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Peer-reviewedCC BY 4.0Batteries

A Comparative Study of Diagnostic Methods for Lithium-Ion Batteries

  • Martina Marafetti1
  • Andrea Barisione2
  • Silvia Colnago2
  • Simone Barcellona3
  1. 1Department of Energy, Politecnico di Milano, 20156 Milan, Italy
  2. 2Department of Generation Technologies and Materials, Ricerca sul Sistema Energetico S.p.A., 20134 Milan, Italy
  3. 3Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy
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Abstract

Lithium-ion batteries are increasingly used in stationary energy storage and electric mobility, making reliable degradation assessment essential for improving lifetime, safety, and performance. Since aging is governed by coupled electrochemical processes influenced by temperature, state of charge (SOC), and cycling conditions, this work investigates the diagnostic information that can be extracted directly from experimental data without relying on predefined battery model fitting. A structured testing campaign was conducted on a commercial lithium nickel cobalt aluminum oxide cell at different temperature, SOC, and aging conditions. Charge/discharge voltage curve analysis, incremental capacity analysis (ICA), differential voltage analysis (DVA), electrochemical impedance spectroscopy (EIS) analysis, distribution of relaxation times (DRT) analysis, and voltage relaxation analysis were comparatively evaluated. The results show that charge/discharge voltage curve analysis provides global indicators of degradation, while ICA and DVA highlight aging-related changes in characteristic voltage features. EIS, DRT, and voltage relaxation analyses provide complementary information on resistive, interfacial, transport, and relaxation processes. Overall, the different techniques exhibit consistent aging trends and, when considered jointly, support the qualitative interpretation of degradation modes and aging mechanisms. The combined diagnostic approach provides complementary insights into battery aging directly from experimentally observable features while avoiding the uncertainties associated with predefined battery model fitting.

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Cite

BibTeXRIS
BibTeX
@article{marafetti2026comparative,
  title = {{A Comparative Study of Diagnostic Methods for Lithium-Ion Batteries}},
  author = {Martina Marafetti and Andrea Barisione and Silvia Colnago and Simone Barcellona},
  journal = {Batteries},
  year = {2026},
  month = oct,
  doi = {10.3390/batteries12100386},
  url = {https://www.mdpi.com/2313-0105/12/10/386},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - A Comparative Study of Diagnostic Methods for Lithium-Ion Batteries
AU  - Martina Marafetti
AU  - Andrea Barisione
AU  - Silvia Colnago
AU  - Simone Barcellona
PY  - 2026
DA  - 2026/10/01
JO  - Batteries
DO  - 10.3390/batteries12100386
UR  - https://www.mdpi.com/2313-0105/12/10/386
AB  - Lithium-ion batteries are increasingly used in stationary energy storage and electric mobility, making reliable degradation assessment essential for improving lifetime, safety, and performance. Since aging is governed by coupled electrochemical processes influenced by temperature, state of charge (SOC), and cycling conditions, this work investigates the diagnostic information that can be extracted directly from experimental data without relying on predefined battery model fitting. A structured testing campaign was conducted on a commercial lithium nickel cobalt aluminum oxide cell at different temperature, SOC, and aging conditions. Charge/discharge voltage curve analysis, incremental capacity analysis (ICA), differential voltage analysis (DVA), electrochemical impedance spectroscopy (EIS) analysis, distribution of relaxation times (DRT) analysis, and voltage relaxation analysis were comparatively evaluated. The results show that charge/discharge voltage curve analysis provides global indicators of degradation, while ICA and DVA highlight aging-related changes in characteristic voltage features. EIS, DRT, and voltage relaxation analyses provide complementary information on resistive, interfacial, transport, and relaxation processes. Overall, the different techniques exhibit consistent aging trends and, when considered jointly, support the qualitative interpretation of degradation modes and aging mechanisms. The combined diagnostic approach provides complementary insights into battery aging directly from experimentally observable features while avoiding the uncertainties associated with predefined battery model fitting.
SN  - 2313-0105
ER  -