Fractional Depletion Estimation for Battery State Assessment
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Solution Overview
Problem
Industrial batteries, particularly lead-acid batteries used in electric vehicles, face challenges in accurately assessing their state of health and remaining life due to the lack of effective methods for evaluating fractional depletion, leading to inefficient maintenance and premature replacement.
Innovation Solution
A method and system that collect and analyze samples of current discharged during a discharge event, sorting them into bins to create a battery use estimate and determine fractional depletion contributions, allowing for a comprehensive evaluation of battery state and energy state through spectral representation and integration, enabling predictions on remaining life and depletion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional battery monitoring methods are used, then the system is simple and easy to implement, but the accuracy of battery state assessment is insufficient
Solution Approach 1:
The patent segments the continuous current discharge data into discrete bins based on current magnitude ranges. Each bin represents a specific current range (e.g., 0-10A, 10-20A, etc.), allowing the system to analyze different discharge conditions separately. This segmentation enables more precise assessment of battery state by considering the distribution of current across multiple intervals rather than using a single aggregate value.
Solution Approach 2:
The patent collects and analyzes current samples at a high sampling rate, gathering more data points than traditionally necessary. By sorting these samples into multiple bins and analyzing the distribution across all bins, the system achieves superior measurement precision. The excessive sampling and detailed binning provide a comprehensive view of battery discharge patterns, enabling more accurate state assessment.
2Measurement precision
If comprehensive current sampling and binning is performed, then battery health estimation accuracy improves, but computational requirements and processing time increase
Solution Approach 1:
The patent performs preliminary sorting of current samples into bins during the data collection phase, organizing the data before the actual health assessment calculation. By pre-grouping samples according to current magnitude ranges and maintaining running totals for each bin, the system reduces the computational burden during the assessment phase. This preliminary organization enables faster processing when generating the fractional depletion estimate.
Solution Approach 2:
The binning structure automatically accumulates sample counts and current totals for each bin as new samples arrive, without requiring complex recalculations. The system maintains running sums of samples and current values for each bin, allowing the fractional depletion to be updated incrementally as new data comes in, rather than reprocessing all historical data each time.
3Productivity
If fractional depletion estimation is implemented, then maintenance scheduling is optimized, but the complexity of battery management increases
Solution Approach 1:
The patent transforms the traditional approach of monitoring single parameters (like average current or total charge) into a multi-parameter analysis using fractional depletion estimates. By calculating the fraction of battery capacity depleted in each current bin and aggregating these fractions, the system derives a comprehensive health metric that captures the non-linear effects of different discharge rates on battery aging. This parameter transformation enables more intelligent maintenance scheduling.
Solution Approach 2:
The system continuously updates the fractional depletion estimate as new current samples are collected and binned. This real-time feedback mechanism allows the battery management system to dynamically adjust maintenance predictions based on actual discharge patterns. The accumulated fractional depletion data provides ongoing feedback about battery wear, enabling proactive maintenance scheduling before failure occurs.
Data Source
AI summary
A system is provided that evaluates an energy state. The system comprises a sensor, bins, and a processor. The sensor is communicatively coupled to at least one cell that is powering an electric load. The sensor is operative to collect samples, each representing a measure of current discharged from the at least one cell during a discharge event. The bins are stored in a memory, and are accessible by the processor. The processor is programmed to sort the collected samples into bins based upon sample value. The processor is also programmed to create a use estimate based upon samples sorted into their corresponding bins. Yet further, the processor is programmed to determine a fractional depletion for each bin, each fractional depletion being a quotient that is computed by dividing an expected value for that bin by the use estimate for that bin.