Vehicle Battery Control Using Differential Physics 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, and are computationally expensive.
Innovation Solution
Implementing a differential physics network integrated with the battery management system to execute real-time optimization control, using a centralized multi-factor control system that optimizes battery parameters via a differential physics computation to determine target control values for voltage, current, and temperature management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional battery management systems are used, then device complexity is reduced, but battery degradation cannot be effectively minimized due to lack of centralized multi-factor optimization
Solution Approach 1:
The patent merges multiple control functions (voltage management, thermal management, current management) into a single centralized electronic controller that executes differential physics computations. This integration enables simultaneous multi-factor optimization of battery parameters while extending battery lifetime by up to 30%, resolving the contradiction between improved reliability and increased device complexity.
Solution Approach 2:
The patent introduces a differential physics network as an intermediary computational layer between sensor inputs and control outputs. This intermediary enables sophisticated multi-factor optimization without requiring complex hardware modifications, allowing the system to minimize battery degradation through advanced algorithms while maintaining manageable device complexity.
2Reliability
If real-time optimization control is implemented via differential physics network, then battery degradation is reduced, but computational cost increases
Solution Approach 1:
The patent pre-computes and stores the differential physics network model and optimization algorithms in memory before runtime. By preparing the computational framework in advance, the system can execute real-time optimization with reduced computational burden during actual battery operation, minimizing degradation while controlling energy consumption.
Solution Approach 2:
The patent dynamically adjusts computational parameters based on battery state conditions. The differential physics network adapts its computation intensity and optimization frequency according to real-time battery parameters, enabling effective degradation reduction during critical periods while reducing computational energy consumption during stable operating conditions.
3Duration of action of stationary object
If centralized multi-factor control system is implemented, then battery lifetime is extended, but system complexity increases
Solution Approach 1:
The patent designs the electronic controller to perform multiple functions through a single unified architecture. The same controller executes voltage management, thermal management, current management, and differential physics computations, eliminating the need for separate control systems for each function and extending battery lifetime without proportionally increasing overall system complexity.
Solution Approach 2:
The differential physics network enables the battery management system to autonomously optimize its own operation parameters based on real-time sensor data. The system self-adjusts control strategies without external intervention, extending battery lifetime through autonomous multi-factor optimization while maintaining manageable system complexity through self-regulation.
Data Source
AI summary
A vehicle battery management system includes a battery management circuitry and an electronic controller. The battery management circuitry includes a voltage management component, a thermal management component and a current management component. The electronic controller is configured to execute a differential physics computation via a differential physics network using detected battery state values to determine target control values for the battery management circuitry. The electronic controller is further programmed to control the battery management circuitry in accordance with the target control values.