Battery State Estimation Using Split Local and Remote Computing
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Solution Overview
Problem
Current battery management systems face challenges in accurately estimating battery states, particularly in real-time, especially on compute-limited resources, and in validating models to ensure precision, while also optimizing the useful lifetime and economic value of battery systems.
Innovation Solution
A system and method that includes a computing system with a state estimator, simulation engine, and model generator, which distributes computation between remote and local systems to enhance battery state estimation, uses modular models to select and validate battery models based on application-specific criteria, and enables real-time monitoring and prediction of battery performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If battery state estimation is performed in real-time on compute-limited resources, then responsiveness and operational utility are improved, but measurement precision and reliability deteriorate
Solution Approach 1:
The battery management system is segmented into a remote computing system that performs computationally intensive model generation and validation, and a local computing system that executes real-time state estimation. This segmentation allows the local system to operate with limited computational resources while maintaining precision through algorithms pre-processed by the remote system.
Solution Approach 2:
The remote computing system performs preliminary actions by generating and validating battery models offline before deployment. These pre-validated models are then deployed to the local computing system, enabling real-time estimation without requiring the local system to perform complex model validation computations.
2Reliability
If complex validation processes are applied to ensure model precision, then reliability is improved, but computational resource requirements and system complexity increase
Solution Approach 1:
A simulation engine acts as an intermediary between the model generator and the state estimator. It performs automated validation by simulating battery behavior under various conditions and comparing results against expected performance, thereby ensuring model reliability without requiring complex manual validation processes.
Solution Approach 2:
The validation system performs self-service through automated testing and verification processes. The simulation engine automatically generates test cases, executes simulations, and validates model accuracy without requiring external intervention, reducing both complexity and resource requirements compared to manual validation approaches.
3Measurement precision
If comprehensive battery monitoring and analysis are implemented, then measurement precision and reliability are improved, but use of energy and computational resources increase
Solution Approach 1:
Monitoring functions are segmented between local and remote systems. The local computing system performs essential real-time measurements and state estimation with minimal energy consumption, while the remote computing system handles comprehensive analysis, historical data processing, and detailed reporting that require more computational energy.
Solution Approach 2:
The system implements local quality by optimizing the local computing system for energy-efficient real-time operations. Only essential monitoring functions are executed locally with simplified algorithms tailored for low-power operation, while comprehensive analysis is performed remotely where energy resources are more abundant.
Data Source
AI summary
A system or method for determining a battery state can include receiving a set of sensor measurements; determining the battery state using a state estimator collocated with the battery; and determining parameters used by the state estimator using a second state estimator operating on a processor remote from the battery.