battery are established and verified.

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