Kalman-Based Auxiliary Power Estimation for EV Failure Detection
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Solution Overview
Problem
Existing systems fail to accurately estimate and analyze auxiliary loads and power losses in battery and hybrid electric vehicles, leading to inefficiencies and potential battery depletion due to unaccounted energy usage and system failures.
Innovation Solution
Implementing a method using a Kalman filter to estimate power losses by measuring torque and speed of the motor-generator-unit (MGU), battery current and voltage, and treating power losses as an unknown disturbance, along with optional inclusion of known auxiliary power uses, to obtain precise power loss estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a coulomb counter or current monitoring device is used to track current leaving the battery, then current monitoring is achieved, but auxiliary power usage and power losses cannot be analyzed
Solution Approach 1:
The patent introduces a power estimation model as an intermediary that processes multiple input signals (battery voltage, battery current, MGU torque, MGU speed) to calculate auxiliary power usage and power losses. This model acts as a mediator between the available measurement data and the required analysis, transforming raw measurements into meaningful power allocation information that reveals auxiliary loads and losses without requiring direct measurement of each individual component.
2Device complexity
If power losses are not accurately estimated, then system simplicity is maintained, but failure detection capability is reduced
Solution Approach 1:
The patent implements a feedback mechanism where the estimated auxiliary power usage and power losses are continuously monitored and compared against expected values or thresholds. When anomalies are detected (such as unexpected power consumption patterns indicating a short circuit or component failure), the system generates alerts and can trigger corrective actions. This feedback loop enables reliable failure detection while maintaining relatively simple system architecture by using computational analysis rather than complex hardware modifications.
3Device complexity
If auxiliary power usage is not tracked, then system simplicity is maintained, but battery depletion due to unaccounted energy usage cannot be prevented
Solution Approach 1:
The system performs self-service by automatically estimating auxiliary power usage and power losses using the power estimation model that processes standard vehicle sensors data. The model continuously monitors battery voltage, battery current, MGU torque, and MGU speed to calculate power allocation without requiring manual intervention or additional specialized sensors. This self-service capability enables the system to detect and report energy depletion issues proactively, allowing for timely corrective actions to prevent unnecessary battery discharge while maintaining simple system architecture.
Data Source
AI summary
Methods and systems for quantifying and monitoring auxiliary power usage and losses in an electrified vehicle. The auxiliary power is treated as an unknown disturbance in a Kalman filter, which compares power generated from the battery pack to power of the motor generator unit (MGU). The auxiliary power is considered as a sum of all auxiliary power usage and power losses in the vehicle, including in the MGU. The calculated auxiliary power from the Kalman filter is compared to a threshold to generate an alert, or to aid in range estimation, or to update battery state of charge estimates.