Vehicle Subsystem Preconditioning Controller
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Solution Overview
Problem
Existing vehicle subsystem preconditioning systems fail to effectively adjust to user preferences and optimal operating conditions, particularly when there is a discrepancy between user-specified and learned usage times, and do not adequately account for ambient and forecasted climate conditions.
Innovation Solution
A vehicle subsystem conditioner controller that activates conditioners a predetermined time before user-specified or learned times of day, based on a threshold difference, and adjusts climate conditions using input channels for user and learned times, as well as ambient and forecasted climate data to maintain optimal operating ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the conditioner is activated based on user-specified times only, then the system follows explicit user instructions, but it fails to adapt to actual usage patterns and climate conditions
Solution Approach 1:
The controller performs preliminary actions by learning and storing usage time patterns in advance. The system accumulates historical usage data and generates predicted usage times before the actual usage occurs, allowing the conditioner to be activated proactively based on learned patterns rather than waiting for explicit user input each time.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual usage times and comparing them with learned patterns. The controller adjusts its predictions based on discrepancies between expected and actual usage, refining its learning over time to improve adaptability while maintaining manageable complexity through iterative optimization.
2Reliability
If the conditioner is activated a predetermined time before usage, then the subsystem reaches optimal operating conditions, but energy is consumed unnecessarily when usage patterns change
Solution Approach 1:
The system dynamically adjusts the predetermined activation time based on learned usage patterns and environmental conditions. Rather than using a fixed time offset, the controller modifies the activation schedule in real-time according to actual usage behavior and climate forecasts, ensuring optimal operating conditions are achieved only when necessary and reducing unnecessary energy consumption.
3Ease of operation
If the system accounts for both user-specified and learned times, then it improves user experience, but the control logic becomes more complex
Solution Approach 1:
The system provides self-service by automatically learning and adapting to user preferences without requiring explicit programming or frequent user input. The controller autonomously analyzes usage patterns, predicts future usage times, and adjusts activation schedules independently, improving user experience through personalization while keeping the interface simple and the control logic manageable through automated decision-making.
Data Source
AI summary
A vehicle includes a subsystem conditioner and a controller. The controller is programmed to, in response to a difference between user specified and learned times of day being greater than a threshold, activate the conditioner a predetermined time before each of the times of day. The controller is also programmed to, in response to the difference being less than the threshold, activate the conditioner the predetermined time before the specified time of day but not the learned time of day.