Fleet Telematics and Route Optimization
EV fleet management software aggregates real-time telematics data from every vehicle in the fleet: State of Charge, location (GPS), speed, driving behavior, battery temperature, charging session status, and fault codes. This data enables operational decisions impossible with a conventional ICE fleet. Route optimization for EVs is fundamentally different from ICE route optimization because range constraint is a hard limit — unlike ICE vehicles that can be refueled in 5 minutes at any gas station. The fleet management platform must solve a multi-constrained optimization problem: assign drivers to routes that their vehicle's current SoC can complete (with a safety margin, typically 20% SoC at route completion), minimize total energy consumption across all routes (considering terrain — hilly routes consume more energy, affecting which vehicles are assigned), incorporate charging stops for longer routes where available charging infrastructure allows mid-route charging, and meet all delivery time windows. Modern fleet optimization uses variations of the Electric Vehicle Routing Problem (EVRP), an NP-hard combinatorial optimization problem solved approximately using metaheuristic algorithms (genetic algorithms, simulated annealing, or machine learning-based approaches). Predictive range modeling uses individual vehicle's energy consumption history (vehicle efficiency degrades as battery ages, as tire pressure varies, and in cold weather) to provide accurate per-vehicle range estimates rather than generic manufacturer specifications. A well-optimized fleet with smart charging and route assignment typically achieves 10–20% reduction in total energy cost compared to unoptimized operations.