EV Charging Fault Translation for Clear User Guidance
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Solution Overview
Problem
The increasing demand for public EV charging stations is hindered by infrastructure reliability issues, including faulty chargers, which cause inconvenience to users due to complex technical fault codes that are difficult for laypeople to understand.
Innovation Solution
A vehicle charging optimization system that translates technical fault codes into user-friendly natural language messages, identifies charging faults, and provides remedial actions, while calculating user and station assessment scores to enhance charging experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If technical fault codes are used to identify charging faults, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent introduces an intermediary translation layer that converts technical fault codes into user-friendly natural language messages. The system includes a message generator that receives fault codes from the charging system and translates them into explanatory messages with remedial actions, serving as a mediator between the technical charging system and the end user.
2Reliability
If detailed fault analysis is performed, then reliability is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary fault analysis by continuously monitoring charging parameters and pre-identifying potential issues before they escalate. The message generator is ready to immediately translate and present fault information as soon as a problem is detected, reducing the time users spend waiting for diagnostic information.
Solution Approach 2:
The system implements a feedback loop where fault messages are presented to users with remedial actions, and the system continues to monitor whether the fault persists after user intervention. This enables rapid iterative diagnosis and resolution, reducing overall troubleshooting time while maintaining reliable fault detection.
Data Source
AI summary
A vehicle charging optimization system including a transceiver and a processor is disclosed. The transceiver may be configured to receive vehicle information from a vehicle or a server. The processor may be configured to determine that the vehicle may be plugged-in to a charger associated with a charging station based on the vehicle information. The processor may further determine that the vehicle did not charge by using the charger based on the vehicle information, and determine that a first type of fault may have occurred in charging the vehicle based on the vehicle information. The processor may further translate a first error code associated with the first type of fault to a first message in natural language, and output the first message on a user device or a vehicle Human-Machine Interface.