Autonomous Vehicle Routing Based on Battery State and Power Demand
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Solution Overview
Problem
Conventional autonomous driving vehicles do not account for power consumption when navigating, leading to reduced operational range due to high electrical demand from sensors and computing power, which can deplete battery energy reserves.
Innovation Solution
A dynamic routing algorithm utilizing artificial intelligence and improved memory management in an on-board computer system that monitors battery state and environmental factors to optimize navigation routes, adjust speed, and conserve energy by selecting alternative routes that minimize power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If sensors and computing power are used for autonomous navigation, then navigation capability is improved, but power consumption increases
Solution Approach 1:
The system dynamically adjusts navigation strategies based on real-time battery state and environmental conditions. The routing algorithm continuously optimizes the path to minimize power consumption while maintaining autonomous navigation capability, adapting to changing conditions during operation.
Solution Approach 2:
The system changes operational parameters such as speed, acceleration, and route selection based on battery state of charge and environmental factors. By adjusting these parameters dynamically, the system maintains autonomous navigation while optimizing power consumption to extend operational range.
2Measurement precision
If high computing power is used for processing sensor data, then navigation accuracy is improved, but battery energy is depleted faster
Solution Approach 1:
The system applies computing power selectively based on navigation needs. Rather than continuously using maximum computing resources, the system processes sensor data at appropriate levels of detail, using higher precision only when necessary for safe navigation decisions, thereby conserving battery energy while maintaining adequate navigation accuracy.
Solution Approach 2:
The system performs preliminary assessment of navigation requirements and battery state before executing high-computing operations. By pre-planning routes and anticipating complex navigation scenarios, the system can prepare processing requirements in advance, reducing the need for intensive real-time computing and extending operational range.
3Loss of time
If the vehicle follows the fastest route, then travel time is reduced, but power consumption increases
Solution Approach 1:
The routing algorithm dynamically optimizes the balance between travel time and power consumption based on real-time battery state. When battery charge is high, the system may select faster routes; when charge is low, it automatically prioritizes energy-efficient routes, continuously adapting to maintain optimal operation.
Solution Approach 2:
The system changes route selection parameters based on battery state of charge and environmental conditions. By adjusting the optimization criteria between speed and energy efficiency, the system can extend operational range while still providing acceptable travel times, adapting to the specific situation at hand.
Data Source
AI summary
A system and method for navigating an autonomous driving vehicle (ADV) that utilizes an-onboard computer and/or one or more ADV control system nodes in an ADV network platform. The on-board computer receives battery monitoring and management data concerning a battery stack. The on-board computer, utilizing a battery management system, determines the current state of charge (SOC) and other information concerning the battery stack and determines if the estimated total amount of electrical power required to navigate an ADV along a generated route to reach the predetermined destination is available. In response to determining that the ADV cannot reach the predetermined destination, the on-board computer automatically initiates a dynamic routing algorithm, which utilizes artificial intelligence, to generate alternative routes in an effort to find a route that the ADV can navigate to reach the destination utilizing the current state of charge (SOC) of the battery stack.