Battery Management Module Phantom Software Architecture
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Solution Overview
Problem
Power transformation systems face challenges in effectively managing battery life and providing reliable power due to infrequent battery usage, leading to improper voltage level detection and potential system interruptions.
Innovation Solution
A battery management module (BMM) utilizing software services from a phantom module, power broker, and low-level board support package (BSP) software, such as A2D and memory read/write, to execute routines for battery life monitoring, including signature profiling and power metering, using volatile and non-volatile memory resources to extend battery life and prevent interruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If battery management is implemented with infrequent usage monitoring, then system complexity is reduced, but voltage level detection accuracy deteriorates leading to improper system operation
Solution Approach 1:
The system performs preliminary battery characterization during manufacturing or initial setup, storing voltage thresholds and capacity data in lookup tables. During operation, the BMM retrieves pre-calculated values based on battery age and usage patterns, avoiding complex real-time calculations while maintaining accurate voltage detection.
Solution Approach 2:
The battery management module uses the battery itself to provide characterization data by monitoring its own voltage, current, and temperature parameters. The system learns battery behavior patterns over time and automatically adjusts management parameters without external intervention, reducing system complexity while improving detection accuracy.
2Measurement precision
If comprehensive battery monitoring routines are executed frequently, then battery life detection accuracy is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic monitoring with variable intervals based on battery state. During normal operation, monitoring occurs at extended intervals to minimize energy consumption. When voltage thresholds or anomaly conditions are detected, the system transitions to more frequent monitoring modes, balancing accuracy requirements with energy conservation.
Solution Approach 2:
The monitoring strategy dynamically changes parameters such as sampling frequency, measurement resolution, and characterization depth based on battery charge level, age, and operational context. Low-power modes use reduced-parameter measurements while critical states trigger full-characterization routines, optimizing the balance between detection accuracy and energy usage.
3Quantity of substance
If battery characterization data is stored in limited memory resources, then device cost is reduced, but ability to handle edge cases deteriorates
Solution Approach 1:
The system extracts and stores only the most critical battery characterization parameters in on-device memory, such as voltage thresholds, capacity milestones, and anomaly flags. Less critical data is processed locally or transferred to external storage when available, reducing memory requirements while maintaining edge case handling for critical scenarios.
Solution Approach 2:
The system uses lightweight, approximate data structures for temporary battery state storage that can be regenerated from measurements. Instead of storing extensive historical data, the system maintains compact summaries that capture essential patterns, accepting some information loss in exchange for reduced memory usage while preserving edge case detection capabilities.
Data Source
AI summary
A battery life monitoring approach for a power transformation powered system. “Signature profiling” and “power metering” may deal with statistically significant edge cases. Relative to product resources, a battery management module (BMM) may use software services from a “phantom module”, “power broker” and other low level board support package (BSP) software, such as A2D, time bases and memory R/W in order to execute routines needed for successful deployment of the product. The memory resources should be volatile and non-volatile memory resources to fulfill the needs of a fully functional power transformation BMM system.