EV Fleet Charging Load Balance Planner

Plan EV depot charging around energy delivery, simultaneous kW, and utility demand

EV depot charging is not simply a question of supplying enough kilowatt-hours before the next dispatch. Fleet operators must satisfy three connected limits: enough daily grid energy to replenish vehicles, a controlled simultaneous kW load that the service can support, and a charging schedule that does not create avoidable demand charges. This planner puts those depot constraints in one calculation. Enter the operating assumptions to see whether the selected overnight or between-shift window has room for the modeled fleet under the available charger power and concurrency limit.

An EV depot can have sufficient total energy over an eight-hour charging period yet still face a high site peak if too many vehicles connect at once. The opposite can also occur: ample electrical capacity does not help if the available hours or charger output cannot deliver the required daily energy. By placing per-vehicle grid energy, fleet energy, average load, possible concurrent peak, charger count, and monthly cost estimates together, this planner helps distinguish an energy-short schedule from a peak-management problem.

EV fleet, battery, and depot charger inputs

Number of Fleet Vehicles is the group expected to use this depot during the modeled charging day. The calculation treats those vehicles as having the same representative operating pattern, so use the relevant shift or vehicle group rather than every asset in a larger organization. Average Daily Miles per Vehicle turns route activity into battery-energy demand. It should represent a normal operating day or a deliberately conservative route assumption, not an unusually short day.

Vehicle Efficiency gives the battery energy used per mile. For example, 0.35 kWh per mile corresponds to 35 kWh for 100 miles. Battery Capacity per Vehicle limits the battery energy the model can assign to one vehicle in a charging cycle. Average Arrival State of Charge and Required Departure State of Charge describe the depot's battery readiness rule. A vehicle arriving near 30% and leaving at 90%, for example, needs a substantial refill even if its representative route-energy estimate is lower.

The remaining entries describe the EV charging installation and utility assumptions. Charger Power is the rated kW of one active charger. Charging Window per Day is the number of hours in which the fleet can charge. Maximum Simultaneous Chargers is the site concurrency limit imposed by equipment, transformer capacity, service capacity, or operating rules. Charging System Efficiency converts battery energy to utility-meter energy, accounting for charging losses. Energy Cost per kWh is used for a 30-day energy-cost estimate, while Demand Charge per kW is applied to the calculated concurrent peak load.

How the EV depot planner calculates charging load

The EV charging calculation first finds useful battery energy needed per vehicle. Route energy equals daily miles times vehicle efficiency. State-of-charge energy equals battery capacity times the positive difference between departure and arrival state of charge. The planner takes the larger of these requirements because the modeled vehicle must meet both route use and its departure target. It then caps that battery-energy requirement at battery capacity and divides it by charging efficiency to estimate the grid energy purchased for one vehicle.

For the EV depot totals, grid energy per vehicle is multiplied by vehicle count. Average load distributes that total over the available charging hours. The possible site peak is different: it is the smaller of the fleet size and maximum simultaneous-charger setting, multiplied by charger power. The minimum charger count is rounded up because the calculation describes whole chargers operating at rated output throughout the full window; a result of 3.2 therefore requires four chargers in this simplified model.

For EV depot planning, the most important distinction is between energy throughput and simultaneous demand. Daily miles, battery size, state-of-charge targets, and charging losses determine how much electricity the fleet requires. Charger power and concurrent sessions determine the largest modeled kW draw. Check both sides of the result: a low energy total does not necessarily mean a low demand charge, and a low peak does not prove the available window can finish all charging.

Worked EV depot example with the displayed defaults

Consider the displayed depot assumptions: 60 vehicles travel 85 miles per day at 0.35 kWh per mile and have 120 kWh batteries. They arrive at 30% state of charge and need to depart at 90%. The depot has 150 kW chargers, an eight-hour charging window, and a 20-charger concurrency limit. Charging efficiency is 92%, electricity costs $0.11 per kWh, and the demand charge is $15 per kW. Route energy is 85 × 0.35 = 29.75 kWh per vehicle, while the state-of-charge requirement is 120 × 60% = 72 kWh. The planner uses the larger 72 kWh battery-energy requirement.

At 92% charging efficiency, 72 kWh of battery energy requires about 78.3 kWh from the grid per vehicle. For 60 vehicles, that is about 4,695.7 kWh per day and an average eight-hour load of about 587.0 kW. If 20 of the 150 kW chargers operate together, the modeled concurrent peak is 3,000 kW. The full-power energy calculation requires four chargers over the eight-hour window. The 30-day energy estimate is about $15,495.65, while the demand-charge estimate is $45,000 per month. This difference illustrates why session staggering can matter as much as the fleet's total kWh consumption.

Reading the EV depot charging result

After selecting Plan Charging Load, the EV fleet result summarizes the calculation in operational terms. Per-vehicle grid energy is electricity delivered at the utility meter for one representative vehicle. Total daily energy extends that amount across the fleet. Average load indicates the kW needed if fleet energy were evenly spread over the stated window. Peak load at max concurrency is the modeled kW to compare with service, transformer, and demand-charge exposure. The monthly energy and demand-charge lines keep volume-related and peak-related utility costs separate.

The final feasibility statement tests the charger count required by total daily energy against the simultaneous-charger limit. If the required count is higher than allowed concurrency, the selected combination of charging window, charger rating, and vehicle energy need cannot complete at rated power under this model. Possible responses include extending the depot charging period, reducing route energy, improving vehicle efficiency, changing the departure state-of-charge requirement, increasing charger power, or permitting more concurrent chargers. A feasible result confirms the simplified energy arithmetic, not a minute-by-minute dispatch schedule.

EV fleet growth comparison at fixed depot settings

This EV depot comparison changes only fleet size while retaining the displayed vehicle, charger, and utility assumptions. It shows how added vehicles increase daily grid energy, average load, and the full-power charger requirement. Because the concurrency setting remains at 20 chargers, the possible 3,000 kW peak remains unchanged in these examples; fleet growth first reduces scheduling slack before it necessarily changes the configured maximum site peak.

For this EV depot example, moving from 60 to 72 vehicles increases energy cost roughly with energy consumption but also increases the modeled charger requirement from four to five. That step change matters operationally: it leaves less room for delayed arrivals, additional route miles, or charging losses. Testing fleet size, route distance, and window length one at a time can reveal where a depot will first lose scheduling flexibility.

EV charging model assumptions and useful checks

This EV fleet tool is a fast sizing and comparison model rather than a charger-by-charger dispatch optimizer. It represents the fleet with one average mileage value, efficiency, battery capacity, arrival state of charge, and departure target. It also treats active chargers as able to deliver their rated kW for planning purposes, although real battery charging can taper. The demand-charge estimate uses the calculated concurrent peak and does not model tariff details such as seasonal rules, ratchets, or time-of-use periods. Use the result for early depot sizing and scenario discussion rather than final electrical engineering design.

Several EV charging inputs deserve a deliberate review. When arrival state of charge exceeds the target, the state-of-charge portion is zero, although route energy can still create a charging requirement. If miles times efficiency exceeds battery capacity, the model caps the per-vehicle battery need at battery capacity. That outcome can indicate inconsistent route, battery, or charging-opportunity assumptions. Similarly, a small minimum charger count paired with a large concurrent peak suggests that the depot can meet daily energy with fewer active chargers when sessions are sequenced instead of all started together.

A practical EV depot sensitivity check is to change one assumption at a time. Raising daily miles should raise fleet energy. Shortening the charging window should raise average load. Raising maximum concurrency should raise the modeled peak load, while leaving the energy-based minimum charger count unchanged unless another input changes. These direction checks make it easier to spot an assumption that does not match actual fleet operations.

Using EV charging load results for depot decisions

Use this EV fleet planner to compare charging strategies before committing to a service upgrade or additional hardware. It can show the effect of an extra hour of charging time, a different departure state-of-charge policy, improved vehicle efficiency, or a lower concurrency setting. In many depots, the economical approach is not simply the highest charger power: it is sufficient energy throughput combined with scheduled sessions that limit simultaneous kW. The calculator does not replace managed-charging software or a utility tariff analysis, but it provides a focused first check of whether the fleet can receive its required energy without an unnecessary demand peak.