Battery Management System Using Extended Kalman Filter
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Solution Overview
Problem
Hybrid electric vehicle (HEV) battery management systems face challenges in accurately estimating battery parameters such as state of charge, power fade, and capacity fade, especially as cells age, which affects performance and longevity.
Innovation Solution
A battery management system using an equivalent circuit model with multiple RC circuits to estimate battery parameters, incorporating an Extended Kalman Filter for real-time calculations, allowing for precise estimation of battery current limits and power capability by capturing both fast and slow dynamics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a simple battery model is used for parameter estimation, then the device complexity is reduced, but the measurement precision of battery parameters deteriorates
Solution Approach 1:
The patent transforms the battery model from a simple static representation to a dynamic time-varying model where parameters such as resistance and capacitance are allowed to change over time based on operating conditions. This enables the model to adapt to aging effects and varying states of charge, improving measurement precision without requiring a completely complex system architecture.
Solution Approach 2:
The patent implements a feedback mechanism using an extended Kalman filter that continuously compares estimated parameters with actual measurements and adjusts the model parameters accordingly. This feedback loop maintains high measurement precision by correcting estimation errors in real-time while keeping the overall system complexity manageable through efficient algorithm design.
2Measurement precision
If a time-varying battery model is used to capture aging effects, then the measurement precision improves, but the difficulty of detecting and measuring parameters increases
Solution Approach 1:
The patent introduces an extended Kalman filter as an intermediary computational layer that bridges the complex time-varying battery model and the available measurements. This intermediary handles the mathematical complexity of parameter detection by using recursive estimation algorithms, making the measurement process tractable while maintaining high precision in parameter estimation.
3Measurement precision
If high-frequency voltage and current data is processed to capture fast dynamics, then the measurement precision improves, but the loss of information from noise increases
Solution Approach 1:
The patent segments the voltage and current signals into different frequency components, processing high-frequency components to capture fast dynamics while separately handling low-frequency components for slow dynamics. This segmentation allows selective filtering and processing of different frequency ranges, improving fast dynamics detection while managing noise through targeted signal processing techniques.
Solution Approach 2:
The patent employs a dynamic model with separate time constants for fast and slow dynamics, allowing the system to adaptively track different timescales of battery behavior. The extended Kalman filter dynamically adjusts parameter estimates based on the current operating state, enabling accurate capture of fast transient responses while filtering out high-frequency noise through the probabilistic framework.
Data Source
AI summary
A battery management system includes a battery pack and a controller. The controller is configured to receive pack terminal voltage and current data. In response, the controller may estimate battery model parameters in an equivalent circuit model and output state variable values indicative of fast and slow dynamics of the voltage responses. The controller may also output parameter values indicative of feedback gains to compute a current limit in a state feedback structure. The controller may further estimate battery current limits based on the state variable values and the feedback gains to control operation of the battery pack.