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EV Fleet Orchestrator

Predict, Optimize, Charge and Go

Optimize EV fleet charging and energy use operation-wide for reduced costs and emissions

Intelligent management of EV fleet charging and dispatch

Integration of solar generation and battery energy storage

Intelligent management of
mixed fleets

It’s a highly complex task to effectively manage the operation and charging of EV fleets, along with building energy management, local generation and storage, and energy purchases, plus meeting service levels and turnaround time requirements. It requires the intelligent coordination of separate but inter-related systems and the ability to do so in real-time. No one system or human operator is up to the full task.

This is where the BluWave-ai EV Fleet Orchestrator offers a novel solution. Built on BluWave-ai’s distributed AI-enabled platform, the EV Fleet Orchestrator optimizes energy costs in real-time by consolidating the many parameters of energy and fleet operations, providing a holistic view and coordinated energy dispatch/control.

EV FLEET
ORCHESTRATOR
PRODUCT BRIEF

The BluWave-ai EV Fleet Orchestrator pulls together:

  • Intelligent management of EV fleet charging and dispatch. This factors in energy price, peak demand constraints and equipment schedules, considering factors such as equipment service requirements and routes.
  • Integration of solar generation and battery energy storage. Having local generation and battery storage reduces energy costs and emissions through considering load, energy prices and solar output as driven by weather conditions.
  • Intelligent management of mixed fleets. Most operations will run a mix of gas-powered, hybrid and EVs for an extended period. The BluWave-ai EV Fleet Orchestrator effectively manages mixed fleets, factoring in ambient temperature, energy prices, optimal battery cycling and more

The BluWave-ai Advantage

Opportunity Assessment and Deployment

BluWave-ai’s team of AI and data scientists, business analysts, and smart grid engineers can assess your project or existing system and determine the appropriate AI-based control and optimization solution to maximize economic benefits and operational reliability for your unique environment.

Customers appreciate the clarity and quantifiable benefits demonstrated through our low-risk, multi-phase deployment process. It starts with an opportunity assessment and AI model building service based on historical or simulated data. This initial phase includes a survey of site assets and computing and communications infrastructure, and plans for the connection of sensors and data conditioning. This assessment can be either the first phase of deployment or a standalone service. Read more about deployment.

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AI MODEL BUILDING AND GOING LIVE

Optimize EV Charging in Concert with System-Wide Mixed Fleet and Energy Management