Differential Physics Network for Battery SOH Optimization
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Solution Overview
Problem
Conventional battery management systems lack a centralized controller for simultaneous multi-factor optimization, making it difficult to reduce state-of-health (SOH) degradation in vehicle batteries, which affects their lifetime.
Innovation Solution
Implementing a differential physics network integrated with the battery management system (BMS) to perform real-time optimization using backpropagation, determining target control parameters by connecting various battery degradation factors through directional relationships and partial derivatives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional battery management systems are used without centralized multi-factor optimization, then the system complexity remains low, but the battery degradation (SOH loss) cannot be effectively reduced
Solution Approach 1:
The patent merges multiple degradation factors (temperature, charge rate, depth of discharge, voltage) into a single centralized differential physics network that performs multi-factor optimization simultaneously. This consolidation allows comprehensive battery lifetime optimization without proportionally increasing system complexity, as the network integrates all factors into one unified control architecture.
Solution Approach 2:
The differential physics network acts as an intermediary layer between sensor inputs and control outputs. It processes multiple degradation factors through mathematical models (partial derivatives, chain rule) to generate optimized control parameters, mediating between raw battery state data and actionable control decisions without requiring direct complex interconnections between all components.
2Reliability
If real-time multi-factor optimization is implemented to reduce battery degradation, then the battery lifetime is extended, but the computational requirements and processing complexity increase
Solution Approach 1:
The patent replaces complex mechanical control systems with a mathematical computation-based differential physics network. By using analytical solutions involving partial derivatives and the chain rule, the system performs real-time optimization through computational mathematics rather than iterative trial-and-error methods, reducing the difficulty of real-time processing while maintaining optimization effectiveness.
Solution Approach 2:
The differential physics network pre-calculates the relationships between degradation factors and control parameters using mathematical models before real-time operation. The chain rule and partial derivatives are established in advance, allowing the system to quickly compute optimized control parameters during real-time operation without performing full optimization calculations from scratch, thus reducing real-time computational complexity.
3Reliability
If additional sensors and controllers are added to achieve centralized multi-factor optimization, then the battery degradation control is improved, but the system cost and complexity increase
Solution Approach 1:
The differential physics network is designed as a universal control architecture that can process multiple degradation factors (temperature, charge rate, depth of discharge, voltage) and generate optimized control parameters for various battery operations (charging, discharging, thermal management). This multi-functional capability allows the system to achieve comprehensive degradation control without adding separate dedicated sensors and controllers for each function, as one unified network handles all optimization tasks.
Data Source
AI summary
A non-transitory differential physics network disposed upon a non-transitory computer readable storage medium and executable by a computer is provided. The non-transitory differential physics network includes an input layer, an output layer and an intermediate layer. First input values related to a first set of detected battery state values is input to the input layer. A total capacity loss value is output from the output layer. Second input values related to a second set of detected battery state values is input to the intermediate layer. The differential physics network utilizes differential physics to determine the directional relationship of the first and second sets of detected battery state values to output the output layer. The differential physics network performs optimization using backpropagation to determine target input values for the input layer.