UNIVERSITA’ DI PERUGIA – Engineering department

UniPG Engineering – Advanced Battery — RES4NET
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Università degli Studi di Perugia — Engineering department

Advanced Battery Characterization
and Modeling

Sensing, system identification and digital twins for rechargeable batteries

Industry Energy
Università di Perugia – Engineering department research activity

Research overview

Application field Industry
Technology Sensing, system identification and digital twins
Activity Modeling, measurement, diagnostic

Keywords

Electrical Impedance Spectroscopy (EIS) Digital Twin (DT) System Identification (SI) Multifrequency stimulus Real-time characterization State of Charge (SoC) State of Health (SoH)

Operation of the proposed solution

Advanced batteries are a key enabler of many innovative applications, including automotive, IoT-based sensing, and powering of unmanned aerial vehicles (UAVs). Early warning of battery deterioration is an important task, as it enables both predictive and proactive maintenance, preventing battery failures during operations.

The main goal of this activity is the development of advanced real-time and non-intrusive techniques for assessing the State of Charge (SoC) and the State of Health (SoH) of rechargeable batteries. Lithium-Ion Batteries are considered as the primary case study.

Target application areas:

Automotive
IoT-based sensing
UAVs (drones)

Low-cost, low-power embedded hardware is being developed, featuring multifrequency stimuli to quickly assess battery impedance across different frequencies, relating measurement results to SoC and SoH. Results are used to develop a Digital Twin that, once connected to a programmable impedance emulator, can appear as a true battery to a measuring instrument — enabling rapid prototyping and testing of embedded monitoring systems without time-consuming physical charge/discharge cycles.

Open challenges

  • 1 Realizing a low-cost, low-power embedded device running signal generation and estimation algorithms with potentially large computational cost
  • 2 The estimation hardware needs to scan a frequency interval spanning over several decades
  • 3 The synthesized EIS multifrequency stimulus must excite all frequencies with similar power

Selected bibliography

1 Crescentini M., De Angelis A., Ramilli R., De Angelis G., Tartagni M., Moschitta A., Traverso P.A., Carbone P. “Online EIS and Diagnostics on Lithium-Ion Batteries by Means of Low-Power Integrated Sensing and Parametric Modeling.” IEEE Transactions on Instrumentation and Measurement, 70, art. no. 9224889 2021
DOI: 10.1109/TIM.2020.3031185
2 Santoni F., De Angelis A., Moschitta A., Carbone P. “Digital impedance emulator for battery measurement system calibration.” Sensors, 21(21), art. no. 7377 2021
DOI: 10.3390/s21217377
3 De Angelis A., Ramilli R., Crescentini M., Moschitta A., Carbone P., Traverso P.A. “In-situ Electrochemical Impedance Spectroscopy of Battery Cells by means of Binary Sequences.” Conference Record – IEEE I2MTC, 2021, art. no. 9459998 2021
DOI: 10.1109/I2MTC50364.2021.9459998
4 Santoni F., De Angelis A., Moschitta A., Carbone P. “Analysis of the Uncertainty of EIS Battery Data Fitting to an Equivalent Circuit Model.” RTSI 2021 – Proceedings, pp. 497–501 2021
DOI: 10.1109/RTSI50628.2021.9597288
5 Carbone P., Angelis A.D., Santoni F., Moschitta A. “Measurement of the Parameters of Multiple Sinusoids Based on Binary Data.” IEEE Transactions on Instrumentation and Measurement, 70, art. no. 9239262 2021
DOI: 10.1109/TIM.2020.3033074
6 De Angelis G., De Angelis A., Moschitta A., Carbone P., Pintelon R. “Online Identification of the LC Product in Coupled Resonant Circuits.” IEEE Transactions on Instrumentation and Measurement, 69(7), pp. 4592–4603 2020
DOI: 10.1109/TIM.2019.2950583
7 Moschitta A., Comuniello A., Santoni F., De Angelis A., Carbone P., Fravolini M.L. “Statistically Efficient Simultaneous Amplitude Measurement of Multiple Linear Chirp Signals.” IEEE Transactions on Instrumentation and Measurement, 70, art. no. 9323044 2021
DOI: 10.1109/TIM.2021.3051667

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