DC fast chargers (DCFCs) are critical infrastructure for fleets and public EV charging networks, but they don’t always “just work.” Unlike simple electrical equipment, modern EV chargers are connected, software-driven systems that generate constant diagnostic data and status codes. Managing this data efficiently is one of the biggest challenges in EV charging operations today.
The Challenge of Interpreting EV Charger Status Codes
If you operate DCFCs, you will likely get bombarded by “status codes” generated by your chargers daily. They cover a wide range of messages: everything from “someone stopped charging in an unusual way, but the charger is fine” to “this charger’s main circuit board has failed and the charger is non-functional”. The tricky part is to
- interpret these status codes
- distinguish which ones are serious enough to require intervention
- decide how to resolve them with remote resets vs. dispatching a technician to the site
- and (ideally) to sift through the patterns of codes over time to predict failures before they happen
Until recently, that meant a lot of time and effort for human operators to read through charger diagnostic logs and status code descriptions, manually analyzing large datasets in Excel spreadsheets, and occasionallyimplementing limited automations to remotely reset chargers when specific status codes appear.
How AI and Large Language Models Improve EV Charger Troubleshooting
Now, with the latest large language models (LLMs), the diagnostic and troubleshooting process can be greatly accelerated. It’s easy to feed in days or months or even years of charger logs and status codes, along with a clear description of the issues observed, into a LLM chat interface. Within minutes, you can receive a comprehensive summary of what went wrong, likely root causes, and a prioritized list of next steps to isolate the problem and then fix it. With enough contextual information, these LLMs can diagnose issues just as well as many charging hardware application engineers.
Reducing EV Charging Operational Costs with AI
This is exciting not just because it makes the lives of EV charging operators a whole lot easier, but it will bring down the cost of EV charging for the entire industry. One major constraint today on the growth of reliable EV charging is the human capacity to rapidly diagnose and troubleshoot chargers that fail. With AI tools, fewer people will be able to manage more chargers while delivering high uptime and a great customer experience in the field, reducing per-unit operational costs drastically. Many charging management software companies are now working toward the goal of automating most of the processes involved in EV charging operations, to stretch the capability of each human operator as far as possible.
How Inspiration Mobility Uses AI in EV Charging Operations
Inspiration Mobility has been dedicated to refining our processes to leverage AI, which is already saving our team valuable time on each charger issue we encounter. And this is just the beginning of how we are maximizing LLMs to make EV charging better. As AI-enabled automation evolves, our team is ensuring that our processes prioritize efficiency so our team can focus on delivering higher-impact solutions and a more reliable charging experience for every customer.
If you’re operating DC fast chargers and struggling with uptime, diagnostic overload, or field service costs, our team can help design and optimize your EV charging operations process from deployment through long-term maintenance.
Author: Dan Wilson, VP of Energy Solutions