Retrofit Vehicle Control Interface for Predictive Maintenance
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Solution Overview
Problem
Existing vehicle control systems lack efficient integration of adaptive mobility systems and predictive maintenance capabilities, particularly for vehicles with pre-retrofit components, and do not adequately support users with physical disabilities in controlling vehicle functions.
Innovation Solution
A retrofit control system with an embedded computer assembly (ECA) that collects operational data, generates predictive maintenance recommendations, and provides user-friendly control interfaces, including touch screens and voice/gesture recognition, to manage vehicle components and adapt to user preferences and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a retrofit control system is installed on a pre-retrofit vehicle, then predictive maintenance capabilities and user accessibility are improved, but device complexity and integration difficulty increase
Solution Approach 1:
The patent introduces an embedded computer assembly (ECA) as an intermediary component that mediates between the existing pre-retrofit vehicle systems and the new predictive maintenance functions. The ECA includes processing circuitry that collects operational data from existing sensors and components, processes this data through machine learning models, and generates maintenance recommendations without requiring fundamental changes to the original vehicle architecture.
Solution Approach 2:
The control system is designed with multi-functional capabilities that serve both original vehicle control functions and new predictive maintenance functions through a single integrated platform. The processing circuitry can perform real-time data collection, historical data analysis, predictive maintenance recommendation generation, and user interface management, eliminating the need for separate dedicated systems for each function.
2Measurement precision
If operational data is collected and processed in real-time, then predictive maintenance accuracy is improved, but energy consumption and processing load increase
Solution Approach 1:
The system performs preliminary data processing and feature extraction at the edge device (ECA) before transmitting data to remote servers. The processing circuitry pre-processes operational data by extracting relevant features and filtering out redundant information, which reduces the computational load required for accurate predictive maintenance while minimizing energy consumption during data transmission and remote processing.
Solution Approach 2:
The system implements a hierarchical processing approach where only critical real-time data is processed immediately with high precision, while less time-sensitive data is processed with lower precision or deferred to batch processing. This selective processing strategy maintains predictive accuracy for critical maintenance issues while reducing overall energy consumption by avoiding excessive full-precision processing of all data.
Data Source
AI summary
A method of controlling a vehicle system includes collecting, via an embedded computer assembly (ECA) retrofit on the vehicle, operational data from at least one of a pre-retrofit vehicle component, a retrofit vehicle component, or an adaptive mobility system installed on the vehicle. The method includes transmitting at least a portion of the operational data to a remote training platform. The method includes receiving an update for a model from the remote training platform, the update for the model derived from aggregated operational data of multiple vehicles. The method includes generating a predictive maintenance recommendation, coordinating servicing of the vehicle, or generating a user notification based on the model. The ECA can intercept and suppress communications on a Controller Area Network (CAN) bus from the pre-retrofit component and generate new communications on the CAN bus to at least partially operate at least one pre-retrofit component of the vehicle.