Battery Management System Adaptive State-of-Charge Estimation
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Solution Overview
Problem
Existing battery management systems face challenges in efficiently estimating the state-of-charge of batteries while minimizing computational burden, as capacitance and internal resistance parameters change slowly and require less frequent updates, but frequent estimation degrades the accuracy of state-of-charge estimation.
Innovation Solution
Implementing an adaptive estimation frequency for capacitance and internal resistance, triggered by specific thresholds, allowing for precise estimation only when necessary, and using Kalman filters to predict and correct estimates, reducing computational load while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If capacitance estimation is performed at high frequency, then estimation precision is improved, but computational burden increases
Solution Approach 1:
The patent implements dynamic adjustment of capacitance estimation frequency based on the actual variation rate of capacitance. When capacitance changes rapidly, estimation frequency increases; when changes slowly, frequency decreases. This dynamic adaptation resolves the contradiction by matching computational effort to actual need, improving precision when necessary while reducing burden during stable periods.
Solution Approach 2:
The patent changes the estimation frequency parameter adaptively based on observed capacitance variation. By monitoring how quickly capacitance changes and adjusting the estimation interval accordingly, the system optimizes the balance between precision and computational load, performing frequent estimates only when capacitance dynamics demand it.
2Device complexity
If capacitance estimation frequency is reduced, then computational burden is decreased, but estimation precision deteriorates
Solution Approach 1:
The system dynamically adjusts estimation frequency based on capacitance variation rate. When capacitance is stable, frequency is reduced to minimize computational burden. When capacitance changes rapidly, frequency increases automatically to maintain precision, thus resolving the trade-off between computational efficiency and estimation accuracy.
Solution Approach 2:
The patent employs feedback mechanisms to monitor capacitance variation and adjust estimation frequency accordingly. The system continuously observes capacitance changes and uses this information to determine appropriate estimation intervals, ensuring precision is maintained only when necessary while reducing computational load during stable operation.
3Reliability
If capacitance is estimated at every instant, then up-to-date estimation is achieved, but computational resources are excessively consumed
Solution Approach 1:
The patent implements periodic capacitance estimation with variable intervals rather than continuous estimation at every instant. The estimation frequency is adjusted periodically based on observed capacitance variation patterns, achieving up-to-date estimates when needed while consuming computational resources efficiently during stable periods.
Solution Approach 2:
The system changes the estimation interval parameter dynamically based on capacitance variation rate. By adapting the time parameter between estimations to actual system conditions, the patent maintains estimation currency when capacitance changes rapidly while improving computational efficiency during stable operation periods.
4Productivity
If threshold-based triggering is implemented, then unnecessary estimations are avoided, but estimation delay may occur
Solution Approach 1:
The patent adjusts threshold parameters dynamically based on operating conditions and capacitance variation patterns. By adapting threshold sensitivity to current system state, the system avoids unnecessary estimations during stable periods while ensuring timely detection of significant capacitance changes, thus balancing computational efficiency with response time.
Solution Approach 2:
The triggering mechanism is made dynamic with adjustable thresholds based on operating conditions. When capacitance varies rapidly, thresholds are lowered to reduce delay; when stable, thresholds are raised to improve efficiency. This dynamic adaptation resolves the contradiction between avoiding unnecessary computations and maintaining timely response.
Data Source
AI summary
A method for estimating state-of-charge of a battery cell includes suppressing full execution of a capacitance-estimation algorithm for as long as a parameter does not exceed a certain threshold. The parameter is either a measured voltage value across the cell's terminals, the cell's estimated state-of-charge, or an amount of charge passing in or out of the cell during some interval. A battery-management system triggers full execution when the parameter falls below the threshold, or, if the parameter is the amount of charge passing, when it rises above the threshold.