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Konetik Receives SME Instrument Support for Its New AI Based Charging Assistant

Konetik Receives SME Instrument Support for Its New AI Based Charging Assistant

Konetik’s long term vision is to support e-mobility integration and management with smart software solutions. To take a step further we are currently working on an AI based charging assisstant service for business fleets. We have conducted a research project in this field co-funded by the SME Instruments programme of the European Union.

Light Commercial Vehicles are important for the EV adoption as they have the operational usage patterns where the electrification ROI is high but on the other hand this segment faces a lot of challenges due to the limited battery range and limited charging opportunities. During the project our aim was to conduct a feasibility study of potential development of the first AI based charging assistant for Light Commercial Vehicle fleets.

Konetik already developed an automated EV suitability advisor and charging assistant to help fleets to integrate electric vehicles.During the project we validated the technical feasibility of the product by examining the the State of Charge data acquisition opportunities and and validating the accessible APIs of charging infrastructure. We have find no technical obstacle hindering the development of AI based charging assistant tool. Based on the results of the technical feasibility researches we have created the concept of the MVP and have outlined a technology roadmap for the development process.

We also analyzed the product concept from business point of view. We conducted a market research and competitor analysis in the e-mobility scene. The research has shown developing competition in the broadening plate of e-mobility solutions. However, the combination of state of charge data, location data and charging information proves to be still a less utilized area.

We have conducted 20 interviews with fleet managers and e-mobility experts to understand the major daily challenges of electric vehicle fleet operation and validate the necessity of our charging assistant solution. The interviews have confirmed our hypothesis about difficulties with charging management and represented the customer need for a holistic solution.

 We elaborated the business model and market access strategy for the AI based charging assistant solution. We examined the legal environment and conducted a risk analysis on the business and technical aspects of the product.

Developing our LCV charging management tool will allow us to facilitate the market spread of eLCVs with the first machine learning based smart charging assistant tool based on our unique algorithm that combines advanced energy management and telematics.

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