AI for Electric Delivery Vehicle Route Planning With Charging Station Optimization

Electric delivery vehicles are increasingly practical for urban and suburban delivery operations, but they introduce a planning constraint that diesel operations do not face: the battery has a finite range, and running out of charge is not solved by a quick stop at any fuel station. Route planning for electric fleets must account for the vehicle range, the delivery schedule, the locations and availability of charging stations, and the time required for charging.

AI route planning makes electric fleet operations viable by solving this multi-variable optimization problem.

Range Estimation That Reflects Reality

The rated range of an electric vehicle and its actual range in delivery operations are different numbers. Actual range depends on payload weight (heavier loads consume more energy), route elevation changes (hills drain batteries faster), ambient temperature (extreme cold and heat reduce range), driving patterns (stop-and-go delivery driving uses more energy per mile than highway driving), and auxiliary system usage (heating, cooling, lift gates).

AI range estimation models account for all of these factors using the fleet own historical data. Rather than planning routes based on the manufacturer rated range, the system uses realistic range estimates for the specific vehicle, load, route, and conditions. This prevents the situation where a vehicle runs low on charge mid-route because the plan assumed a range the vehicle could not achieve in practice.

Route Planning With Charging Integration

AI route planning for electric vehicles integrates charging stops into the route when the planned delivery route exceeds the estimated range. The system identifies charging stations along or near the planned route, evaluates their availability and charging speed, and inserts a charging stop at the optimal point in the route.

The optimization considers the time cost of the charging stop against alternative route configurations that might avoid the need for mid-route charging. Sometimes a slightly different delivery sequence eliminates the need for a charging stop by keeping the vehicle closer to its base where it can charge overnight.

Overnight Charging Optimization

For most delivery fleets, overnight charging at the depot is the primary charging method. AI manages the charging schedule to ensure every vehicle is fully charged for its morning departure while minimizing electricity costs. This involves staggering charging start times to avoid demand charge spikes, scheduling charging during off-peak electricity rate periods, prioritizing vehicles with early departure times or long routes, and managing total electrical load to stay within the facility power capacity.

Fleet Mix Optimization

During the transition period where a fleet operates both electric and diesel vehicles, AI optimizes which routes get assigned to which vehicles. Routes within the electric vehicle comfortable range get assigned to EVs. Routes that would require mid-route charging or that involve conditions unfavorable for EVs (extreme cold, heavy mountain routes) get assigned to diesel vehicles.

This intelligent assignment maximizes the utilization of the electric fleet while ensuring service levels are maintained. As the electric fleet grows and charging infrastructure improves, the proportion of routes assigned to EVs naturally increases.

For more on how AI supports fleet electrification in logistics, see FirmAdapt's logistics and transportation analysis.