Hybrid Vehicle Controller Predicting Driver Torque to Avoid Full Load Mode
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Solution Overview
Problem
Hybrid vehicles often enter full load mode due to low battery state of charge, leading to reduced fuel efficiency and increased drivability issues, as they rely on maximum engine torque and motor output, which is inefficient and decreases battery SOC.
Innovation Solution
A method and device that predict driver accelerations and decelerations using sensors and neural networks to control the engine, preventing full load mode by calculating driver demand torque and predicting battery SOC, thereby maintaining optimal operating points and charging the battery to avoid full load mode.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If the hybrid vehicle operates in full load mode to meet high power demand, then the available power and torque are improved, but fuel efficiency deteriorates and battery SOC decreases
Solution Approach 1:
The system performs preliminary action by predicting future driver demand torque and battery SOC before the vehicle actually enters full load mode. The neural network model predicts acceleration/deceleration patterns and torque requirements in advance, allowing the control system to prepare appropriate power distribution strategies proactively, preventing unnecessary full load mode activation and preserving fuel efficiency while ensuring power availability when needed.
Solution Approach 2:
The system dynamically adjusts power distribution between engine and motor based on real-time conditions and predictions. Instead of static full load mode operation, the controller continuously optimizes the mixing ratio of engine torque and motor torque according to predicted driver behavior patterns, battery state, and driving conditions, enabling flexible power management that maintains performance while improving fuel efficiency.
2Power
If the hybrid vehicle enters full load mode due to low battery SOC, then the power availability is maintained, but drivability deteriorates and fuel consumption increases
Solution Approach 1:
The system predicts driver demand torque and battery SOC in advance using neural network models that analyze driving patterns and vehicle state. By preparing appropriate power distribution strategies before full load mode activation, the system ensures smooth transitions and maintains optimal drivability, preventing the harsh power delivery associated with abrupt full load mode entry while ensuring power availability is maintained.
3Power
If the engine operates at maximum torque in full load mode, then the immediate power demand is met, but fuel efficiency decreases and engine wear increases
Solution Approach 1:
The system dynamically optimizes engine torque output by continuously adjusting the mixing ratio of engine and motor torque based on real-time vehicle state, predicted driver behavior, and battery SOC. Instead of operating at fixed maximum torque, the engine torque is adaptively controlled to meet power demands efficiently, reducing unnecessary fuel consumption and engine wear while maintaining power availability through coordinated motor assistance.
Solution Approach 2:
The system changes operating parameters by adjusting the mixing ratio of engine torque and motor torque as a dynamic variable. This parameter optimization allows the engine to operate in more efficient torque ranges while the motor compensates for power requirements, reducing engine stress and fuel consumption during high-demand situations without sacrificing overall power delivery performance.
Data Source
AI summary
A method for controlling a full load mode of a hybrid vehicle by a controller may include: calculating driver demand torque based on acceleration pedal position sensor (APS) information or brake pedal position sensor (BPS) information; predicting the driver's acceleration/deceleration information based on the hybrid vehicle's driving information; predicting the driver demand torque based on the predicted acceleration/deceleration information; predicting a state of charge (SOC) of a battery that supplies electric power to a motor for driving the hybrid vehicle, based on the calculated driver demand torque and the predicted driver demand torque; and controlling an engine for charging the battery based on the predicted battery SOC, in order to keep the hybrid vehicle from entering into the full load mode in which the engine configured to produce a maximum torque and the motor are used.