routevolt

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Description

routevolt is an open-source, cloud-ready service that offers real-time route optimization and charging support for electric vehicles (EVs). It can be accessed through an API and is designed to deliver practical and accurate estimations of travel time and state of charge (SOC). Users can employ routevolt either for planning direct trips between chosen coordinates or for identifying suitable charging stations within a defined search area.

Unlike conventional navigation tools, routevolt integrates multiple data sources—such as live traffic, weather conditions, and terrain information—to provide estimates that are closer to real driving outcomes. It also adjusts for secondary effects like vehicle self-discharge during idle times, auxiliary power usage, and variations caused by traffic, making the results more reliable for everyday use.

The service is useful not only for individual EV drivers but also for stakeholders such as charging infrastructure operators, distribution system managers, and researchers, who can leverage its outputs for better planning, optimization, and testing of e-mobility strategies.

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Modes of Operation

routevolt can be used in two primary ways:

1. Navigation Mode

   • Designed for EV drivers planning trips.
   • Provides multiple route alternatives.
   • Each option includes:
     • Estimated time of arrival (ETA)
     • State of charge (SOC) at the destination
     • A sustainability score based on energy efficiency

2. ITS Integration Mode

   • Intended for integration with traffic and mobility management systems.
   • Identifies suitable charging stations along or near the planned route, within a user-defined radius.
   • Returns SOC and travel time estimates for each suggested station.
   • Enables more precise operational planning, infrastructure use, and resource allocation.

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Key Features

• Dynamic Route Analysis

  • Calculates the fastest and most energy-efficient routes.
  • Incorporates live traffic conditions for realistic planning.

• Charging Station Recommendations

  • Finds compatible charging points within a chosen search radius (ITS Mode).
  • Powered by Open Charge Map data.

• Accurate SOC Estimation

  • Adjusts for temperature effects on auxiliary consumption.
  • Differentiates by road type (highway vs. urban) for tailored discharge rates.
  • Considers altitude changes, including regenerative braking on downhill segments.
  • Uses a Mean Percentage Error (MPE)–based correction for improved accuracy.

• Real-Time Data Integration

  • Combines inputs from Google Maps, Open Meteo, and Open Charge Map.
  • Ensures predictions stay up to date with current conditions.

• Sustainability Ranking (Navigation Mode)

  • Ranks alternative routes by SOC efficiency.
  • Encourages environmentally friendly travel choices.

• Feedback-Driven Learning

  • Users can provide actual SOC and travel time at trip completion.
  • System refines its predictions over time for continuous improvement.

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Feedback and Learning

routevolt includes a feedback mechanism to improve future estimations. Users can provide their actual SOC at arrival and actual travel time, which are incorporated into the MPE-based learning approach to continuously refine prediction accuracy.

Feedback API Request Format:

  {
    "trip_id": "002",
    "destination": "50.785, 6.047586",
    "route": "B1 and Pariser Ring",
    "actual_arrival_SOC": "77",
    "actual_travel_time": "900"
  }

Field Descriptions:

• trip_id: Unique identifier for the trip (string).
• destination: Latitude/Longitude coordinates of the destination (string, format: "lat, lon").
• route: Name or identifier of the route taken (string).
• actual_arrival_SOC: State of charge at arrival, in percent (string, 0–100).
• actual_travel_time: Actual travel duration, in seconds (string).

Contact

• Aytug Yavuzer, M.Sc. aytug.yavuzer@eonerc.rwth-aachen.de
• Sreejith Pananchickal Sajeev, M.Sc. sreejith.pananchickal@eonerc.rwth-aachen.de
• Taeyoung Kim, M.Sc. taeyoung.kim@eonerc.rwth-aachen.de
• Univ.-Prof. Antonello Monti, Ph.D. post_acs@eonerc.rwth-aachen.de

Institute for Automation of Complex Power Systems (ACS) E.ON Energy Research Center (E.ON ERC) RWTH Aachen University, Germany