Overpredicted SOC Battery Management via Lag Filter
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Solution Overview
Problem
Existing battery management systems face challenges in accurately predicting the state of charge (SOC) of rechargeable batteries, leading to potential thermal runaway and inefficiencies in electric vehicle powertrains due to underestimation of SOC, which can result in inadequate derating of current draw and increased operating temperatures.
Innovation Solution
A multi-cell rechargeable energy storage system (RESS) with an electronic controller that uses a first-order lag filter algorithm to generate an overpredicted open circuit voltage (OCV) and corresponding SOC value, calibrated to advance slower in discharge and faster in charge, allowing for earlier derating of current draw and thermal management to prevent thermal runaway.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional battery management systems use standard SOC prediction methods, then the system complexity remains low, but the SOC prediction accuracy deteriorates leading to thermal runaway risks
Solution Approach 1:
The system pre-calculates and stores OCV-SOC lookup tables before operation, and pre-determines filter coefficients based on historical data. This preliminary preparation enables real-time overprediction without complex calculations during critical moments, resolving the contradiction between accuracy and computational complexity
Solution Approach 2:
The system dynamically adjusts the lag filter coefficients and overprediction margins based on real-time operating conditions such as temperature, charge/discharge rates, and battery age. This dynamic adaptation maintains high prediction accuracy across varying conditions without requiring a completely different system architecture
2Reliability
If the system uses accurate SOC prediction to prevent thermal runaway, then the reliability improves, but the response time increases due to additional processing
Solution Approach 1:
The system continuously pre-processes battery data during normal operation to build accurate SOC predictions before thermal events occur. By maintaining ready-to-use prediction models and lookup tables, the system can immediately respond to thermal runaway risks without time-consuming calculations, thus improving both reliability and response time
Solution Approach 2:
When thermal runaway risk is detected, the system bypasses detailed analysis steps and directly implements protective actions based on pre-determined thresholds and overpredicted SOC values. This rushing through of critical decision-making reduces response time while maintaining reliability
3Object-affected harmful factors
If the system derates current draw early to prevent thermal events, then the safety improves, but the productivity decreases due to reduced power output
Solution Approach 1:
The system applies partial derating by reducing current draw only for affected battery cells or modules rather than the entire battery pack. The overprediction margin is calibrated to apply excessive caution only when necessary, maintaining full power output during normal operation while providing targeted protection when thermal risks are detected
Solution Approach 2:
The system implements localized derating strategies that reduce current draw only in specific battery cells, modules, or regions showing thermal risk, while maintaining normal operation in healthy portions of the battery system. This selective approach preserves overall productivity while addressing local thermal hazards
4Measurement precision
If the system uses overpredicted SOC values with predetermined margins, then the measurement precision improves, but the loss of information increases due to conservative estimation
Solution Approach 1:
The system continuously compares overpredicted SOC values with actual measured values and uses this feedback to refine prediction models and adjust overprediction margins. This closed-loop approach maintains measurement precision while minimizing information loss by learning from discrepancies between predicted and actual SOC
Solution Approach 2:
The overprediction margin is dynamically adjusted based on confidence levels, operating conditions, and historical accuracy data. When confidence is high or conditions are stable, the margin is reduced to minimize information loss. When uncertainty increases or conditions are volatile, the margin expands to maintain precision, creating a balanced approach to information preservation
Data Source
AI summary
A battery system includes a multi-cell rechargeable energy storage system (RESS) and an electronic controller configured to process RESS cell data. The controller is programmed with a battery cell open circuit voltage (OCV) versus state of charge (SOC) data table used to calculate a peak OCV movement for a representative battery cell at predetermined capacity and maximum discharge/charge rate. The controller is configured to acquire voltage data for one of the cells during active RESS cycling. The controller is also configured to pass the acquired voltage data through a first-order lag filter algorithm to generate an overpredicted OCV for the subject cell. The controller is additionally configured to determine an overpredicted SOC value for the subject cell in the data table using an OCV value from the overpredicted OCV. Furthermore, the controller is configured to regulate the RESS using the determined overpredicted SOC value for the subject cell.