Vehicle Solar Panel Orientation Learning for Parking Power Gain
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Solution Overview
Problem
Solar power generation systems on vehicles face inefficiencies due to unknown or customizable solar panel layouts, which hinder optimal orientation for maximum power harvesting, especially when geometric analysis is not feasible.
Innovation Solution
A self-learning procedure that measures and records power output at various azimuth angles to create calibration curves, allowing the vehicle to autonomously adjust its orientation for optimal solar energy generation without relying on geometric details of the panel layout or vehicle surfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a preprogrammed control system is used to determine optimal vehicle orientation, then the system can efficiently calculate optimal orientation, but it requires complete geometric details about solar panel layout and vehicle surfaces which may not be available for customizable or aftermarket installations
Solution Approach 1:
The system performs self-calibration by automatically measuring power output at different azimuth angles and generating its own calibration curves without requiring external geometric information. The controller learns the optimal orientation through self-testing and stores this information for future use, making the system self-sufficient and adaptable to any panel configuration.
Solution Approach 2:
The system changes operational parameters by varying the vehicle's azimuth angle during calibration and uses these parameter changes to map power output characteristics. By measuring power output across a range of azimuth angles and storing these as calibration curves, the system creates a parameter-based model that replaces complex geometric analysis.
2Measurement precision
If geometric analysis is performed to determine optimal orientation, then precise orientation control can be achieved, but the system becomes unable to adapt to customizable or aftermarket solar panel layouts where geometric details are unknown
Solution Approach 1:
The system uses feedback from actual power output measurements to build calibration curves that relate azimuth angle to power generation. This empirical feedback loop allows the system to learn the specific characteristics of any solar panel configuration and adapt its orientation control strategy accordingly, rather than relying on predetermined geometric models.
Solution Approach 2:
The system performs preliminary calibration measurements during a self-learning sequence before normal operation begins. By collecting power output data across various azimuth angles in advance and storing calibration curves, the system prepares the necessary adaptation information upfront, enabling precise orientation control for the specific panel configuration without requiring geometric analysis during actual operation.
3Power
If the vehicle orientation is adjusted frequently to track the sun, then maximum power output can be achieved, but additional time and energy are consumed for repositioning the vehicle
Solution Approach 1:
The system performs calibration at periodic intervals or under specific conditions (e.g., when parking duration exceeds a threshold) rather than continuously adjusting orientation. The stored calibration curves enable the system to determine optimal orientation quickly based on current solar position, reducing the frequency of physical repositioning while maintaining high power capture efficiency.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables vehicles to achieve up to 90% of maximum power output during parking events by determining the optimal azimuth orientation based on actual power measurements, regardless of the solar panel configuration, thereby maximizing solar energy harvesting.
Implementation Method 1
Solar power generation systems using solar panels (e.g., photovoltaic arrays)
Data Source
AI summary
Solar power generation panels added on a transportation vehicle have a layout wherein power output of the panels varies according to an azimuth orientation of the vehicle. A controller includes a database of calibration curves relating an expected power output to a respective range of the azimuth orientation according to different solar altitude angles. A self-learning sequence is performed which (a) collects a magnitude of power output while the vehicle traverses the respective range of the azimuth orientation, (b) identifies a current solar altitude angle, and (c) stores a resulting calibration curve. A parking sequence comprises (a) selecting a calibration curve according to solar altitude angle, (b) determining a target vehicle azimuth angle which optimizes a cumulative power output based on the calibration curve and solar azimuth, and (d) initiating a movement of the vehicle to orient it at the target vehicle azimuth angle.