EV Battery Controller Propulsion Loss Detection
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Solution Overview
Problem
Existing systems for controlling rechargeable energy storage packs in electric vehicles fail to effectively assess and manage state of charge disparities across cells, leading to inefficiencies and potential propulsion loss, particularly during long-term parking or charging/discharging cycles.
Innovation Solution
A controller system that assesses state of charge disparities by calculating parameters such as disparity mean, slope, and standard deviation, and raises flags to alert users or management units when thresholds are exceeded, incorporating off-time and balancing correction factors to adjust for parking and charging events, thereby optimizing cell balance and preventing propulsion loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system monitors state of charge disparities across all cells continuously, then propulsion loss detection accuracy is improved, but system complexity and computational load increase
Solution Approach 1:
The system segments the monitoring task by focusing only on the cell with the minimum state of charge rather than continuously monitoring all cells. This segmentation reduces computational complexity while maintaining detection accuracy, as the minimum SOC cell is the critical factor in determining propulsion loss.
Solution Approach 2:
The system extracts and focuses on the critical parameter (minimum SOC) from the complete set of cell data. By taking out only the essential information needed for propulsion loss detection, the system reduces data processing requirements while preserving detection capability.
2Loss of time
If the system raises flags for threshold exceedances, then propulsion loss alert timeliness is improved, but false positive rate increases due to long-term parking scenarios
Solution Approach 1:
The system performs preliminary assessment by evaluating the time duration since last movement before raising propulsion loss flags. This preliminary check distinguishes between genuine propulsion loss during operation and normal SOC disparities during long-term parking, reducing false positives while maintaining timely alerts.
Solution Approach 2:
The system incorporates feedback about vehicle status (parking duration, movement history) into the flag-raising decision process. This feedback mechanism allows the system to adjust its sensitivity based on contextual information, preventing false alarms during parking while maintaining responsiveness during actual propulsion loss events.
3Measurement precision
If the system applies correction factors for parking and charging events, then measurement accuracy is improved, but computational requirements increase
Solution Approach 1:
The system changes the measurement parameter by applying correction factors that adjust the SOC disparity calculation based on parking and charging conditions. These parameter modifications account for natural SOC equalization during parking and charging, improving measurement accuracy without requiring complex computational models.
Data Source
AI summary
System and method of controlling operation of a device having a rechargeable energy storage pack with a plurality of cells, based on propulsion loss assessment. A controller is configured to obtain a state of charge data and an open circuit voltage of the rechargeable energy storage pack. The controller is configured to obtain a state of charge disparity factor (dSOC) from a selected dataset. The state of charge disparity factor (dSOC) is defined as a difference between a minimum value of the state of charge and an average value of the state of charge of the plurality of cells. The controller is configured to control operation of the device based in part on the state of charge disparity factor (dSOC) and a plurality of parameters (Pi), including raising one or more of a plurality of flags each transmitting respective information to a user.