Hybrid Vehicle Battery SOC Threshold Control From Driving History
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Solution Overview
Problem
Existing battery energy management strategies for hybrid vehicles rely on a constant remaining state of charge threshold for switching between power consumption and power holding modes, which fails to accurately meet the diverse needs of different users due to varying usage habits.
Innovation Solution
A battery energy management method and device that dynamically adjusts the remaining state of charge threshold by predicting the next cutoff remaining state of charge value and lower limit of the remaining state of charge threshold based on historical usage data, ensuring more personalized energy management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a constant remaining state of charge threshold is used for switching between power consumption and power holding modes, then the management strategy is simple and easy to implement, but it cannot accurately meet the diverse needs of different users due to varying usage habits
Solution Approach 1:
The patent transforms the static constant threshold into a dynamic adaptive threshold that automatically adjusts based on historical usage data. The system collects historical cutoff remaining state of charge values and maximum power demands, then uses prediction models to generate personalized thresholds for different users and driving conditions, making the management strategy both easy to implement and highly adaptable
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously monitors actual usage patterns, compares them with historical data, and adjusts the remaining state of charge threshold accordingly. The prediction model uses historical cutoff values and power demands to generate optimized thresholds, creating a closed-loop system that learns from user behavior and improves over time
2Adaptability or versatility
If the remaining state of charge threshold is adjusted dynamically based on historical usage data, then the adaptability to user needs is improved, but the complexity of the management strategy increases
Solution Approach 1:
The patent applies preliminary action by pre-collecting and storing historical usage data during normal operation, including historical cutoff remaining state of charge values and maximum power demands. This pre-processed data is then used by the prediction model to quickly generate optimized thresholds without requiring complex real-time calculations, thereby reducing operational complexity while maintaining high adaptability
Solution Approach 2:
The patent introduces a prediction model as an intermediary between the raw historical data and the threshold adjustment decision. This intermediary component simplifies the complexity by encapsulating the complex analysis logic within a dedicated module, allowing the main control system to make threshold adjustments through simple model predictions rather than complex real-time computations
Data Source
AI summary
A battery energy management method comprises: acquiring, for a vehicle usage circulation, a historical cutoff remaining battery capacity value and a historical maximum power demand; predicting a next cutoff remaining battery capacity value according to the historical cutoff remaining battery capacity value; predicting, according to the maximum historical power demand, a lower limit value of a next remaining battery capacity threshold for switching from a power consumption mode to a power maintaining mode; determining the lager value of the predicted next cutoff remaining battery capacity value and the predicted lower limit value of the next remaining battery capacity threshold as a next remaining battery capacity threshold for switching from the power consumption mode to the power maintaining mode; and sending, to the hybrid vehicle, the determined next remaining battery capacity threshold for switching from the power consumption mode to the power maintaining mode.