Battery Model for Accurate Power Prediction
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Solution Overview
Problem
Existing methods for predicting battery performance in electric and hybrid vehicles do not accurately account for maximum charging and minimum discharging voltage limits, leading to unrealistic performance values and inefficient energy management, and fail to distinguish between voltage-limited and current-limited cases, resulting in potential battery overload or undersizing.
Innovation Solution
A method that differentiates between four cases: charge with voltage limitation, charge with current limitation, discharge with voltage limitation, and discharge with current limitation, using a battery model that includes passive electrical components and takes into account the battery's load history and initial conditions, ensuring that the model provides realistic maximum power predictions by adhering to permissible operating limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If performance data is stored in characteristic diagrams for energy management, then energy management capability is improved, but the values do not reflect dynamic battery behavior resulting in performance values that are too low or too high
Solution Approach 1:
The patent transitions from static characteristic diagrams to a dynamic equivalent circuit model that continuously adapts to changing battery conditions. The model includes time-dependent components (resistors, capacitors, inductors) that capture the dynamic behavior of the battery, allowing performance predictions that accurately reflect real-time battery state rather than relying on pre-stored static values.
2Reliability
If the number of forecast periods is increased to improve prediction coverage, then the prediction capability is improved, but the storage space required increases directly proportionally
Solution Approach 1:
Instead of storing actual performance data for multiple forecast periods, the patent creates a mathematical model (equivalent circuit) that generates predictions on-demand. The model parameters are stored once, and the system can compute predictions for any forecast period as needed, eliminating the need for extensive pre-computed data storage while maintaining comprehensive prediction coverage.
3Ease of operation
If a simple prediction method is used to reduce computational complexity, then ease of operation is improved, but the method cannot distinguish between voltage-limited and current-limited cases leading to unrealistic performance values
Solution Approach 1:
The patent employs an equivalent circuit model where parameters (resistance, capacitance, inductance values) automatically change based on battery state (temperature, charge condition, pulse duration). This allows the model to adapt its behavior to different operating conditions including voltage-limited and current-limited cases, providing accurate predictions without requiring complex manual intervention while maintaining computational efficiency through standardized circuit analysis methods.
Data Source
Figure 1
AI summary
The invention relates to a method for determining and/or predicting the maximum performance capacity of a battery using a model of the battery, based on an electrical schematic representation that predicts the maximum performance capacity of the battery. The maximum power of the battery is thereby prognosticated for a defined prognosis period and for the different operating modes with respect to charging or discharging operation, considering the maximum permissible operating voltage and the maximum permissible operating current.