A data-driven planning guide for parking facilities, fleets, logistics yards, and commercial EV operations
For a parking operator, fleet manager, or charging-infrastructure buyer, the most important question is not simply whether a charging robot can move autonomously or whether it can deliver 100 kW. The real operational question is: how many useful charging jobs can one robot complete in a day, and how much energy can it deliver before additional equipment is required?
That question matters because a mobile charging system does not spend every minute charging. Each job includes dispatch, travel, positioning, connection, charging, disconnection, task switching, and eventually recharging the robot itself. A system that looks powerful on a specification sheet can still underperform if travel distances are long, vehicles request deep charges, or the robot spends too much time waiting or returning to its energy source.
Door Energy develops mobile energy-storage and charging systems for applications such as parking facilities, roadside assistance, fleet support, industrial sites, and emergency power. Its MCP-D autonomous Mobile EV Charger robot is designed for environments with defined parking locations, where charging demand can move between bays instead of being tied permanently to a fixed charger.
This article provides a practical method for estimating daily service capacity. The examples are planning models rather than guaranteed performance figures. Actual results depend on vehicle acceptance power, state of charge, travel distance, route conditions, charger-to-vehicle connection time, robot recharge strategy, site rules, and dispatch efficiency.
Many parking facilities face a structural mismatch. Vehicles occupy hundreds of spaces, but only a portion of them need charging at any given time. Installing a fixed EV Charger at every bay can require substantial electrical distribution work, civil construction, cable routing, protection equipment, and long-term maintenance. At the same time, a fixed charger cannot move when demand shifts from one zone to another.
Door Energy describes this problem as a transition from “vehicles finding chargers” to “charging finding vehicles.” In its article on autonomous charging route planning, the company explains how mobile charging can reduce the mismatch between fixed parking spaces and changing charging demand.
| Customer pain point | What the operator should measure | Why it affects robot capacity |
| Too few charging spaces | Peak number of vehicles requesting energy | Determines queue pressure and required robot count |
| Vehicles occupy chargers after charging | Average dwell time after charge completion | Shows whether mobile charging can release dedicated charging bays |
| High infrastructure cost | Cost of grid upgrade, cabling, trenching, and charger installation | Allows comparison between mobile capacity and fixed expansion |
| Uneven demand by zone | Requests by parking area and time of day | Determines travel distance and dispatch strategy |
| Vehicles leave at different times | Departure deadline for each vehicle | Determines task priority |
| Robot seems busy but output is low | kWh delivered per day and charging utilization | Separates productive charging from travel, waiting, and recharge time |
For this reason, daily vehicle count should never be evaluated in isolation. A more useful planning model combines four KPIs: vehicles served per day, kWh delivered per day, charging utilization, and average response time.
| MCP-D item | Published specification | Why it matters operationally |
| Energy storage | 105 kWh | Sets the energy available in each onboard energy cycle |
| Maximum charging power | 100 kW | Sets the upper power limit for vehicle charging |
| Charging connectors | CCS1 / CCS2 | Supports deployment across major North American and European vehicle standards |
| Voltage range | 200-1000 Vdc | Covers a broad DC charging voltage range |
| Communication | OCPP 1.6J | Supports integration with charging-management and dispatch platforms |
| Autonomous driving | L4 | Enables autonomous movement within suitable mapped operating environments |
| Maximum travel speed | 10 km/h | Defines the upper movement capability; actual site speed will normally be lower |
| Protection rating | IP55 | Supports use in demanding operating environments |
| Operating temperature | -20°C to 65°C | Relevant for year-round site planning |
| Thermal management | Liquid cooling | Supports thermal control during charging and energy-storage operation |
| Battery cycle life | >5,000 cycles at stated test conditions | Relevant to long-term utilization and lifecycle planning |
The specifications above are taken from the Door Energy MCP-D product page. Buyers can also review the Door Energy company profile for manufacturing, engineering, and product-development information.
An EV Charger rating is an equipment limit, not a promise that every vehicle will continuously accept the full rated power. The actual average charging rate depends on the vehicle’s DC charging capability, battery temperature, state of charge, battery-management strategy, and charge taper at higher SOC.
For capacity planning, use the expected average charging power during the target charging window. For example, if a vehicle receives 35 kWh at an average of 70 kW, the theoretical energy-transfer time is 30 minutes. If the same vehicle only needs 20 kWh to complete its next route, the theoretical energy-transfer time falls to about 17 minutes.
This is one of the most important operational decisions. In a fleet or long-dwell parking environment, every vehicle does not necessarily need a full charge. A vehicle that needs 18 kWh to complete its next shift may not benefit from receiving 50 kWh immediately. Target-energy charging can allow the same Mobile EV Charger to complete more useful jobs with the same onboard energy and available operating time.
Total Task Time = Dispatch + Travel + Positioning + Connection + Charging + Disconnection + Task Switching
Door Energy’s autonomous charging workflow can be summarized as: charging request, vehicle localization, autonomous movement, connection, charging, task completion, and return to standby or movement to the next task. This means charging time is only one component of the cycle.
| Task component | Illustrative time | Operational question |
| Dispatch and request confirmation | 1 min | Can the platform assign tasks automatically? |
| Travel to vehicle | 3 min | How far apart are the target parking bays? |
| Final positioning | 1 min | How complex is alignment near the vehicle? |
| Connect charging cable | 2 min | Automatic arm or manual connection? |
| Energy delivery | 25 min | How many kWh are required and what is average accepted power? |
| Disconnect and confirm completion | 2 min | How quickly can the task be closed? |
| Switch to next job | 1 min | Can the robot go directly to the next vehicle? |
| Total | 35 min | This is the figure used for time-capacity calculations |
Time-Limited Jobs per Day = Available Operating Minutes × Utilization Rate ÷ Average Task Time
| Operating window | Utilization | Average task time | Time-limited jobs/day |
| 8 h | 50% | 35 min | ~7 |
| 8 h | 70% | 35 min | ~10 |
| 12 h | 50% | 35 min | ~10 |
| 12 h | 70% | 35 min | ~14 |
| 16 h | 60% | 35 min | ~16 |
| 16 h | 75% | 35 min | ~21 |
| 24 h | 60% | 35 min | ~25 |
| 24 h | 75% | 35 min | ~31 |
These numbers are not final capacity figures because they only test the time constraint. A storage-based EV Charger is also constrained by onboard energy. The actual planning value is the lower of time-limited capacity and energy-limited capacity.
In a compact parking area, a three-minute movement may seem insignificant. However, repeated across 15 or 20 daily tasks, travel can consume an hour or more. If the average movement time rises from three to eight minutes across 15 jobs, the robot spends 75 additional minutes moving rather than delivering energy.
| Average task distance | Illustrative average travel speed | One-way travel time |
| 50 m | 4 km/h | ~0.75 min |
| 100 m | 4 km/h | ~1.5 min |
| 200 m | 4 km/h | ~3 min |
| 300 m | 4 km/h | ~4.5 min |
| 500 m | 4 km/h | ~7.5 min |
| 800 m | 4 km/h | ~12 min |
This is why route planning should be part of the business case. Door Energy also discusses this issue in its parking-lot route-planning article. Instead of returning to base after every task, a robot can be scheduled through a cluster of nearby vehicles before returning for recharge.
The following examples use a 70 kW average vehicle charging rate for illustration, plus approximately 10 minutes of combined non-charging time for travel, positioning, connection, disconnection, and task switching. They are intended as planning examples, not guaranteed product performance.
| Energy delivered per vehicle | Pure charging time at 70 kW | Illustrative total task time | 105 kWh theoretical jobs per onboard cycle |
| 10 kWh | ~9 min | ~19 min | 10.5 |
| 20 kWh | ~17 min | ~27 min | 5.25 |
| 30 kWh | ~26 min | ~36 min | 3.5 |
| 40 kWh | ~34 min | ~44 min | 2.6 |
| 50 kWh | ~43 min | ~53 min | 2.1 |
If vehicles only require about 10 kWh of opportunity charging, energy transfer can be relatively short. A 12-hour operating window at 70% time utilization could theoretically support more than 20 task cycles from a time perspective. However, onboard energy becomes the tighter constraint. In practice, the robot must return for recharge after a number of jobs, and reserve energy plus conversion losses should be included in the operating model.
At 20 kWh per vehicle, theoretical charging time at 70 kW is about 17 minutes. With approximately 10 minutes for movement and handling, one job may take around 27 minutes. A 12-hour day at 70% utilization gives a time ceiling of roughly 18 jobs, but 105 kWh of onboard storage theoretically covers only about five 20 kWh deliveries before losses and operating reserve are considered. Therefore, daily output depends strongly on how many effective robot recharge cycles can be completed.
When each vehicle requires 30-40 kWh, daily vehicle count falls even if the EV Charger can deliver high power. A larger share of both onboard energy and time is consumed by each job. In these scenarios, the operating team should focus less on “vehicles per day” and more on total kWh delivered, departure deadlines, and whether a second robot can alternate with the first during recharge.
| Average energy per vehicle | Typical job duration in this model | Conservative jobs per onboard cycle* | Illustrative daily planning range** | Best-fit use |
| 10 kWh | 18-20 min | ~8-9 | ~16-25 | Short opportunity charging |
| 20 kWh | 25-30 min | ~4 | ~10-16 | Routine parking-facility charging |
| 30 kWh | 35-40 min | ~3 | ~8-12 | Medium-depth charging |
| 40 kWh | 40-50 min | ~2 | ~5-8 | Deeper charging / lower task count |
* Conservative cycle values intentionally leave room for operating reserve and conversion losses. ** Illustrative daily ranges assume multiple recharge cycles and site conditions that support continued dispatch. They are not guaranteed performance values. Actual results depend on vehicle acceptance power, route distance, charging demand, robot recharge strategy, and dispatch efficiency.
Related reading: Mobile Power Station Applications | Roadside Charging and Fast Replenishment
A storage-based Mobile EV Charger must eventually replenish its own battery. This is a key difference from a fixed grid-connected charger. When planning capacity, the operator should identify the return-to-charge point, available input power, queue risk at that point, and whether recharging can occur during naturally low-demand periods.
Door Energy’s broader mobile charging portfolio also supports different recharge approaches. For applicable models, the company describes approximately one hour for DC replenishment and approximately two hours for AC replenishment under stated operating conditions. The exact recharge strategy for a project should always be confirmed against the selected model and site power source.
| Data to collect before sizing | Example | What decision it supports |
| Vehicles requiring charging per day | 42 vehicles | Baseline demand |
| Average energy needed per vehicle | 18 kWh | Energy-cycle requirement |
| Peak simultaneous requests | 12 requests | Queue and robot-count planning |
| Average parking duration | 3.5 h | Whether mobile scheduling has enough time flexibility |
| Latest departure time | 17:30 | Task-priority rules |
| Average robot travel distance | 180 m | Travel-time loss |
| Existing fixed charger count | 14 units | How mobile charging supplements existing infrastructure |
| Available robot recharge source | DC / AC / dedicated point | Recharge downtime and operating continuity |
| Door Energy capability | Customer problem | Operational value |
| 105 kWh onboard storage | Charging demand exists away from fixed power points | Energy can be moved to parked vehicles instead of requiring a charger at every bay |
| Up to 100 kW charging output on MCP-D | Long service cycles reduce daily throughput | Higher available power can shorten energy-transfer time when the vehicle can accept it |
| L4 autonomous movement | Manual vehicle relocation or staff dispatch adds labor | Robot can travel to assigned bays in suitable mapped environments |
| OCPP 1.6J | Standalone equipment creates data silos | Supports integration with charging and dispatch platforms |
| CCS1 / CCS2 | Different regional vehicle standards | Supports major North American and European charging interfaces |
| Modular maintenance approach across Door Energy solutions | Equipment downtime reduces daily service capacity | Faster maintenance can reduce lost operating hours |
Explore more Door Energy use cases: Mobile EV Charger Product Range | Construction-Site Mobile Power | Emergency Mobile Power
A charging robot is especially attractive when demand is distributed across many parking spaces, only a subset of vehicles need energy at one time, electrical upgrades to every bay would be expensive, charging demand changes by zone or time of day, or operators want flexible capacity for peaks and contingency support.
Door Energy’s article on shared charging for commercial parking facilities explores this shared-resource logic in more detail.
A mobile robot is not automatically the best answer for every site. If every vehicle parks in the same assigned location for long periods, the site already has sufficient electrical capacity, charging demand is highly predictable, and nearly every vehicle requires deep charging every day, fixed charging infrastructure may provide a simpler long-term solution.
This distinction improves project quality. Door Energy’s role should not be to force every site into the same architecture, but to help customers determine where mobile charging creates measurable value and where fixed charging should remain the base layer.
For many larger facilities, the best design may combine fixed chargers with one or more Mobile EV Charger units. Fixed chargers handle predictable overnight or assigned-bay demand, while mobile robots absorb temporary peaks, serve ordinary parking spaces, support vehicles that cannot move to a charger, and provide redundancy when fixed infrastructure is occupied or unavailable.
There is no single fixed number. Daily capacity depends on the average kWh delivered per vehicle, the vehicle’s accepted charging power, travel distance, task-handling time, operating hours, utilization, and how often the robot can recharge. In an opportunity-charging model, daily vehicle count can be much higher than in a deep-charge model. The correct approach is to calculate both the time constraint and the energy constraint, then use the lower value.
That calculation only estimates the theoretical number of jobs in one onboard energy cycle. A robot can recharge and return to service during the same day. Daily service capacity therefore depends on the number of effective energy cycles that can be completed as well as the total available operating time.
No. The MCP-D can provide up to 100 kW, but actual vehicle charging power is controlled by compatibility, battery temperature, SOC, the vehicle’s battery-management system, and the charging curve. Capacity models should use realistic average accepted power rather than assuming the maximum for the entire session.
Daily kWh delivered is critical. Serving ten vehicles at 10 kWh each is operationally very different from serving ten vehicles at 40 kWh each. Operators should monitor both job count and energy throughput, along with utilization and response time.
Reduce unnecessary travel, cluster nearby tasks, assign charging based on departure deadlines, avoid overcharging vehicles that only need limited energy, shorten connection and task-switching time, and schedule robot recharging during low-demand periods. Data-driven dispatch can often release capacity that already exists.
A second unit becomes more attractive when peak-period queues are persistent, response time is increasing, one robot regularly operates near the planned utilization ceiling, or robot recharge periods repeatedly interrupt required service. Two units can also alternate between vehicle service and self-recharging.
The published MCP-D configuration supports CCS1 and CCS2 charging connectors and OCPP 1.6J communication. Project teams should still verify the exact target vehicles, charging interface, voltage range, and backend requirements before deployment.
No. The mobile-energy concept can also support fleet depots, logistics facilities, commercial parking, airport-related operations, roadside assistance, and selected industrial applications. The MCP-D itself is especially suited to mapped areas with defined parking locations and predictable movement boundaries.
Not necessarily. In many facilities, a hybrid model is stronger. Fixed charging can handle predictable baseline demand, while a mobile robot provides flexible capacity for dispersed requests, peaks, ordinary parking bays, and backup service.
Prepare the number and type of vehicles, charging connector requirements, battery sizes, typical arrival SOC, target departure SOC, daily charging demand, parking layout, average travel distance, operating hours, peak request periods, existing charger inventory, and the power source available for recharging the robot. This information makes capacity sizing much more reliable.
For additional technical and purchasing questions, see the Door Energy FAQ or browse the Door Energy News & Applications section.
For parking facilities and fleets, the value of an autonomous EV Charger should not be judged only by rated power, storage capacity, or maximum travel speed. What matters is the entire operating cycle: how much energy each vehicle actually needs, how quickly the vehicle can accept that energy, how far the robot must travel, how long connection and switching take, how frequently the robot must recharge, and how efficiently the dispatch platform converts available hours into productive charging.
A useful planning model is:
Task Time = Travel + Positioning + Connection + Charging + Disconnection + Task Switching
Time-Limited Capacity = Available Operating Time × Utilization ÷ Average Task Time
Energy-Limited Capacity = Available Daily Robot Energy ÷ Average Energy Delivered per Vehicle
Planning Capacity ≈ the lower of the time-limited and energy-limited results
This framework also explains why two sites using exactly the same Mobile EV Charger can produce very different results. A parking facility that mainly provides 10-20 kWh opportunity charging may complete many more daily jobs than a site where every vehicle requires 40-50 kWh. Likewise, a compact parking layout with intelligent task clustering can outperform a large facility where the robot repeatedly travels long distances or returns to base after every job.
Door Energy’s MCP-D combines 105 kWh of onboard energy storage, up to 100 kW vehicle charging output, CCS1/CCS2 connectivity, OCPP 1.6J, and autonomous movement in one platform. Instead of installing high-power charging capability at every parking bay, the system is designed to move charging capacity toward the vehicles that need it. Learn more on the Door Energy homepage and the MCP-D autonomous charging robot product page.
For buyers, the final procurement question should therefore move beyond “How many chargers should we install?” A better question is: how many vehicles need energy, how many kWh do they actually need, when must that energy be delivered, and what combination of fixed and mobile charging resources can meet that requirement with the least idle capacity? That is the point at which a charging robot becomes not just a piece of equipment, but an operational energy-dispatch tool.
Product specifications and Door Energy application concepts referenced in this article are based on Door Energy published product and application materials. All numerical service-capacity examples are illustrative planning models and should be validated against actual site data before procurement or deployment.
