Vehicle Powertrain Control Using Perception-Guided Power Distribution
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Solution Overview
Problem
Conventional powertrain systems in vehicles rely on rule-based systems for power distribution, which are inefficient as they cannot account for various scenarios, environments, and situations, leading to suboptimal power usage.
Innovation Solution
Implementing a machine learning model that uses perception data to determine power distribution among different power sources, allowing for efficient power allocation based on real-time vehicle conditions and scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If rule-based systems are used for power distribution, then the system structure is simple and easy to implement, but the power usage efficiency is suboptimal and cannot adapt to various scenarios
Solution Approach 1:
The patent replaces the traditional rule-based control system with a machine learning-based control system. The machine learning model processes perception data from multiple sensors and generates optimized power distribution strategies, substituting the rigid rule-based approach with an adaptive intelligent system that can handle complex scenarios and improve power usage efficiency.
Solution Approach 2:
The patent changes the control parameters from fixed rule-based decisions to dynamic machine learning model outputs. The machine learning model continuously adjusts power distribution parameters based on real-time perception data, enabling adaptive optimization of power usage across different driving scenarios while maintaining system implementability through standardized model interfaces.
2Use of energy by moving object
If machine learning model is implemented for power distribution, then power usage efficiency is improved and fuel storage needs are reduced, but the system complexity increases
Solution Approach 1:
The patent implements a universal machine learning controller that handles multiple functions: processing perception data from various sensors, determining power distribution strategies, and optimizing power usage across different power sources. This multi-functional approach consolidates complexity into a single intelligent controller rather than distributing it across multiple specialized components.
Solution Approach 2:
The machine learning model acts as an intermediary between the perception system and the power distribution system. It processes raw perception data and translates it into optimized power distribution commands, mediating the complexity between sensor inputs and actuator outputs while improving overall power usage efficiency.
3Device complexity
If conventional powertrain systems are used, then the system structure is simple, but the overall power consumption is high and fuel storage needs are large
Solution Approach 1:
The patent introduces dynamic power distribution optimization through the machine learning model, which continuously adapts power allocation strategies based on real-time perception data and driving scenarios. This dynamic approach optimizes power consumption across different operating conditions, reducing overall energy loss compared to static conventional systems.
Solution Approach 2:
The machine learning model performs preliminary analysis of perception data and predicts optimal power distribution strategies before executing power allocation. This preliminary processing enables proactive optimization of power consumption, allowing the system to prepare efficient power distribution plans in advance based on anticipated driving scenarios.
Data Source
AI summary
A powertrain system may determine a power distribution for one or more power sources of a vehicle. The powertrain system may be coupled to a perception system that may provide perception data indicating a scenario, situation, or environment that has been encountered by the vehicle. The powertrain system may include machine learning model that may generate the power distribution based on one or more of the perception data and a power request.