EV Battery Computing Offload for Autonomous Energy Savings
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Solution Overview
Problem
Electric vehicles face inefficiencies in battery energy usage due to power-intensive computations, particularly in autonomous functions, reducing vehicle range and increasing greenhouse gas emissions.
Innovation Solution
A computing system that intelligently allocates computations between onboard and remote systems based on signal strength, data quality, time/distance constraints, and energy savings, offloading computations to a remote computing system when beneficial.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If computations are performed onboard the vehicle, then autonomous functions can be executed, but battery energy consumption increases
Solution Approach 1:
The patent segments computations into two categories: critical real-time computations performed onboard the vehicle and non-critical batch computations offloaded to remote servers. This segmentation allows the vehicle to maintain essential autonomous functions while reducing onboard energy consumption by transferring suitable workloads to external infrastructure.
Solution Approach 2:
The patent introduces a communication network as an intermediary between the vehicle and remote computing resources. This intermediary enables the vehicle to offload computations without direct physical connection, allowing autonomous functions to be supported by external computing power while minimizing onboard energy usage.
2Use of energy by moving object
If computations are offloaded to remote computing system, then onboard energy consumption is reduced, but communication network dependency increases
Solution Approach 1:
The patent implements a dynamic computation offloading strategy that adapts to real-time network conditions. The system continuously monitors communication network availability and adjusts the proportion of computations performed onboard versus offloaded, ensuring reliable operation whether network conditions are favorable or degraded.
Solution Approach 2:
The patent changes the parameter of computation location based on network conditions. When network signal strength is high, more computations are offloaded to reduce energy consumption; when signal strength is low, more computations are performed onboard to maintain reliability, thus optimizing the balance between energy efficiency and system reliability.
3Speed
If more computations are performed onboard, then real-time response is improved, but battery range is reduced
Solution Approach 1:
The patent applies partial action by performing only the necessary minimum computations onboard for real-time autonomous functions, while offloading non-critical batch processing tasks. This partial onboard computation maintains adequate real-time response for safety-critical operations while maximizing battery range by minimizing energy-intensive onboard processing.
Data Source
AI summary
An example computing system may be configured to obtain sensor data associated with the vehicle and determine an estimated energy usage for performing a computation onboard the vehicle using the sensor data. The estimated energy usage is indicative of an estimated amount of energy from one or more batteries of the vehicle for performing the computation using the sensor data. The computing system can determine, based on the estimated energy usage, that performance of the computation using the sensor data is to be offloaded to a remote computing system that is remote from the vehicle. In response to determining that the computation using the sensor data is to be offloaded to the remote computing system, the computing system can generate a sensor data payload indicative of the sensor data and output, over a communication network, the sensor data payload to the remote computing system.