AVOIDING SERVICE DOWNTIME
Enhancing User Satisfaction in EV Charging stations through AI-Driven Predictive Maintenance
One of the challenges in deploying a successful Electric Vehicle (EV) Charger network is infrastructure reliability : clients will churn to competitors when experiencing outages or slower than usual charging. Our client aimed to create a unique predictive maintenance software in-house, designed to identify a range of charger faults. iuvo-ai was chosen as a primary AI partner to expedite the development of their Minimum Viable Product (MVP), ensuring a rapid market entry.
MVP Development
Deploying an EV charger network across the United-States, our client seeked accurate infrastructure failure predictions a few days ahead in order to allow its maintenance team to take action on our model’s predictions.
Dataset Imbalance
A common challenge with predictive maintenance modeling is the imbalanced nature of the data. Charger faults, occurring less than 99.5% of the time, required the use of a specialized set of ML techniques to be handled appropriately.
Ever Expanding Network
Given the fast-paced evolution of the EV infrastructure industry, we developed a modeling architecture capable of seamlessly adapting to these dynamic changes.
Agile Development and AI Integration
Using an agile development approach, we worked with our client’s infrastructure and data experts in an iterative way to improve the model’s performance and enhance the solution. For the solution’s first iterations, we prioritized failures that mattered most to the client and its customers to ensure that we delivered value swiftly.
Using Appropriate ML and Data Architecture
Imbalanced Time-series data presents unique ML requirements. We quickly iterated through different data processing and model architectures allowing us to find the most efficient technique in a matter of weeks.
Timely and Efficient Solution
Our optimized AI model quickly yielded accurate results which would allow our client to optimize its maintenance operations and sustain a reliable EV charger network.
Through agile development and ML expertise, we enabled our client to proactively detect and mitigate infrastructure faults. This successful project ensures optimal maintenance operations, contributing to the sustained reliability of the EV charger network and the company's competitive edge in the market.