Project Description
Developing a complex e-mobility pricing system
With 25+ companies in the Conclusion’s unique ecosystem, Conclusion Intelligence is part of a diverse team that inspires and empowers each other. This often results in cross-collaborations that allow us to provide our clients with the best possible solution. Let’s have a look at a use case where this applies in practice!
Together with our sister company, First8, we developed an internal pricing system capable of handling the complexity of pricing data. We do this for one of the leading companies that builds a network of powerful electric vehicle (EV) charging stations along major highways in Europe! During this project, we provided support to our client in the following areas:
- Price configuration
- Microservices architecture
- Web development
- Cloud migration
Theme
Intelligent Solutions
Sector
E-mobility
The concept
Our client works with B2B partners with whom they have made price agreements for charging sessions. In order to accurately manage these price settlements and communicate the correct information to the charging stations, a price management system is required. Our project’s goal is to develop a comprehensive application logic; from tariff management to pricing of charging sessions.
The challenge
Pricing configuration is complex, with rules varying by factors like partner rates or weekday vs. weekend pricing. Accurate data is vital, requiring secure setup, historical tracking, and protection against unauthorized changes. Additionally, our team must explore E-mobility protocols for B2B use cases due to limited available information.
Techniques and technologies
The team was divided into several different fields including Software Engineering, DevOps/ infrastructure and QA (Quality Assurance). The following methods and techniques are used:
Angular
Kotlin/Spring Boot
PostgreSQL database
Microservices architecture with event-driven communication
- Open Charge Point Interface (OCPI)
- Amazon Web Services (AWS)
Result
The provided solution retrieves data every five minutes, depending on the type of patients and specialties. The created model incorporates a comprehensive analysis of patient flow patterns, taking into account the influx data from the past two years. This historical insight, combined with real-time updates, provides patients with a transparent and reliable estimation of their waiting times. Overall, this innovative solution has significantly improved the patient experience in the ER.
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