Vehicle Power Management Using Trajectory-Based Energy Control
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Solution Overview
Problem
Conventional vehicle power management systems in autonomous, semi-autonomous, and manually-driven vehicles are inefficient, leading to wasted energy due to sub-optimal utilization of resources and reliance on rudimentary rules that do not account for the vehicle's immediate trajectory or route information.
Innovation Solution
The implementation of an efficient vehicle management module that utilizes trajectory and route information to optimize power management by determining when to engage or disengage engines, motors, and braking systems, using semantic maps and historical power consumption data to predict and adjust power settings based on the vehicle's intended path and environmental factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If conventional power management systems use rudimentary rules without trajectory information, then the system complexity is low, but energy efficiency deteriorates due to sub-optimal resource utilization
Solution Approach 1:
The system performs preliminary actions by determining the vehicle's trajectory in advance and using this information to proactively optimize power management settings. The power management module anticipates future power needs based on the trajectory, allowing it to pre-position power resources and optimize engine/motor engagement strategies before actual power demands occur, thereby improving energy efficiency without requiring complex real-time responses
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual power consumption against predicted consumption based on trajectory data. This feedback loop allows the power management module to adjust its strategies in real-time, refining its predictions and optimizations. The feedback from actual vehicle operation informs future trajectory-based power management decisions, creating a self-improving system that balances complexity with energy efficiency
2Use of energy by moving object
If the system uses trajectory and route information to optimize power management, then energy efficiency improves, but the device complexity increases due to additional sensors and processing requirements
Solution Approach 1:
The power management module serves multiple functions: it determines vehicle trajectory, predicts power consumption, optimizes engine/motor engagement, and controls braking systems. By consolidating these diverse functions into a single multi-functional module that leverages trajectory information, the system achieves significant fuel savings without proportionally increasing overall device complexity. The same trajectory data serves multiple optimization purposes simultaneously
Solution Approach 2:
The system utilizes existing vehicle data infrastructure and trajectory information that is already being collected for navigation and control purposes. Rather than requiring entirely new sensing and processing systems, the power management module serves itself by leveraging available trajectory data and vehicle state information to optimize its own control strategies, thereby reducing the incremental complexity burden
3Loss of energy
If conventional systems do not account for immediate trajectory, then the control system is simple, but energy waste increases due to missed regenerative braking opportunities
Solution Approach 1:
The power management module performs preliminary analysis of the determined trajectory to identify upcoming conditions that would benefit from regenerative braking, such as upcoming declines or traffic signals. By anticipating these opportunities in advance based on trajectory information, the system can proactively manage battery charge levels and timing of regenerative braking events to maximize energy recovery, preventing energy waste before it occurs
Solution Approach 2:
The system continuously monitors actual braking events and energy recovery against the predicted trajectory-based opportunities. This feedback allows the control system to learn from actual outcomes and refine its trajectory interpretation and regenerative braking strategies. The feedback loop enables the system to adapt to real-world variations while maintaining the core trajectory-based optimization approach, managing complexity through iterative improvement
Data Source
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AI summary
Systems, methods, and non-transitory computer-readable media can determine a trajectory for a vehicle, the trajectory associated with a predicted path to be traveled by the vehicle. A power optimization plan is generated for the vehicle based on the trajectory. One or more power management settings are modified based on the power optimization plan.