Periodic Reporting for period 1 - Konetik eLCV (Artificial Intelligence based Smart Charging Assistant for Electric Light Commercial Vehicle Fleets)
Reporting period: 2018-11-01 to 2019-01-31
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 autonomy and constrained charging opportunities. During the project our aim was to conduct a feasibility study of the 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. Based on the learnings of the previous projects, we identified that Large Commercial vehicles have a great electrification potential, so we chose this early adopter segment to customer base of our future product.
Konetik already developed an automated EV suitability advisor and charging assistant to help fleets to integrate electric vehicles. Based on the learnings of the previous projects, we identified that Large Commercial vehicles have a great electrification potential, so we chose this early adopter segment to customer base of our future product.
During the project we validated the technical feasibility of the product by examining the the State of Charge data acquisition opportunities and validating the accessible APIs of charging infrastructure. We found 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. 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.
The Konetik team 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.
We also analyzed the product concept from business point of view. We conducted a market research and competitor analysis in the e-mobility scene. 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.
The Konetik team 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.
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.