Electronic Fuse Reallocation for Vehicle Load Current Demands
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Solution Overview
Problem
Each vehicle model and variation has a unique arrangement of loads with distinct load current demands, requiring reconfiguration of smart electronic fuses, which is cumbersome and inefficient.
Innovation Solution
A system with a controller circuit that manages an array of electronic fuses, categorizes their health based on load current and temperature, and dynamically reallocates fuse limits within preprogrammed clusters to meet changing load demands, allowing for reallocation of failed or derated fuses to support increased load currents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If smart electronic fuses are reconfigured for each new vehicle iteration, then the load current demands are met accurately, but the reconfiguration process becomes cumbersome and inefficient
Solution Approach 1:
The fuse harness configuration is preprogrammed with cluster definitions and member assignments before vehicle production. This preliminary configuration allows the electronic fuses to be dynamically allocated to meet different load current demands without physical reconfiguration for each vehicle iteration, thus reducing reconfiguration time while maintaining accurate load current matching.
Solution Approach 2:
The system dynamically allocates electronic fuses to clusters based on real-time load current demands and fuse health status. The controller circuit can modify cluster memberships and reallocate fuses between clusters, enabling the system to adapt to different vehicle configurations without manual reconfiguration, thereby resolving the contradiction between precision and time loss.
2Adaptability or versatility
If electronic fuses are dynamically reallocated within clusters, then system adaptability to different vehicle configurations is improved, but the control system complexity increases
Solution Approach 1:
The fuse harness is segmented into multiple preprogrammed clusters, each with defined members. This segmentation allows the controller to manage fuse allocation in discrete, manageable units rather than managing all fuses individually, reducing control complexity while maintaining high adaptability to different vehicle configurations through cluster-based reallocation.
Solution Approach 2:
The controller circuit is designed with multi-functional capabilities to handle various vehicle configurations, fuse health assessments, and dynamic reallocation decisions within a single integrated system. This universal design approach manages complexity by consolidating multiple functions into one controller rather than requiring separate control mechanisms for each function.
3Reliability
If fuse health monitoring and reallocation is implemented, then system reliability is improved, but the monitoring and control overhead increases
Solution Approach 1:
The controller circuit continuously monitors fuse health parameters such as temperature and current draw, and uses this feedback to assess fuse status and make reallocation decisions. This feedback mechanism improves reliability by detecting potential failures early and redistributing loads, while the automated nature of the feedback loop minimizes the operational overhead compared to manual monitoring systems.
Solution Approach 2:
The system performs self-diagnosis and self-reconfiguration by automatically monitoring fuse health and reallocating fuses within clusters without external intervention. This self-service capability improves reliability through continuous health assessment while reducing monitoring overhead by eliminating the need for external diagnostic equipment or manual inspection.
Data Source
AI summary
Systems and methods for reallocation of electronic fuses (EFn). The method includes concurrently receiving, for a plurality (N) of EFn, respective sensor data and comparing the sensor data to respective operating ranges. An EFn that exceeds its operating range is turned off. Wherein a preprogrammed configuration of a fuse harness defines multiple (M) clusters, the method identifies, for each EFn that is within the operating range, whether the EFn is a member of a cluster of the M clusters, and other members of the cluster. The method monitors each cluster member's sensor data, with respect to preprogrammed thresholds and load current demands, to thereby categorize each EFn in each cluster as either healthy, derated, or failed. For failed EFns, a target EF in the same cluster having a reallocation potential is identified, and its fuse limits are modified in accordance with the reallocation potential.