pith. sign in

arxiv: 2603.29107 · v2 · submitted 2026-03-31 · 📡 eess.SY · cs.SY· eess.SP

Design of an embedded hardware platform for cell-level diagnostics in commercial battery modules

Gabriele Marini , Alessandro Colombo , Andrea Lanubile , William A. Paxton , Simona Onori This is my paper

Pith reviewed 2026-05-14 00:24 UTC · model grok-4.3

classification 📡 eess.SY cs.SYeess.SP
keywords battery modulescell diagnosticshardware platformstate-of-healthvoltage balancingelectric vehicle batteriesnon-invasive testing
0
0 comments X

The pith

A hardware platform enables safe cell-level diagnostics inside assembled commercial battery modules without disassembly.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Battery aging studies typically focus on individual cells, yet evaluating performance inside fully assembled modules remains difficult because cell signals are hard to reach. This paper designs an embedded testing platform that adds voltage sensors, balancing circuitry, and a microcontroller to commercial modules from the Audi e-tron pack. The system runs across all 36 modules and keeps cell voltage imbalances within a set reference while permitting simultaneous, non-invasive access to cell signals. The result is accurate cell state-of-health assessment plus measurement of internal module heterogeneity that supports maintenance and repurposing decisions.

Core claim

The platform integrates voltage sensors, balancing circuitry, and a microcontroller to monitor and control cells in fully assembled modules. Testing across all 36 modules shows that cell voltage imbalances are constrained to a defined reference value, allowing safe access to cell signals for state-of-health assessments and quantification of internal module heterogeneity.

What carries the argument

Embedded hardware platform integrating voltage sensors, balancing circuitry, and a microcontroller for simultaneous cell screening without module disassembly.

If this is right

  • Cell signals become accessible for accurate non-invasive state-of-health assessments.
  • Internal heterogeneity inside each module can be quantified.
  • Data supports decisions for first-life and second-life battery applications.
  • Battery pack maintenance and repurposing become more efficient.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same integration approach could apply to battery packs from other manufacturers.
  • Further development might allow continuous diagnostics while the vehicle is in operation.
  • Data from many modules could improve long-term aging predictions used in fleet management.

Load-bearing premise

The hardware can be integrated into commercial modules to enable safe, simultaneous cell screening without disassembling the modules or compromising module integrity.

What would settle it

A demonstration that the platform cannot be installed without disassembly or that it damages module integrity would disprove the central claim.

Figures

Figures reproduced from arXiv: 2603.29107 by Alessandro Colombo, Andrea Lanubile, Gabriele Marini, Simona Onori, William A. Paxton.

Figure 1
Figure 1. Figure 1: FIGURE 1 [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIGURE 2 [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIGURE 3 [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIGURE 4 [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: shows the C/3 discharge capacities (top) and C/3 dis￾charge energies (bottom) for all Cells in the pack, computed using (3), (5) and Algorithm 1. Each module test character￾izes a set of three Cells. On the left-hand side, capacities and energies are plotted according to the Cell position within the pack (following the module ordering) highlighting intrinsic variations. The capacity and energy trends are c… view at source ↗
Figure 6
Figure 6. Figure 6: FIGURE 6 [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: FIGURE 7 [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: FIGURE 8 [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: FIGURE 9 [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: FIGURE 10 [PITH_FULL_IMAGE:figures/full_fig_p008_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: FIGURE 11 [PITH_FULL_IMAGE:figures/full_fig_p010_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: shows that the linear correction model (best linear fitting using ordinary least square) yields model residuals smaller than 1 mV across the whole voltage operating range. The model fittings for the three ADC channels are reported in [PITH_FULL_IMAGE:figures/full_fig_p011_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: FIGURE 13 [PITH_FULL_IMAGE:figures/full_fig_p012_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: FIGURE 14 [PITH_FULL_IMAGE:figures/full_fig_p013_14.png] view at source ↗
read the original abstract

While battery aging is commonly studied at the cell-level, evaluating aging and performance within battery modules remains a critical challenge. Testing cells within fully assembled modules requires hardware solutions to access cell-level information without compromising module integrity. In this paper, we design and develop a hardware testing platform to monitor and control the internal cells of battery modules contained in the Audi e-tron battery pack. The testing is performed across all 36 modules of the pack. The platform integrates voltage sensors, balancing circuitry, and a micro-controller to enable safe, simultaneous cell screening without disassembling the modules. Using the proposed testing platform, cell voltage imbalances within each module are constrained to a defined reference value, and cell signals can be safely accessed, enabling accurate and non-invasive cell-level state-of-health assessments. On a broader scale, our solution allows for the quantification of internal heterogeneity within modules, providing valuable insights for both first- and second-life applications and supporting efficient battery pack maintenance and repurposing.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript describes the design of an embedded hardware platform for cell-level diagnostics in commercial battery modules from the Audi e-tron pack. It integrates voltage sensors, balancing circuitry, and a microcontroller to enable non-invasive monitoring and balancing across all 36 modules without disassembly, claiming that cell voltage imbalances are constrained to a defined reference value and that cell signals can be safely accessed for accurate state-of-health assessments.

Significance. If the performance claims are substantiated with quantitative data, the platform could provide a practical method for assessing module-level heterogeneity, supporting battery repurposing and maintenance in first- and second-life applications.

major comments (3)
  1. [Results] Results section: the central claim that imbalances are constrained to a defined reference value is unsupported by any measured post-balancing voltage spreads (e.g., max/min deviation in mV), statistics across the 36 modules, or explicit reference value; without these metrics the accuracy and constraint assertions cannot be evaluated.
  2. [Experimental validation] Safety verification: the assertion of safe, non-invasive access without compromising module integrity lacks supporting data such as isolation resistance measurements, thermal profiles during balancing, or fault-injection test outcomes.
  3. [Discussion] SoH assessment: the claim of enabling accurate cell-level state-of-health evaluations is not accompanied by any error metrics, comparison to reference methods, or validation against known cell conditions.
minor comments (2)
  1. [Abstract] Abstract: the phrase 'defined reference value' is used without stating its numerical value or derivation method.
  2. [Figures] Figure captions: hardware schematic diagrams would benefit from explicit component labels and pinouts for reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight areas where the manuscript can be strengthened with additional quantitative evidence. We address each major point below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Results] Results section: the central claim that imbalances are constrained to a defined reference value is unsupported by any measured post-balancing voltage spreads (e.g., max/min deviation in mV), statistics across the 36 modules, or explicit reference value; without these metrics the accuracy and constraint assertions cannot be evaluated.

    Authors: We agree that the Results section requires quantitative support for the balancing claim. The platform's balancing circuitry is designed to constrain cell voltages to a hardware-defined reference (set via the balancing resistors and control logic). To address this, we will add measured pre- and post-balancing voltage data from all 36 modules, including max/min deviations in mV, mean spreads, and statistics. This will be incorporated into a revised Results section with new figures or tables. revision: yes

  2. Referee: [Experimental validation] Safety verification: the assertion of safe, non-invasive access without compromising module integrity lacks supporting data such as isolation resistance measurements, thermal profiles during balancing, or fault-injection test outcomes.

    Authors: The design incorporates galvanic isolation and automotive-grade components to ensure non-invasive access. However, we acknowledge the lack of explicit verification data. We will add isolation resistance measurements (>1 MΩ) and thermal profiles from balancing tests to the Experimental validation section. Full fault-injection testing was outside the current scope but will be noted as a limitation with discussion of planned follow-up. revision: partial

  3. Referee: [Discussion] SoH assessment: the claim of enabling accurate cell-level state-of-health evaluations is not accompanied by any error metrics, comparison to reference methods, or validation against known cell conditions.

    Authors: The manuscript focuses on the hardware platform for cell access rather than full SoH algorithm validation. We will revise the Discussion to clarify that voltage access enables SoH assessment and include sensor accuracy specs (e.g., <5 mV error) as a basis for potential accuracy. Direct comparisons to reference methods and validation against known conditions will be noted as future work, with the current contribution limited to non-invasive signal access. revision: partial

Circularity Check

0 steps flagged

No circularity: hardware design paper with no derivations or fitted parameters

full rationale

The paper is a pure hardware design and implementation description for a battery module testing platform. It contains no mathematical equations, no parameter fitting, no predictions derived from models, and no derivation chain that could reduce to its inputs. Central claims rest on the physical integration of voltage sensors, balancing circuitry, and a microcontroller, with testing performed across 36 modules. No self-citations are invoked to justify any uniqueness theorem or ansatz. The work is self-contained against external benchmarks as an engineering artifact, with any performance claims (e.g., imbalance constraint) depending on unshown experimental data rather than circular logic. This matches the default expectation for non-circular papers.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No mathematical models, free parameters, or invented physical entities are introduced; the work is a practical hardware integration relying on standard engineering components.

pith-pipeline@v0.9.0 · 5481 in / 1029 out tokens · 40198 ms · 2026-05-14T00:24:04.268313+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

39 extracted references · 39 canonical work pages

  1. [1]

    Trends and 2025 insights on the rise of electric vehicles in the usa,

    M. Muratori, D. Arent, M. D. Bazilian, J. Bistline, B. Borlaug, A. Brown, P. Cazzola, E. M. Dede, C. Gearhart, D. Greeneet al., “Trends and 2025 insights on the rise of electric vehicles in the usa,” Nature Reviews Clean Technology, pp. 1–19, 2025

    work page 2025

  2. [2]

    Global ev outlook 2022: Securing supplies for an electric future,

    E. M. Bibra, E. Connelly, S. Dhir, M. Drtil, P. Henriot, I. Hwang, J.-B. Le Marois, S. McBain, L. Paoli, and J. Teter, “Global ev outlook 2022: Securing supplies for an electric future,” 2022. [Online]. Available: https://www.iea.org/reports/global-ev-outlook-2022

    work page 2022

  3. [3]

    Deng and K

    H. Deng and K. E. Aifantis,Applications of Lithium Batteries, ch. 4, pp. 83–103. John Wiley and Sons, Ltd, 2023

    work page 2023

  4. [4]

    A. A. Pesaran, A. Vlahinos, S. D. Burchet al.,Thermal performance of EV and HEV battery modules and packs. National Renewable Energy Laboratory Golden, CO, USA, 1997

    work page 1997

  5. [5]

    Mastering battery testing: Everything you need to know,

    Averna, “Mastering battery testing: Everything you need to know,”

    work page

  6. [6]

    Available: https://www.averna.com/mastering-battery- testing-everything-you-need-to-know 1Differences below1 ◦C are within the±1 ◦C thermocouple accuracy [38]

    [Online]. Available: https://www.averna.com/mastering-battery- testing-everything-you-need-to-know 1Differences below1 ◦C are within the±1 ◦C thermocouple accuracy [38]. -200 -100 0 100 200 Current [A] Im 25 30 35 40 45 Temperature [ ℃ ] Ttc1 Ttc2 Ttc3 environment Time [h] 0 2 4 6 8 10 12 14 160 0.5 1 1.5 Temperature [ ℃ ] "Tmax FIGURE 14. Top: module cur...

    work page

  7. [7]

    Challenges and opportunities for second-life batteries: Key technologies and economy,

    X. Gu, H. Bai, X. Cui, J. Zhu, W. Zhuang, Z. Li, X. Hu, and Z. Song, “Challenges and opportunities for second-life batteries: Key technologies and economy,”Renewable and Sustainable Energy Reviews, vol. 192, p. 114191, 2024

    work page 2024

  8. [8]

    Second-life lithium-ion battery aging dataset based on grid storage cycling,

    K. Moy, M. A. Khan, S. Fasolato, G. Pozzato, A. Allam, and S. Onori, “Second-life lithium-ion battery aging dataset based on grid storage cycling,”Data in Brief, vol. 57, p. 111046, 2024

    work page 2024

  9. [9]

    Fair market valuation of electric vehicle batteries in second life applications,

    A. Bach, S. Reichelstein, S. Onori, and J. Zhuang, “Fair market valuation of electric vehicle batteries in second life applications,” Available at SSRN 4940320, 2024

    work page 2024

  10. [10]

    Technoeconomic decision support for second-life batteries,

    J. Zhuang, A. Bach, B. H. van Vlijmen, S. J. Reichelstein, W. Chueh, S. Onori, and S. M. Benson, “Technoeconomic decision support for second-life batteries,”Applied Energy, vol. 390, p. 125800, 2025

    work page 2025

  11. [11]

    Mea- suring test bench with adjustable thermal connection of cells to their neighbors and a new model approach for parallel-connected cells,

    A. Fill, T. Mader, T. Schmidt, R. Llorente, and K. P. Birke, “Mea- suring test bench with adjustable thermal connection of cells to their neighbors and a new model approach for parallel-connected cells,” Batteries, vol. 6, no. 1, 2020

    work page 2020

  12. [12]

    Degradation in parallel- connected lithium-ion battery packs under thermal gradients,

    M. Naylor Marlow, J. Chen, and B. Wu, “Degradation in parallel- connected lithium-ion battery packs under thermal gradients,”Com- munications Engineering, vol. 3, no. 1, p. 2, 2024

    work page 2024

  13. [13]

    Effect of parallel connection topology on air-cooled lithium-ion battery mod- ule: Inconsistency analysis and comprehensive evaluation,

    C. Li, N. Cui, L. Chang, Z. Cui, H. Yuan, and C. Zhang, “Effect of parallel connection topology on air-cooled lithium-ion battery mod- ule: Inconsistency analysis and comprehensive evaluation,”Applied Energy, vol. 313, p. 118758, 2022

    work page 2022

  14. [14]

    Experimental analysis of thermal runaway and propagation in lithium-ion battery modules,

    C. F. Lopez, J. A. Jeevarajan, and P. P. Mukherjee, “Experimental analysis of thermal runaway and propagation in lithium-ion battery modules,”Journal of the electrochemical society, vol. 162, no. 9, p. A1905, Jul. 2015

    work page 2015

  15. [15]

    Management of imbalances in parallel- connected lithium-ion battery packs,

    W. Diao, M. Pecht, and T. Liu, “Management of imbalances in parallel- connected lithium-ion battery packs,”Journal of Energy Storage, vol. 24, p. 100781, 2019

    work page 2019

  16. [16]

    A novel measurement technique for parallel-connected lithium-ion cells with controllable interconnection resistance,

    P. Jocher, M. Steinhardt, S. Ludwig, M. Schindler, J. Martin, and A. Jossen, “A novel measurement technique for parallel-connected lithium-ion cells with controllable interconnection resistance,”Journal of Power Sources, vol. 503, p. 230030, 2021

    work page 2021

  17. [17]

    Parallel-connected battery module modeling based on physical characteristics in multiple do- mains and heterogeneous characteristic analysis,

    Y . Tian, Z. Huang, X. Li, and J. Tian, “Parallel-connected battery module modeling based on physical characteristics in multiple do- mains and heterogeneous characteristic analysis,”Energy, vol. 239, p. 122181, 2022

    work page 2022

  18. [18]

    Influence of circuit design on load distribution and performance of parallel-connected lithium 13 : ion cells for photovoltaic home storage systems,

    T. Gr ¨un, K. Stella, and O. Wollersheim, “Influence of circuit design on load distribution and performance of parallel-connected lithium 13 : ion cells for photovoltaic home storage systems,”Journal of Energy Storage, vol. 17, pp. 367–382, 2018

    work page 2018

  19. [19]

    Current imbalance in dissimilar parallel-connected batteries and the fate of degradation convergence,

    A. Weng, H. Movahedi, C. Wong, J. B. Siegel, and A. Stefanopoulou, “Current imbalance in dissimilar parallel-connected batteries and the fate of degradation convergence,”Journal of Dynamic Systems, Mea- surement, and Control, vol. 146, no. 1, p. 011106, 2024

    work page 2024

  20. [20]

    Piombo, S

    G. Piombo, S. Fasolato, R. Heymer, M. Hidalgo, M. Faraji Niri, S. Onori, and J. Marco, “Unveiling the performance impact of module level features on parallel-connected lithium-ion cells via explainable machine learning techniques on a full factorial design of experiments,” Journal of Energy Storage, vol. 84, p. 110783, 2024

    work page 2024

  21. [21]

    Full factorial design of ex- periments dataset for parallel-connected lithium-ion cells imbalanced performance investigation,

    G. Piombo, S. Fasolato, R. Heymer, M. F. Hidalgo, M. Faraji Niri, D. M. Raimondo, J. Marco, and S. Onori, “Full factorial design of ex- periments dataset for parallel-connected lithium-ion cells imbalanced performance investigation,”Data in Brief, vol. 53, p. 110227, 2024

    work page 2024

  22. [22]

    Differential voltage analysis and patterns in parallel- connected pairs of imbalanced cells,

    C. Wong, A. Weng, S. Pannala, J. Choi, J. B. Siegel, and A. Ste- fanopoulou, “Differential voltage analysis and patterns in parallel- connected pairs of imbalanced cells,” in2024 American Control Conference (ACC), pp. 3492–3497. IEEE, 2024

    work page 2024

  23. [23]

    Characterization of aging propagation in lithium-ion cells based on an electrochemical model,

    A. Allam and S. Onori, “Characterization of aging propagation in lithium-ion cells based on an electrochemical model,” in2016 Amer- ican Control Conference (ACC), pp. 3113–3118. IEEE, 2016

    work page 2016

  24. [24]

    Impact of module configuration on lithium-ion battery performance and degradation: Part i. energy throughput, voltage spread, and current distribution,

    Y . Preger, J. Mueller, A. Fresquez, S. Allu, and C. Rich, “Impact of module configuration on lithium-ion battery performance and degradation: Part i. energy throughput, voltage spread, and current distribution,”Journal of The Electrochemical Society, vol. 172, no. 5, p. 050540, 2025

    work page 2025

  25. [25]

    Module design and fault diagnosis in electric vehicle batteries,

    G. J. Offer, V . Yufit, D. A. Howey, B. Wu, and N. P. Brandon, “Module design and fault diagnosis in electric vehicle batteries,”Journal of Power Sources, vol. 206, pp. 383–392, 2012

    work page 2012

  26. [26]

    Fast screening of lithium-ion batteries for second use with pack-level testing and machine learning,

    S. Yang, C. Zhang, J. Jiang, W. Zhang, H. Chen, Y . Jiang, D. U. Sauer, and W. Li, “Fast screening of lithium-ion batteries for second use with pack-level testing and machine learning,”eTransportation, vol. 17, p. 100255, Jul. 2023

    work page 2023

  27. [27]

    Quantifying the state of the art of electric powertrains in battery electric vehicles: Range, efficiency, and lifetime from component to system level of the volkswagen id.3,

    N. Wassiliadis, M. Steinstr ¨ater, M. Schreiber, P. Rosner, L. Nico- letti, F. Schmid, M. Ank, O. Teichert, L. Wildfeuer, J. Schneider, A. Koch, A. K ¨onig, A. Glatz, J. Gandlgruber, T. Kr ¨oger, X. Lin, and M. Lienkamp, “Quantifying the state of the art of electric powertrains in battery electric vehicles: Range, efficiency, and lifetime from component t...

    work page 2022

  28. [28]

    Parameter variations within li-ion battery packs: Theoretical investigations and experimental quantification,

    M. Baumann, L. Wildfeuer, S. Rohr, and M. Lienkamp, “Parameter variations within li-ion battery packs: Theoretical investigations and experimental quantification,”Journal of Energy Storage, vol. 18, pp. 295–307, 2018

    work page 2018

  29. [29]

    On- line state of health estimation for series-connected LiFePO 4 battery pack based on differential voltage and inconsistency analysis,

    Z. Zhou, B. Duan, Y . Kang, Q. Zhang, Y . Shang, and C. Zhang, “On- line state of health estimation for series-connected LiFePO 4 battery pack based on differential voltage and inconsistency analysis,”IEEE Transactions on Transportation Electrification, vol. 10, no. 1, pp. 989– 998, 2024

    work page 2024

  30. [30]

    A rapid capacity evaluation of retired electric vehicle battery modules using partial discharge test,

    M. Ahmeid, M. Muhammad, S. Lambert, P. S. Attidekou, and Z. Milo- jevic, “A rapid capacity evaluation of retired electric vehicle battery modules using partial discharge test,”Journal of Energy Storage, vol. 50, Jun. 2022

    work page 2022

  31. [31]

    Evaluation of the second-life potential of the first-generation nissan leaf battery packs in energy storage systems,

    W. Gao, Z. Cao, N. V . Kurdkandi, Y . Fu, and C. Mi, “Evaluation of the second-life potential of the first-generation nissan leaf battery packs in energy storage systems,”ETransportation, vol. 20, p. 100313, 2024

    work page 2024

  32. [32]

    Taking second-life batteries from exhausted to empowered using ex- periments, data analysis, and health estimation,

    X. Cui, M. A. Khan, G. Pozzato, S. Singh, R. Sharma, and S. Onori, “Taking second-life batteries from exhausted to empowered using ex- periments, data analysis, and health estimation,”Cell Reports Physical Science, vol. 5, no. 5, 2024

    work page 2024

  33. [33]

    Experimental data of lithium-ion batteries under galvanostatic discharge tests at different rates and temperatures of operation,

    E. Catenaro and S. Onori, “Experimental data of lithium-ion batteries under galvanostatic discharge tests at different rates and temperatures of operation,”Data in Brief, vol. 35, p. 106894, 2021

    work page 2021

  34. [34]

    Research on digital calibration of voltage mea- surement for automotive battery management systems,

    H. Niu and B. Luo, “Research on digital calibration of voltage mea- surement for automotive battery management systems,” in2024 13th International Conference on Communications, Circuits and Systems (ICCCAS), pp. 122–126. IEEE, 2024

    work page 2024

  35. [35]

    Battery management system for electric vehicles: com- prehensive review of circuitry configuration and algorithms,

    A. Kurkin, A. Chivenkov, D. Aleshin, I. Trofimov, A. Shalukho, and D. Vilkov, “Battery management system for electric vehicles: com- prehensive review of circuitry configuration and algorithms,”World Electric Vehicle Journal, vol. 16, no. 8, p. 451, 2025

    work page 2025

  36. [36]

    Comparison of passive cell balancing and active cell balancing for automotive batteries,

    W. C. Lee, D. Drury, and P. Mellor, “Comparison of passive cell balancing and active cell balancing for automotive batteries,” in2011 IEEE Vehicle Power and Propulsion Conference, pp. 1–7. IEEE, 2011

    work page 2011

  37. [37]

    Extending life of lithium-ion battery systems by embracing heterogeneities via an optimal control- based active balancing strategy,

    V . Azimi, A. Allam, and S. Onori, “Extending life of lithium-ion battery systems by embracing heterogeneities via an optimal control- based active balancing strategy,”IEEE Transactions on Control Sys- tems Technology, vol. 31, no. 3, pp. 1235–1249, 2022

    work page 2022

  38. [38]

    Aging effect on the variation of li-ion battery resistance as function of temperature and state of charge,

    S. Barcellona, S. Colnago, G. Dotelli, S. Latorrata, and L. Piegari, “Aging effect on the variation of li-ion battery resistance as function of temperature and state of charge,”Journal of Energy Storage, vol. 50, p. 104658, 2022

    work page 2022

  39. [39]

    Arbin instruments, auxiliary temperature measurement,

    “Arbin instruments, auxiliary temperature measurement,” https://www.arbin.com/auxiliary-temperature-measurement.html. GABRIELE MARINIwas born in Penne, Italy. He received the Ph.D. in Information Technology from Politecnico di Milano, Italy, in 2024. Since November 2024, he is a postdoctoral researcher at Stanford university, Department of Energy Science ...

    work page 2024