Harnessing AI Automation for Fast Charger Diagnostics
Our AI-powered solution simplifies charger log file reading for manufacturers, maintenance providers, and CPOs. It enables quick issue identification, insightful analysis, and improved system performance, while reducing manual effort.Book a Demo
EV charging infrastructure up time is critical to EV adoption
High charger uptime is essential for the widespread adoption of electric vehicles. Downtime can lead to range anxiety and discourage drivers from switching to electric vehicles, slowing down the pace of adoption and negatively impacting the entire EV industry. Furthermore, low charger uptime can harm charger manufacturers by reducing customer satisfaction, increasing maintenance costs, and damaging their reputation. Customers may be less likely to purchase chargers from manufacturers with a history of low uptime, leading to lost sales and market share. In contrast, high charger uptime can build customer loyalty, increase sales, and establish a positive brand image. Therefore, ensuring high charger uptime is crucial for the success of charger manufacturers in the rapidly growing EV market and the adoption of electric vehicles. Investing in robust monitoring and maintenance solutions can help to achieve high charger uptime, increase customer satisfaction, and drive the growth of the EV industry.
Parse and visualize any charger log file
- Enables integration with any OCPP-based charger backend or log file for easy compatibility and accessibility.
- Extracts valuable information such as charging session durations, rates, energy consumption, and charger availability from raw EV charger log files.
- Organizes the extracted data into a structured format for effortless analysis and interpretation.
- Presents the extracted data in visually appealing graphs, plots, and charts directly on our platform, providing comprehensive insights and analysis.
- Empower users to gain valuable insights into usage patterns, identify performance issues and trends, and make informed decisions about maintenance.
- Ensures secure storage and proper configuration of EV charger logs while protecting personally identifiable information (PII) of the EV driver.
- Streamlines the root cause analysis (RCA) process by providing a user-friendly interface and easy-to-use tools for analyzing charging session data, allowing users to quickly and efficiently identify the underlying cause of charger issues.
Fast and Accurate EV Charger Diagnosis with AI Automation
- Our solution leverages a supervised learning approach, which involves training an AI algorithm on labeled data to recognize patterns and detect anomalies in EV charger log files.
- Service engineers can label log files to provide the algorithm with examples of normal and abnormal charging behavior, enabling it to learn and improve over time.
- By keeping humans in the loop, our solution can leverage their expertise and insights to enhance the accuracy and effectiveness of the automated diagnosis process.
- The solution can alert service engineers to potential issues or anomalies, allowing them to investigate further and take corrective action, while minimizing downtime and service costs.
- Through its ability to automate diagnosis and optimize maintenance and repairs, our solution can help increase the reliability and availability of EV charging infrastructure, improve user satisfaction, and support the broader adoption of electric vehicles.
Predictive maintenance and ticket management system
Our solution's roadmap includes predictive maintenance and service ticket management features that will enhance the efficiency, reliability, and availability of EV charging infrastructure. The predictive maintenance feature will analyze real-time data to detect issues early, reduce downtime, and optimize maintenance schedules, while the service ticket management platform will improve communication and coordination for timely issue resolution. Together, these features will reduce costs, improve service quality, and support the adoption of electric vehicles.Let's talk!
Reducing diagnosis time by 90%
Tritium, a global EV charger manufacturer, faced the challenge of providing customers with fast and accurate diagnostic support. They turned to our automated diagnostics solution, which uses supervised machine learning to classify patterns in sensor data and diagnose faults. Our solution allowed Tritium to train their own ML model with a small dataset, reducing the time and resources needed to deploy the solution. By leveraging our solution's classification capabilities, Tritium was able to quickly identify and classify fault types with high accuracy, reducing diagnosis time by up to 90%. The efficiency gains of our solution could improve Tritium's customer service, reduce downtime, and increase efficiency.