Battery Management Apparatus Adaptive Sensitivity Estimation
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Solution Overview
Problem
Current Battery Management Systems (BMS) face challenges in accurately measuring battery state due to varying voltage and current data, leading to increased algorithm complexity, power consumption, and reduced efficiency, as they struggle to balance estimation accuracy with power usage.
Innovation Solution
A battery management apparatus and method that determines the sensitivity of the battery state based on sensed and previous battery information, adjusting execution parameters such as algorithm execution period and optimization processes to optimize estimation accuracy and power consumption dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If algorithm complexity is increased to improve measurement accuracy of battery state, then battery state estimation accuracy is improved, but power consumption is increased
Solution Approach 1:
The patent applies dynamics by making the algorithm execution frequency adaptive rather than fixed. The processor dynamically adjusts the execution frequency of battery state estimation algorithms based on real-time sensitivity analysis of battery parameters. When battery sensitivity is high (rapid changes in voltage, current, or temperature), the algorithm executes more frequently to maintain accuracy. When sensitivity is low, execution frequency decreases to conserve power, thus resolving the contradiction between measurement precision and power consumption.
Solution Approach 2:
The patent changes the parameter of algorithm execution frequency based on battery sensitivity conditions. By monitoring parameters such as voltage change rate, current change rate, and temperature change rate, the system adjusts the execution frequency parameter of the estimation algorithm. This parameter adaptation allows the system to maintain high measurement accuracy when needed while reducing power consumption during stable battery conditions.
2Measurement precision
If the number of modules for SOC calculation is increased to improve battery state estimation accuracy, then battery state estimation accuracy is improved, but system complexity is increased
Solution Approach 1:
The patent segments the battery management system into multiple independent estimation modules, each responsible for different aspects of state estimation (e.g., different battery cells or different estimation methods). The processor selectively activates only the necessary segments based on battery sensitivity conditions. This segmentation allows the system to maintain high estimation accuracy when needed while reducing computational complexity by deactivating unnecessary segments during stable conditions.
Solution Approach 2:
The patent applies partial action by activating only the necessary number of estimation modules based on current battery conditions. Instead of running all estimation modules continuously, the system activates additional modules only when battery sensitivity requires higher accuracy. This partial activation reduces system complexity and computational burden during normal operations while maintaining the capability to achieve high accuracy when needed.
3Measurement precision
If algorithm execution frequency is increased to improve battery state estimation accuracy, then battery state estimation accuracy is improved, but power consumption is increased
Solution Approach 1:
The patent implements periodic action with variable periods for algorithm execution. Instead of continuous or fixed-frequency execution, the system performs battery state estimation at periodic intervals that are dynamically adjusted based on battery sensitivity. When battery parameters show rapid changes (high sensitivity), the execution period shortens to maintain accuracy. When parameters are stable (low sensitivity), the execution period lengthens to reduce power consumption, thus resolving the contradiction between measurement precision and energy usage.
Solution Approach 2:
The patent employs feedback mechanisms where the processor continuously monitors battery parameters (voltage, current, temperature) and their rates of change, then uses this feedback to adjust the algorithm execution frequency. The sensitivity analysis results feed back into the scheduling decision, creating a closed-loop system that adapts execution frequency to actual battery conditions. This feedback-driven approach ensures high accuracy when battery conditions require it while minimizing power consumption during stable periods.
Data Source
AI summary
Provided are battery management apparatuses and methods. The battery management apparatus includes a sensitivity determiner configured to determine sensitivity of a battery state based on sensed battery information and previous battery state information, and an execution parameter adjuster configured to adjust a parameter for estimating the battery state based on the determined sensitivity of the battery state.