Fraunhofer Institute for Transportation and Infrastructure Systems IVI
Fraunhofer Institute for Transportation and Infrastructure Systems IVI
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IVImon

Data-driven fleet management for e-mobility

© panitan | Adobe Stock | Fraunhofer IVI

Monitoring Platform for the Cost-efficient Operation of Electric Vehicles

The shift to alternative drive systems opens up new opportunities, but italso presents many challenges. For example, planning and implementation often require significant effort and substantial costs upfront. Modern solutions rely on detailed route planning, intelligent charging strategies, and precise range estimates to optimize charging times and battery usage.

In addition, new business models with batteries at their center are emerging: separate leasing of the vehicles and batteries, partnerships with charging infrastructure operators, or new workshop concepts. This allows batteries to be used efficiently and mobility to be designed with the future in mind.

In order to use batteries efficiently and optimally manage daily fleet operations, reliable data and smart analytics are essential. IVImon securely transfers your vehicles’ data to the cloud and is specifically designed for fleet applications.

Whether logistics provider, shipping company, or municipal utility—with IVImon, everyone can get more out of their electric vehicles

Up to 20% longer battery life

The system identifies anomalies and performance deviations at an early stage, long before they can cause critical disruptions in operation.

By adapting operational and charging stategies to the actual battery state, service life can be extended by up to 20 percent—a significant cost advantage.

Fewer downtimes, higher availability

Real-time remote diagnostics allows the continuous monitoring of battery states and ageing processes—without having to remove vehicles from service.

Operators have access to critical parameters at all times and can take proactive measures.

Decisions based on real vehicle data

The foundation is long-term data storage. By continuously recording relevant parameters, a digital twin of your fleet is created, allowing you to track developments over months and years.

Customizable dashboards ensure that every user receives exactly the information relevant to their role—from dispatchers and technicians to upper management.

Digitalizing fleets – reducing costs – understanding batteries

This is how IVImon works with your data

Dashboard of IVImon platform
© Fraunhofer IVI
Project example: Trolley bus fleet monitoring
Dashboard of IVImon platform
© Fraunhofer IVI
Project example: shipping and logistics. Analysis of traffic to and from the yard forms the basis for an electrification plan.

Long-term data storage

In contrast to cloud-based OEM solutions, IVImon guarantees comprehensive and long-term data storage. This allows well-founded trend analyses and strategic decisions.

 

Flexible data collection & complete data sovereignty

IVImon ensures continuous data transmission—through seamless integration with existing OEM telematics solutions or direct data access via its own hardware. All data is stored and processed on local servers—without involving third-party providers or big tech companies. Your data stays with you: secure and private.

 

Insightful analyses & custom dashboards

From battery performance and charging behavior to energy demand forecasts and availability analyses—IVImon provides customized reports. Dashboards are individually configured in collaboration with our experts and continuously refined based on your feedback.

Practical recommendations for urban e-bus fleets

 

© AI-generated | Fraunhofer IVI

Optimized battery strategies for public transport

Aricle in World Electric Vehicle Journal

Read article

Projects and references

VALA - Validation of novel algorithms for battery diagnostics based on machine learning methods

Established methods for battery diagnostics rely on complex laboratory measurements of individual battery cells or modules, which do not adequately reflect real-world conditions. The VALA project, therefore, takes the approach of using neural networks to learn battery behavior from field data. The aim is taking the accuracy of battery diagnostics to a new level and significantly extending battery life.

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