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GP-EE-1QAG5M1ElectricalReady

Second-Life EV Battery Suitability for Stationary Energy Storage

A completed electrical engineering study comparing six retired EV battery cohorts across four stationary energy-storage applications using diagnostic, safety, traceability, performance, economic and uncertainty evidence.

Second-Life EV Battery Suitability for Stationary Energy Storage project visual
GP-EE-1QAG5M1 · Electrical
  • Python
  • NumPy
  • Pandas
  • Matplotlib
  • Jupyter

Software compatibility

Python 3.11 or later

The retained release and clean Docker run use Python 3.12. No paid engineering software or external dataset is required.

Project definition

Problem statement

An EV battery that has reached the end of its vehicle life can retain useful capacity, but remaining energy alone does not establish suitability for a stationary system.

The engineering problem is to screen diagnostics, safety, traceability, power capability, application duty, economics and lifecycle evidence before selecting a reuse pathway.

Project objectives

  • Define six representative LFP, NMC, mixed and unknown-provenance battery cohorts.
  • Compare backup power, solar shifting, commercial peak shaving and EV charging support applications.
  • Separate four mandatory qualification gates from compensable suitability criteria.
  • Calculate deterministic rankings and application-specific shortlists.
  • Test ranking stability with 25,000 fixed-seed uncertainty samples per application.
  • Identify testing, safety, integration and compliance work required before deployment.

Project structure

Project components

01

Evidence review

Screens 58 official, standards and peer-reviewed sources and records how each source supports the study.

02

Battery cohorts

Defines retained state of health, diagnostic confidence, thermal safety, traceability, power capability and repurposing readiness.

03

Application requirements

Sets the distinct electrical and operational priorities of four stationary energy-storage duties.

04

Hard-gate screening

Rejects cohorts that do not meet diagnostic-confidence, thermal-safety, traceability or standards-readiness requirements.

05

Decision and uncertainty model

Ranks eligible cohorts and tests how score and preference uncertainty change first-rank share and score intervals.

06

Engineering handoff

Provides economic screening, qualification plans, limitations and further-work requirements.

Methodology

Project workflow

  1. 01
    Review the evidence

    Read the source matrix, diagnostic limits and standards boundaries before interpreting a score.

  2. 02
    Select an application

    Choose the stationary duty and inspect its energy, power, cycling, safety and availability priorities.

  3. 03
    Apply mandatory gates

    Remove cohorts that lack the minimum evidence required for responsible reuse screening.

  4. 04
    Calculate the ranking

    Apply the declared application weights to the eligible cohort scores.

  5. 05
    Test uncertainty

    Sample plausible score and preference changes and compare first-rank share and score intervals.

  6. 06
    Plan qualification

    Translate the shortlist into battery tests, integration studies, protection checks and current compliance review.

Demonstration scenario

The LFP85 cohort leads all four central application screens because its retained condition, diagnostic evidence, safety profile and cycle capability remain strong. NMC85 is close for EV charging support. Mixed and unknown-provenance cohorts fail mandatory evidence gates and are not ranked for deployment.

Engineering

Tools and method

Tools
The project uses Python, NumPy, Pandas, Matplotlib, Jupyter for subject analysis, simulation, and results.
Evidence model
A structured reference catalogue connects battery ageing, diagnostics, safety, regulation, reuse performance and lifecycle evidence.
Gate model
Four mandatory conditions prevent a strong compensable score from hiding a critical evidence failure.
Decision analysis
A transparent weighted model compares eligible cohorts against application-specific requirements.
Uncertainty
Bounded score sampling and Dirichlet weight sampling expose stable and fragile preferences.
Reproducibility
Fixed configuration, seed, tables, figures, tests and a clean Linux container reproduce the retained study.

Testing

Evaluation

Evaluation measures

  • Mandatory-gate result for every battery cohort
  • Complete ranking of eligible cohorts in four stationary applications
  • First-rank share from 25,000 samples per application
  • Fifth, fiftieth and ninety-fifth percentile suitability scores
  • Accepted battery cost and illustrative lifecycle-cost comparison
  • Application-specific shortlist and qualification requirements
  • Exact reproducibility of the retained numerical evidence

Project boundaries

  • The six cohorts are declared teaching cases and not measurements from a physical battery lot.
  • The one-to-five scores are analyst-coded literature synthesis values and not certified test results.
  • The economic values are illustrative assumptions and not a supplier quotation or investment case.
  • Passing this screen does not establish electrical, thermal, structural, fire or regulatory compliance.
  • The evidence cutoff is 29 August 2026 and current standards and rules must be checked before real use.
  • Deployment requires lot-specific diagnostics, insulation and protection tests, thermal assessment, integration design and qualified engineering review.

Included

  1. 01Complete Python analysis package
  2. 02Six second-life battery cohorts and four stationary applications
  3. 03Ten-criterion suitability model with four mandatory hard gates
  4. 04Twenty-five thousand Monte Carlo samples for each application
  5. 05Five result tables, one summary and ten labelled figures
  6. 06114-page project documentation in PDF and editable Word formats
  7. 0719-page setup and usage guide in PDF and editable Word formats
  8. 08Fifty-eight screened and annotated references with a source matrix
  9. 09Complete project files, calculations and evidence in a private GitHub repository

Project record

No information is collected on this page.

Permanent project ID
GP-EE-1QAG5M1
Catalogued
21 Aug 2026
Completed
29 Aug 2026
Verified
29 Aug 2026
Demonstration
Included in repository

Handover

After purchase

  1. 01
    Payment is confirmed

    The project is marked unavailable and cannot be purchased again.

  2. 02
    Repository access is granted

    The buyer's submitted GitHub account receives access to the private repository.

  3. 03
    The purchase record is delivered

    The certification sheet is prepared from the reviewed buyer details and sent privately by email.