Vehicle Battery Controller Using Reduced Order Model
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Solution Overview
Problem
Existing battery management systems face challenges in accurately and efficiently predicting battery performance metrics, such as voltage and heat generation rate, due to computationally intensive models and trade-offs between accuracy and processing time, making real-time predictions difficult for electric and hybrid vehicles.
Innovation Solution
A modularized battery management system that employs a reduced order model with components like solid diffusion, electrolyte transport, and electrolyte and solid potential modules, using numerical methods like finite difference to improve prediction accuracy and efficiency, particularly for lithium-ion batteries, by solving the solid diffusion equation and accounting for electrolyte activity coefficients and open circuit voltage hysteresis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computationally intensive models are used to predict battery performance metrics, then prediction accuracy is improved, but processing time and computational resource utilization increase
Solution Approach 1:
The battery management system is divided into multiple independent modules including solid diffusion module, electrolyte transport module, electrolyte potential module, solid potential module, and battery controller module. Each module handles specific calculations separately, allowing parallel processing and reducing overall computation time while maintaining prediction accuracy through comprehensive coverage of electrochemical phenomena.
Solution Approach 2:
The system uses reduced order models that change the mathematical parameters from full partial differential equation solutions to simplified formulations. This includes using algebraic equations instead of complex differential equations for certain calculations, significantly reducing computational intensity while preserving essential battery behavior predictions for voltage and heat generation rate.
2Measurement precision
If computationally intensive models are used to predict battery performance metrics, then prediction accuracy is improved, but computational resource utilization increases
Solution Approach 1:
By segmenting the battery management system into independent computational modules (solid diffusion, electrolyte transport, electrolyte potential, solid potential), the system distributes computational load across multiple specialized components rather than requiring one complex calculation, reducing peak computational resource utilization while maintaining comprehensive prediction accuracy.
Solution Approach 2:
The system employs reduced order models that use simpler mathematical formulations instead of computationally expensive full-order models. These simplified models consume fewer computational resources (less CPU power, memory, and energy) while still providing sufficient prediction accuracy for real-time battery management applications.
3Productivity
If real-time predictions are enabled, then battery management efficiency is improved, but model complexity increases
Solution Approach 1:
The complex electrochemical model is segmented into four independent modules (solid diffusion, electrolyte transport, electrolyte potential, solid potential) that can be executed in parallel or sequence. This modular structure enables real-time predictions by breaking down the complex calculation into manageable segments that can be processed efficiently, reducing overall computation time while maintaining model accuracy.
Solution Approach 2:
The system transitions from full-order models to reduced order models by changing the mathematical parameters and formulations. This simplification reduces model complexity and computational burden while preserving the essential electrochemical relationships needed for accurate real-time prediction of battery voltage and heat generation rate.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides improved accuracy in predicting voltage and heat generation rates with reduced computational resource utilization, enabling real-time battery management and simulation of driving cycles, thus enhancing charging capabilities and battery design processes.
Implementation Method 1
The solid diffusion component can update, based on the value of the current and the profile, a solid concentration model of the battery
Implementation Method 2
The electrolyte transport component can update, based on the value of the current and the profile, an electrolyte concentration model of the battery
Data Source
AI summary
A vehicle battery controller based on a reduced order model is provided. The controller identifies a value of a current output by the battery that supplies power to the vehicle. The controller provides the value of the current to a solid diffusion component, an electrolyte transport component, and a electrolyte and solid potential component. The solid diffusion component can update a solid concentration model of the battery. The electrolyte transport component can update an electrolyte concentration model of the battery. The electrolyte and solid potential component can update an electrolyte potential model and a solid potential model of the battery. The controller component can determine a value of a voltage of the battery and a value of a heat generation rate of the battery. The controller component can generate a command to manage a performance of the battery.