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Record W4406071959 · doi:10.1016/j.geits.2025.100262

Enhancing security in the ISO 15118–20 EV charging system

2025· article· en· W4406071959 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGreen Energy and Intelligent Transportation · 2025
Typearticle
Languageen
FieldEngineering
TopicSafety Systems Engineering in Autonomy
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer securityBusinessSecurity systemComputer science

Abstract

fetched live from OpenAlex

Electric Vehicle (EV) ‘DC Fast Charging’ systems directly connect an EV’s battery to an external charger. A compromised EV charger may damage the EV or be used as part of a demand-side power grid attack. We show that the newest charging standard ISO 15118-20 is not sufficient to prevent charging attacks, as it provides no mechanism to verify charger integrity. We present system and threat models for the attack, before defining an extension to ISO 15118-20 that adds support for firmware integrity verification through remote attestation, while remaining interoperable with non-supporting devices. A proof of concept implementation demonstrates the security improvement by protecting against the specified attack while requiring only 85 bytes of secure storage, 8kB of working memory, and adding less than 0.5 seconds to the length of a charging session. Backwards compatibility with an implementation of the original standard is also demonstrated. • A system and threat model is developed for ISO 15118-20 EV charging • ISO 15118-20 is shown to be insufficient to protect against existing attacks • We develop a remote attestation protocol within ISO 15118-20 to improve security • An experimental platform tests interoperability and evaluates performance overheads

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.700
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.191
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it