Tanks-in-Series Battery Model for High-Current State Estimation
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Solution Overview
Problem
Current battery management systems face challenges in accurately and efficiently controlling lithium-ion battery charging and discharging due to limitations in existing electrochemical models, particularly in predicting battery states and optimizing operations at high current densities, which can lead to computational inefficiencies and errors in estimating overpotentials.
Innovation Solution
The implementation of a Tanks-in-Series modeling approach, which reduces the complexity of the pseudo 2-dimensional (p2D) model by volume-averaging electrolyte conservation equations and approximating interfacial fluxes, allowing for concentration-dependent transport properties without requiring terminal-to-terminal integration, resulting in a fixed-size system of Differential Algebraic Equations (DAEs) and eliminating the need for solving Partial Differential Equations (PDEs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sophisticated electrochemical models (p2D) are used to improve prediction accuracy of battery states, then measurement precision is improved, but device complexity and computational demands increase significantly
Solution Approach 1:
The patent segments the complex p2D electrochemical model into a simplified tanks-in-series model that captures essential battery dynamics. This segmentation reduces the number of differential equations from a full 2D partial differential equation system to a manageable set of ordinary differential equations, maintaining prediction accuracy for state of charge, state of health, and temperature while significantly lowering computational complexity for real-time BMS applications
Solution Approach 2:
The patent transforms the spatially distributed parameters of the p2D model into lumped parameters representing equivalent thermal and electrochemical tanks. By changing the mathematical representation from continuous spatial fields to discrete tank volumes with specific heat capacities, conductivities, and reaction rates, the model achieves real-time computational performance while preserving essential battery behavior characteristics
2Device complexity
If simplified models (SPM) are used to reduce computational complexity, then device complexity is reduced, but measurement precision deteriorates due to neglect of electrolyte phase variations
Solution Approach 1:
The patent introduces an intermediary thermal-electrochemical tank model that bridges the gap between the overly simplified SPM and the computationally intensive p2D model. This intermediary model incorporates electrolyte phase dynamics through equivalent thermal tanks that capture concentration variations and ohmic effects, providing improved predictive accuracy at high current densities while maintaining the computational efficiency needed for real-time control applications
3Device complexity
If polynomial profiles are assumed for electrolyte dynamics to simplify the model, then device complexity is reduced, but manufacturing precision (model accuracy) is compromised due to restrictive assumptions
Solution Approach 1:
The patent employs dynamic tank models that adapt to varying operating conditions rather than assuming fixed polynomial profiles. The equivalent thermal and electrochemical tanks dynamically adjust their parameters based on current, temperature, and state of charge, allowing the model to accurately represent electrolyte dynamics across the full operating range without restrictive polynomial assumptions, thereby maintaining both tractability and accuracy
Data Source
AI summary
In some embodiments, a battery management system is provided. The battery management system comprises a connector for electrically coupling a battery to the battery management system, at least one sensor configured to detect a battery state, a programmable chip configured to control at least one of charging and discharging of the battery, and a controller device. The controller device is configured to receive at least one battery state from the at least one sensor; provide the at least one battery state as input to a tanks-in-series model that represents the battery; and provide at least one output of the tanks-in-series model to the programmable chip for controlling at least one of charging and discharging of the battery.