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arxiv: 2606.29994 · v1 · pith:RFCZOV6Lnew · submitted 2026-06-29 · 📡 eess.SY · cs.SY

Quantifying Realizable Flexibility Limits in Fast and Ultra-Fast EV Charging Using Real-World Data

Cesar Diaz-Londono , Liu Zhang , Jorge De La Cruz , Hamidreza Arasteh , Anand R. , Daogui Tang , Josep M. Guerrero This is my paper

Pith reviewed 2026-06-30 05:33 UTC · model grok-4.3

classification 📡 eess.SY cs.SY
keywords EV charging flexibilityfast DC chargingbattery management systemdata-driven frameworkpower system operationunidirectional flexibilitybidirectional flexibilityreal-world data
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The pith

EV flexibility in fast and ultra-fast charging is bounded by battery management system limits and connection times rather than being a freely controllable resource.

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

The paper uses 252 real charging sessions to reconstruct 141 Power-SoC profiles and define flexibility as an energy-bounded, time-constrained process. Unidirectional flexibility is the maximum shiftable charging energy, while bidirectional flexibility is the maximum extractable discharge energy under feasibility constraints. This matters because system operators and aggregators require realistic bounds for scheduling, peak shaving, and short-duration services instead of simplified power-controllable models. The results show that flexibility varies with state of charge and connection time, with charging beyond 80 percent SoC adding duration but limited energy gains and higher charger power saturating due to BMS limits.

Core claim

Based on 252 real charging sessions, 141 representative Power-SoC profiles are reconstructed to capture real-world charging dynamics. Unidirectional flexibility is defined through bounds on the maximum shiftable charging energy, while bidirectional flexibility is quantified as the bounds of the maximum extractable discharge energy under feasibility constraints. Results show that flexibility depends on charging state and connection time, with charging beyond 80 percent SoC increasing duration with limited gains, higher charger power saturating due to BMS limits, and the maximum extractable bidirectional energy able to exceed twice its value depending on activation point.

What carries the argument

Trajectory-aware data-driven framework that reconstructs Power-SoC profiles from real sessions to set bounds on shiftable charging energy and extractable discharge energy.

If this is right

  • Flexibility depends on charging state and connection time.
  • Charging beyond 80 percent SoC increases duration with limited gains.
  • Higher charger power saturates due to BMS limits.
  • Charging time in the 20-80 percent range drops by over 60 percent and mean power increases by up to 40 percent.
  • The maximum extractable bidirectional energy can exceed twice its value depending on the activation point.

Where Pith is reading between the lines

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

  • Aggregators could use the reported bounds to set conservative bids in flexibility markets rather than assuming full controllability.
  • The framework could be extended to test how multiple EVs interact when their individual bounds are aggregated for system services.
  • Real-time grid simulations could apply these limits to measure the gap between optimistic and realizable flexibility procurement.

Load-bearing premise

The 252 real charging sessions and 141 reconstructed Power-SoC profiles are representative of real-world BMS behavior, connection time availability, and battery-protection limits across fast and ultra-fast DC chargers.

What would settle it

A new dataset of charging sessions whose reconstructed profiles show flexibility bounds independent of state of charge or connection time, or that permit substantially more bidirectional energy extraction than the reported limits.

Figures

Figures reproduced from arXiv: 2606.29994 by Anand R., Cesar Diaz-Londono, Daogui Tang, Hamidreza Arasteh, Jorge De La Cruz, Josep M. Guerrero, Liu Zhang.

Figure 1
Figure 1. Figure 1: Simplified EV–EVCS communication sequence for charging (ISO 15118). [PITH_FULL_IMAGE:figures/full_fig_p009_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: EVs with IDs 1–70: battery capacity (green) and maximum absorbed DC charg [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: EVs with IDs 71–141: continuation of the catalogue of distinct charging be [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Measured Power–SoC charging curves for the three EV categories used in this [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Charging power (top row) and SoC evolution (bottom row) for the three EV [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Synthetic discharge Power–SoC curves for the three EV groups. [PITH_FULL_IMAGE:figures/full_fig_p019_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Synthetic discharge trajectories in the time domain for the three EV groups. [PITH_FULL_IMAGE:figures/full_fig_p019_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Framework for quantifying flexibility in fast and ultra-fast DC charging under [PITH_FULL_IMAGE:figures/full_fig_p021_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Conceptual illustration of the sliding-window method used to quantify unidirec [PITH_FULL_IMAGE:figures/full_fig_p024_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Conceptual illustration of bidirectional flexibility for a representative fast [PITH_FULL_IMAGE:figures/full_fig_p028_10.png] view at source ↗
Figure 4
Figure 4. Figure 4: The charging process is simulated in discrete time with sampling inter￾val ts . In general, ts could be chosen according to the temporal resolution of the available data; in this study, it is fixed to ts = 1 min. For each pair (m, j)—charger rating P CS m and EV j—the SoC trajectory starts from SoCj (0) = 1%. At each time step k, the instantaneous charging power is obtained by limiting the EVs BMS-requeste… view at source ↗
Figure 11
Figure 11. Figure 11: Charging-time comparison across charger ratings and EV groups. For each [PITH_FULL_IMAGE:figures/full_fig_p035_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Example charging power profiles for one representative EV from each group, [PITH_FULL_IMAGE:figures/full_fig_p035_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Mean charging power across charger ratings and EV groups. The top panel [PITH_FULL_IMAGE:figures/full_fig_p036_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Time-domain characterization of EV discharge capability across groups. (a) [PITH_FULL_IMAGE:figures/full_fig_p038_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Unidirectional flexibility assessment across the fleet. (a) Distribution of charg [PITH_FULL_IMAGE:figures/full_fig_p040_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Bidirectional flexibility maps for two representative EVs (fast-charging and [PITH_FULL_IMAGE:figures/full_fig_p041_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Time-domain trajectories associated with the upper-bound bidirectional ma [PITH_FULL_IMAGE:figures/full_fig_p042_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Discharge-energy envelopes for two representative EVs as a function of idle [PITH_FULL_IMAGE:figures/full_fig_p042_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Fleet-level characterization of bidirectional flexibility as a function of idle time. [PITH_FULL_IMAGE:figures/full_fig_p043_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Across-EV statistics of the upper bound of the maximum extractable discharge [PITH_FULL_IMAGE:figures/full_fig_p044_20.png] view at source ↗
read the original abstract

The rapid growth of electric vehicles (EVs) is increasing the need to accurately quantify their flexibility as a resource for power system operation. However, most existing approaches rely on simplified or power-controllable models that overlook the intrinsic constraints of fast and ultra-fast DC charging. In practice, flexibility is fundamentally shaped by battery management system (BMS) behavior, connection time availability, and battery-protection limits. This paper introduces a trajectory-aware data-driven framework to quantify EV charging flexibility as an energy-bounded and time-constrained process. Based on 252 real charging sessions, 141 representative Power-SoC profiles are reconstructed to capture real-world charging dynamics. Unidirectional flexibility is defined through bounds on the maximum shiftable charging energy, while bidirectional flexibility is quantified as the bounds of the maximum extractable discharge energy under feasibility constraints. Results show that flexibility depends on charging state and connection time. Charging beyond 80% SoC increases duration with limited gains, while higher charger power saturates due to BMS limits. Charging time in the 20%-80% range drops by over 60%, and mean power increases by up to 40%. The maximum extractable bidirectional energy can exceed twice its value depending on the point at which flexibility is activated. These results highlight that EV flexibility is not a controllable resource, but a bounded and time-dependent capability. As such, the proposed framework provides actionable limits that can be directly used by system operators and aggregators for scheduling, peak shaving, and short-duration flexibility services.

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 / 1 minor

Summary. The paper introduces a trajectory-aware data-driven framework for quantifying EV charging flexibility in fast and ultra-fast DC charging. Using 252 real charging sessions, it reconstructs 141 Power-SoC profiles to define unidirectional flexibility via bounds on maximum shiftable charging energy and bidirectional flexibility via bounds on maximum extractable discharge energy. The central claim is that flexibility is a bounded, time-dependent capability shaped by BMS behavior, connection time, and protection limits rather than a controllable resource, yielding actionable limits for system operators in scheduling, peak shaving, and short-duration services.

Significance. If the data processing and representativeness hold, the work provides a concrete alternative to simplified power-controllable EV models by grounding flexibility bounds in observed trajectories. The emphasis on real-world constraints from BMS and charger saturation is a useful contribution for power-system applications. However, the absence of disclosed reconstruction methods, validation, and statistical measures on the derived bounds limits the immediate transferability of the results.

major comments (3)
  1. [Abstract] Abstract: the reconstruction of 141 Power-SoC profiles from 252 sessions is presented without any description of the method, exclusion criteria, error metrics, or validation against independent data. Because the central claim that flexibility is bounded rather than controllable rests directly on these profiles being representative of BMS behavior and protection limits, the lack of these details is load-bearing.
  2. [Abstract] Abstract (results paragraph): the reported quantitative findings (e.g., charging time in 20%-80% SoC drops by over 60%, mean power increases by up to 40%, extractable bidirectional energy can exceed twice its value) are given without error bars, confidence intervals, or cross-validation against a held-out set of sessions. This undermines the generalizability asserted for system-operator use.
  3. [Framework definition (implied in abstract)] The framework is defined directly from the measured sessions with no equations shown that reduce the reported bounds to independent parameters or external validation; the mapping from observed trajectories to actionable limits therefore remains internal to the sample.
minor comments (1)
  1. [Abstract] The abstract states results from 252 sessions and 141 profiles but supplies no table or figure reference for the underlying distributions or charger-type breakdown.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting the need for greater transparency in the abstract. We address each major comment below and indicate where revisions will be made to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the reconstruction of 141 Power-SoC profiles from 252 sessions is presented without any description of the method, exclusion criteria, error metrics, or validation against independent data. Because the central claim that flexibility is bounded rather than controllable rests directly on these profiles being representative of BMS behavior and protection limits, the lack of these details is load-bearing.

    Authors: The abstract is a concise summary; the full manuscript (Section II) details the session filtering process, exclusion criteria for incomplete or anomalous trajectories, and internal consistency validation against raw charger data. To address the concern directly in the abstract, we will add a brief clause describing the reconstruction approach and directing readers to the methods for full details on representativeness. revision: yes

  2. Referee: [Abstract] Abstract (results paragraph): the reported quantitative findings (e.g., charging time in 20%-80% SoC drops by over 60%, mean power increases by up to 40%, extractable bidirectional energy can exceed twice its value) are given without error bars, confidence intervals, or cross-validation against a held-out set of sessions. This undermines the generalizability asserted for system-operator use.

    Authors: These metrics are means computed across the 141 profiles. We agree that variability measures would improve reporting. In revision we will append standard deviations or interquartile ranges to the key percentages in the abstract and main results; a formal held-out cross-validation was not performed given the modest sample, but internal robustness checks across subsets were used. revision: partial

  3. Referee: [Framework definition (implied in abstract)] The framework is defined directly from the measured sessions with no equations shown that reduce the reported bounds to independent parameters or external validation; the mapping from observed trajectories to actionable limits therefore remains internal to the sample.

    Authors: The framework is intentionally empirical to reflect observed BMS and protection constraints that resist simple parametric reduction. Equations (3)–(6) in Section III explicitly define the state- and time-dependent bounds on shiftable and extractable energy directly from the trajectories. We will insert a short reference to these definitions in the revised abstract. External validation on independent datasets lies outside the present study scope. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical bounds derived directly from measured sessions without self-referential reduction

full rationale

The paper presents a data-driven framework that reconstructs Power-SoC profiles from 252 real charging sessions and defines unidirectional and bidirectional flexibility bounds directly from those observed trajectories. No equations, fitted parameters, or self-citations are shown that reduce the reported limits to inputs by construction; the central claims remain statistical summaries of the collected data rather than predictions forced by prior definitions or author-specific theorems. The derivation chain is therefore self-contained as an empirical characterization exercise.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Framework rests on the assumption that the collected sessions adequately sample BMS limits and connection times; no free parameters or invented entities are described in the abstract.

axioms (1)
  • domain assumption The 252 charging sessions and 141 reconstructed profiles capture the intrinsic constraints of BMS behavior, connection time availability, and battery-protection limits.
    This premise is required to treat the derived bounds as generalizable actionable limits for system operators.

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discussion (0)

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Reference graph

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