EV Charging Module Guide

EV Charging Infrastructure Fleet & Public TEA

Whether you electrify a fleet or sell public charging, the numbers turn on utilization and demand charges — not nameplate capacity. Here is how to size chargers and build both business cases.

Technology Overview

EV charging economics are dominated by two forces: utilization and demand charges. A charger that sits idle earns nothing; a bank of DC fast chargers that all fire at once can create a demand peak that dwarfs the energy cost. Nameplate power tells you almost nothing about the business case — arrival patterns, dwell times, and queueing do.

There are two fundamentally different business cases. A fleet operator electrifies to avoid gasoline/diesel fuel and maintenance; the chargers are an investment measured against those avoided costs per vehicle class. A public site sells charging sessions; the question is what per-kWh (or per-session) price hits a target margin or payback given realistic utilization.

Demand charges are the sharp edge for fast charging. A single 150 kW session can set a monthly demand charge for the whole site. Dispatch — spreading charging across dwell windows, staging connectors, and optionally adding on-site storage — is what keeps the demand-charge exposure (and the interconnection size) manageable.

The CogenS™ EV Charging Module schedules charging with a daily mixed-integer program against modeled traffic and the real tariff, respecting connector counts, dwell windows, queueing, and grid limits. It branches the financial model into a Fleet (ICE-replacement savings) or Public (session-pricing) case, and reports NPV, IRR, payback, and levelized cost of charging (LCOC) with utilization and session analytics.

Module Specs at a Glance

Charger Types

DC fast chargers and Level 2 AC chargers from major OEMs, with rated power, connector counts, and grid/battery efficiencies.

Charging Model

Fleet duty-cycle schedules or public stochastic arrival profiles set on Site Info, driving realistic occupancy and queueing.

Dispatch

Daily mixed-integer program: charging scheduled across connectors and arrivals within dwell windows and site grid limits.

Business Models

Fleet (avoided fuel + maintenance per vehicle class) or Public (solve the session price for a target margin/payback).

Demand Management

Demand-charge exposure modeled explicitly; optional on-site storage and solar to shave peaks and respect interconnection limits.

Output

TEA report with NPV, IRR, payback, LCOC, utilization tables, session analytics, and multi-vendor comparison.

How to Design a Project

A high-level workflow that mirrors how the CogenS™ platform structures the analysis end-to-end.

  1. Define the charging model

    Set the Charging Model on Site Info: a fleet duty-cycle schedule (arrivals, dwell, energy per vehicle class) or a public stochastic arrival profile. This is the single biggest driver of utilization and the business case.

  2. Choose the charger mix

    Pick DC fast and Level 2 counts and power levels to match dwell times: fast chargers for short-dwell public/transit, Level 2 for long-dwell depots and workplaces. More power is not always better if it drives demand charges.

  3. Pull the tariff and demand charges

    Energy price matters, but the demand-charge structure usually decides the economics of fast charging. Model the real rate, including any EV-specific or demand-subscription tariffs.

  4. Optimize charging within grid limits

    Run the MILP dispatch to schedule charging across dwell windows and connectors, respecting the site interconnection limit. Add on-site storage if the load can't be served within the available grid capacity.

  5. Pick Fleet or Public financials

    For a fleet, model avoided fuel and maintenance per vehicle class against the charger investment. For a public site, solve the session price that meets your margin or payback target at the modeled utilization.

  6. Run the lifecycle TEA and compare vendors

    Compare charger vendors on NPV/IRR/payback/LCOC. Review the utilization and session analytics — min/avg/max session length, energy, wait time, and queue length — to validate the assumptions.

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