Cloud Offloading for Vehicle Battery SOC Estimation
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Solution Overview
Problem
Modern hybrid and electric vehicles face inaccuracies in state of charge (SOC) estimation due to noise in current measurement signals, sensor biases, and battery aging, which require significant computational resources and memory, especially as the number of battery cells increases, leading to increased vehicle processing costs.
Innovation Solution
Offloading the parameter identification task for SOC estimation to a cloud-based computing service, where battery measurements are sent for processing, allowing the vehicle to receive model parameters for state observation, reducing the computational and memory demands on the vehicle's systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If parameter identification for SOC estimation is performed using vehicle-based computing resources, then SOC estimation accuracy is improved, but vehicle processing costs and computational resource requirements increase
Solution Approach 1:
The patent introduces cloud computing services as an intermediary between the vehicle's battery management system and the parameter identification algorithms. The vehicle controller sends battery measurements to external cloud computing resources, which perform the computationally intensive parameter identification tasks and return results to the vehicle. This mediator approach transfers processing demands from the vehicle to external infrastructure.
Solution Approach 2:
The patent moves the parameter identification computation from the traditional vehicle-based dimension to an external cloud-based dimension. By separating the state observation function (remaining in the vehicle) from the parameter identification function (moved to cloud), the system creates a new architectural dimension that distributes computational loads appropriately across different locations and capabilities.
2Quantity of substance
If the number of battery cells is increased to provide more energy, then energy storage capacity is improved, but computational resources and memory requirements for SOC estimation increase
Solution Approach 1:
The cloud computing service acts as an intermediary that handles the computationally intensive parameter identification tasks for large battery packs. Instead of increasing vehicle computing resources proportionally with battery size, the system offloads these tasks to external cloud infrastructure, maintaining efficient energy usage in the vehicle while supporting larger battery configurations.
3Measurement precision
If computational resources are upgraded to improve SOC estimation accuracy, then measurement precision is improved, but vehicle cost increases
Solution Approach 1:
The patent uses cloud computing services as an external intermediary to provide advanced SOC estimation capabilities without requiring expensive onboard hardware upgrades. The vehicle controller communicates with remote computing resources that perform parameter identification, enabling accurate SOC estimation while avoiding the need for costly vehicle manufacturing modifications.
Solution Approach 2:
Instead of duplicating expensive computing resources in every vehicle, the system uses a centralized cloud-based computing resource that serves multiple vehicles. This copying approach allows the sophisticated parameter identification algorithms to be executed once in the cloud and the results shared with multiple vehicle instances, reducing per-vehicle costs.
Data Source
AI summary
A vehicle may include battery cells of a battery and at least one controller configured to control the vehicle based on a state observation associated with the battery according to battery model parameters for the cells received from a computing device external to the vehicle and responsive to measurements relating to a battery model of the cells sent to the computing device.