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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM68061

Digital Twin Enabled Smart E Fuse with Battery SOC and Harness Health Monitoring

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM68061

Volume:

10 |

10 |

Oct 2026

Pages:

1-5

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

PBN

Prashant Bhimrao Nikam

Electronics

Siddhant College of Engineering, Sudhambare

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DAB

Dr. Ashwini Bade

Electronics

Siddhant College of Engineering, Sudhambare

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DPP

Dr. Prabhat Pallav

Electronics

Siddhant College of Engineering, Sudhambare

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PSD

Prof. Swati Deshmukh

Electronics

Siddhant College of Engineering, Sudhambare

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Abstract

This paper proposes a four-channel smart electronic fuse box for 12 V auxiliary loads with battery state-of-charge and wiring-harness condition monitoring. Each branch uses a power MOSFET as a resettable switching and protection element. Two branches supply LED loads and two branches supply DC motors. Current and MOSFET temperature are measured for every branch. Bus sensing supports battery state-of-charge estimation, while paired voltage measurements across a controlled connector test section quantify harness voltage drop. An ESP32 performs acquisition, calibration, local overcurrent and overtemperature protection, gate control, diagnostics, and UART transmission. When state of charge falls below calibrated limits, the controller sheds selected high-power motor loads while retaining essential low-power loads. A PC application maintains synchronized digital-twin objects, records time-series data, displays all measured parameters, classifies harness condition from good to critical, and evaluates an AI anomaly score. The architecture keeps deterministic protection on the embedded controller so loss of the PC or serial connection cannot prevent a trip. The proposed validation covers normal loading, LED overload, motor startup, motor stall, progressive heating, sensor faults, and communication interruption. The design provides a repeatable laboratory platform for selective protection and predictive condition-monitoring research.

KEYWORDS

electronic fuse MOSFET protection ESP32 battery state of charge load shedding harness health voltage-drop monitoring digital twin anomaly detection

Cite This Article

Prashant Bhimrao Nikam, Dr. Ashwini Bade, Dr. Prabhat Pallav, Prof. Swati Deshmukh, "Digital Twin Enabled Smart E Fuse with Battery SOC and Harness Health Monitoring," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-5, Oct 2026.

Full Article/Conclusion

This paper presented a four-channel smart e-fuse architecture that protects two LED loads and two DC motors with independent MOSFET switches. Current and MOSFET temperature are measured on every channel, battery measurements support SOC estimation and priority-based motor load shedding, and paired voltage measurements identify added harness resistance. The ESP32 performs deterministic protection and transfers structured data over UART, while the PC maintains synchronized branch, battery, and harness digital twins. The principal design decision is to keep the trip, low-SOC shedding, and critical-harness response independent of communication and AI prediction. This makes the platform suitable for controlled experiments in selective protection, motor-stall detection, thermal monitoring, battery-aware energy management, connector degradation detection, serial data integrity, and predictive warning. The dashboard makes all parameters and the GOOD-to-CRITICAL harness condition visible, while the proposed validation matrix defines the evidence required before numerical performance claims are made.

References

[1] Espressif Systems, ESP32 Series Datasheet, Version 5.3, 2026. [2] Texas Instruments, ADS111x Ultra-Small, Low-Power, I2C-Compatible, 860-SPS, 16-Bit ADCs with Internal Reference, Oscillator, and Programmable Comparator, Rev. E. [3] F. Tao, J. Cheng, Q. Qi, M. Zhang, H. Zhang, and F. Sui, “Digital twin-driven product design, manufacturing and service with big data,” International Journal of Advanced Manufacturing Technology, vol. 94, pp. 3563-3576, 2018, doi: 10.1007/s00170-017-0233-1. [4] F. T. Liu, K. M. Ting, and Z.-H. Zhou, “Isolation Forest,” Proceedings of the 2008 IEEE International Conference on Data Mining, pp. 413-422, 2008, doi: 10.1109/ICDM.2008.17. [5] L. Breiman, “Random Forests,” Machine Learning, vol. 45, pp. 5-32, 2001, doi: 10.1023/A:1010933404324. [6] G. L. Plett, “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 1. Background,” Journal of Power Sources, vol. 134, no. 2, pp. 252-261, 2004, doi: 10.1016/j.jpowsour.2004.02.031. [7] R. S. Timsit, “Electrical contact resistance: properties of stationary interfaces,” IEEE Transactions on Components and Packaging Technologies, vol. 22, no. 1, pp. 85-98, 1999, doi: 10.1109/6144.748837. [8] IEC 60747-8, Semiconductor devices - Discrete devices - Part 8: Field-effect transistors. [9] ISO 26262, Road vehicles - Functional safety, second edition, 2018. [10] Scikit-learn Developers, “Novelty and Outlier Detection,” Scikit-learn User Guide.

Editorial & Review Transparency

Publication Date

10/19/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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