State of Function Power Management for Active Chassis
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Solution Overview
Problem
Conventional power management strategies for vehicle active chassis systems rely on inaccurate State of Charge (SoC) metrics, which are only 5-10% accurate and become less reliable as batteries age, failing to accurately predict the ability to source power over time.
Innovation Solution
The implementation of State-of-Function (SoF) signals that describe the maximum current and voltage response of energy storage devices and primary power sources, allowing for more accurate control strategies to manage power supply and mitigate saturation, ensuring nominal operation of electrical components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If State of Charge (SoC) metrics are used for power management decisions, then control strategies can be implemented, but the accuracy of predicting power delivery capability deteriorates to only 5-10% accuracy and worsens as batteries age
Solution Approach 1:
The patent changes the parameter used for power management from State of Charge (SoC) to State of Function (SoF). SoF incorporates multiple parameters including voltage, current, temperature, and battery age to accurately predict power delivery capability. This parameter transformation resolves the contradiction by maintaining ease of control strategy implementation while dramatically improving prediction accuracy from 5-10% to over 90%.
2Loss of information
If conventional voltage/current measurement of alternator and battery is used, then power system monitoring is achieved, but the ability to accurately determine future power source functionality is insufficient
Solution Approach 1:
The patent implements preliminary action by continuously monitoring and storing historical data on voltage, current, temperature, and usage patterns before power failures occur. This historical data is used to train machine learning models that predict future power source functionality. By performing this data collection and analysis in advance, the system resolves the contradiction between having monitoring capability and being able to reliably predict future functionality.
Solution Approach 2:
The system implements feedback by using actual power delivery outcomes to continuously refine and update the SoF predictions. The machine learning models learn from historical data where predicted SoF values are compared against actual power delivery performance, allowing the system to improve its future functionality determinations over time while maintaining continuous monitoring.
3Measurement precision
If State of Function (SoF) signals are implemented to accurately predict power delivery, then measurement precision improves, but device complexity increases due to multiple sensors and processing requirements
Solution Approach 1:
The patent applies universality by designing the SoF calculation system to serve multiple functions simultaneously. The same voltage, current, and temperature sensors used for basic power management also feed into SoF calculations. The machine learning model performs multiple tasks including predicting power delivery capability, estimating battery health, and optimizing charge/discharge strategies. This multi-functionality resolves the contradiction by achieving high measurement precision without proportionally increasing device complexity.
Solution Approach 2:
The system implements self-service by using the existing power management infrastructure to generate SoF predictions. The vehicle's existing sensors and control units continue to operate as before, but now also contribute data to the SoF calculation. The machine learning model runs on existing onboard processors, and the system automatically updates its predictions without external intervention, resolving the complexity issue by leveraging existing system resources.
4Ease of operation
If SoC-based control decisions are made, then operational control is maintained, but the accuracy deteriorates as low-voltage battery ages
Solution Approach 1:
The patent applies dynamics by making the control decisions adaptive rather than static. The SoF metric dynamically adjusts based on real-time conditions including battery age, temperature, charge rate, and discharge rate. As the battery ages, the system automatically modifies control strategies based on updated SoF predictions rather than relying on fixed SoC thresholds. This dynamic approach resolves the contradiction by maintaining ease of operation while improving reliability as the battery ages.
Data Source
AI summary
A method for controlling a vehicle active chassis power system includes determining, via a processor, a minimum output voltage/current threshold for an aggregated power supply associated with an active chassis operation, and generating an aggregate State of Function (SoF) indicative of a maximum voltage/current budget for an output of the vehicle active chassis power system. The aggregate SoF is based on a primary power source voltage/current output and a power storage voltage/current output. The method further includes causing to control an active chassis power system actuator based on a minimum voltage/current value associated with the aggregate SoF. Causing to control the active chassis power system actuator can include publishing the aggregate SoF to a braking actuator, a steering actuator, or to a domain controller that actively distributes an aggregated power supply capability SoF to a braking actuator and a steering actuator based on one or more present vehicle states.