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GP-EE-04LIAZAElectricalOpen for request

EV charging schedule optimiser

A constrained scheduling system for assigning EV charging power while respecting vehicle deadlines, charger capacity, transformer limits, and time-varying electricity costs.

  • Nuxt
  • Vue
  • FastAPI
  • PostgreSQL
  • Python
  • Pyomo
  • HiGHS
  • Docker

Project definition

Problem statement

Uncoordinated EV charging can create a new site peak, exceed transformer or connection limits, and still fail to deliver required energy before vehicles depart.

The engineering problem is to schedule discrete chargers and time-varying power while balancing energy deadlines, electrical limits, tariffs, peak demand, and fair treatment of competing vehicles.

Project objectives

  • Model vehicle arrival, departure, energy request, charging rate, and charger compatibility.
  • Respect charger availability, site power, transformer, and optional phase constraints.
  • Minimise selected energy cost, peak demand, missed energy, and schedule disruption.
  • Compare first-come, immediate-charge, and optimised strategies.
  • Test delayed arrivals, early departures, high-demand periods, and uncertain energy requests.

System design

System modules

01

Fleet session manager

Validates vehicle sessions, energy requests, deadlines, priority rules, and charger compatibility.

02

Site model

Defines charger ratings, connection limits, transformer capacity, base load, and tariff periods.

03

Schedule optimiser

Assigns chargers and power by time step under energy and electrical constraints.

04

Baseline simulator

Runs immediate and first-come charging under the same arrivals and capacity.

05

Operations dashboard

Shows allocations, site load, deadlines, cost, unmet energy, fairness, and scenario comparisons.

Methodology

System workflow

  1. 01
    Load site

    The user selects a prepared depot, workplace, or campus charging configuration.

  2. 02
    Import sessions

    Vehicle arrivals, departures, requests, and compatibility are checked against the time horizon.

  3. 03
    Run baseline

    Immediate or first-come charging establishes cost, peak, and missed-energy results.

  4. 04
    Optimise

    The scheduler allocates chargers and power while recording feasibility and active constraints.

  5. 05
    Stress test

    Uncertain arrival, departure, request, tariff, or capacity scenarios are compared.

Demonstration scenario

Twenty prepared vehicle sessions arrive at a workplace with eight chargers and a transformer limit. Immediate charging exceeds the site capacity. The optimiser shifts flexible sessions, respects departure deadlines, and reduces the peak. An early-departure scenario then shows which energy requests become infeasible.

Engineering

Technical architecture

Web application
Nuxt and Vue for site setup, session tables, charging timelines, load charts, and comparison.
Scheduling API
FastAPI for data validation, optimisation jobs, baseline runs, scenario tests, and exports.
Data layer
PostgreSQL for sites, chargers, sessions, tariffs, base loads, schedules, and result metrics.
Optimisation model
Python and Pyomo with a linear or mixed-integer charging and assignment formulation.
Verification
Energy totals, capacity checks, deadline checks, manually solvable cases, and deterministic scenario tests.

Testing

Evaluation

Evaluation measures

  • Vehicle energy delivered before departure
  • Site, transformer, charger, and assignment constraint satisfaction
  • Peak-load and energy-cost reduction against baselines
  • Unmet-energy distribution and selected fairness measures
  • Robustness to arrival, departure, request, and base-load uncertainty
  • Solver time, solution gap, and infeasibility rate across fleet sizes

System boundaries

  • The project creates offline schedules and does not communicate with live chargers or vehicles.
  • Battery behaviour, charger losses, and arrival patterns use documented simplified models.
  • Site protection, wiring design, and utility approval are outside the scope.
  • A real deployment requires certified equipment and review by qualified electrical professionals.

Included

  1. 01Vehicle, charger, and site-constraint interface
  2. 02Charging scheduler and baseline strategy engine
  3. 03Load, cost, deadline, and fairness comparison dashboard
  4. 04Prepared fleet sessions, tariffs, and capacity scenarios
  5. 05Complete source code in a private GitHub repository
  6. 06Complete project documentation with synopsis, abstract, methodology, charging-system diagrams, optimisation results, screenshots, and conclusion
  7. 07Setup and usage guide

Project record

No buyer information is collected on this page.

Permanent project ID
GP-EE-04LIAZA
Catalogued
21 Aug 2026
Completed
Pending
Verified
Pending
Demonstration
Added when ready

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.