Vehicle Control Apparatus Damping Force Adaptation
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Solution Overview
Problem
Existing vehicle control systems fail to dynamically adjust damping force characteristics to match driver preferences and specific requirements, limiting their ability to optimize ride comfort and handling based on varying road conditions and vehicle states.
Innovation Solution
A vehicle control system that employs a deep neural network (DNN) to learn and adjust damping force characteristics by processing input vehicle state data and road surface information, using a calculation processing portion to output target damping forces and control instruction values, allowing for real-time optimization of damping force settings based on driver preferences and specifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed damping force control system is used, then the control system is simple and reliable, but it cannot adapt to different driver preferences and road conditions
Solution Approach 1:
The patent implements dynamic adaptability by enabling the control system to switch between multiple damping force characteristics (first, second, and third characteristics) based on detected road conditions and vehicle states. The system dynamically adjusts the evaluation function weights and damping force targets to match different driving scenarios, transforming a static control system into a dynamic one that adapts to varying conditions without requiring complete system redesign.
Solution Approach 2:
The patent changes key parameters of the control system including the evaluation function weights (q1, q2), damping force characteristics, and control priorities based on detected conditions. By modifying these parameters dynamically, the system achieves multiple damping force characteristics using the same hardware platform, thereby improving adaptability without proportionally increasing device complexity.
2Adaptability or versatility
If multiple damping force characteristics are implemented to satisfy different driver preferences, then the system becomes more versatile, but the control system complexity increases
Solution Approach 1:
The patent achieves multi-functionality by designing a single control system that can provide multiple damping force characteristics (comfort-oriented, sport-oriented, and balanced characteristics) through dynamic parameter adjustment. The same control apparatus serves multiple functions by changing evaluation function weights and control priorities based on detected conditions, eliminating the need for separate control systems for each damping characteristic.
Solution Approach 2:
The system dynamically switches between different damping force characteristics based on real-time detection of road conditions and vehicle states. By making the control parameters dynamic rather than fixed, the system achieves versatility without requiring multiple static control systems, thereby managing complexity through intelligent parameter variation rather than structural multiplication.
3Reliability
If real-time adjustment of damping force is implemented, then ride comfort and handling are optimized, but calculation and control complexity increases
Solution Approach 1:
The patent performs preliminary classification of driving conditions into distinct categories (first, second, and third conditions) with predetermined evaluation function weights and control priorities. This preliminary structuring allows the system to quickly select appropriate control parameters without performing complex real-time calculations for every parameter adjustment, thereby reducing computational complexity while maintaining optimization capability.
Solution Approach 2:
The control system segments the control process into distinct stages: condition detection, condition classification, evaluation function selection, and damping force calculation. By dividing the complex real-time control into segmented steps with predetermined parameters for each condition type, the system reduces calculation complexity at each stage while achieving overall optimization of ride comfort and handling.
Data Source
AI summary
A controller includes a calculation processing portion configured to make a predetermined calculation based on an input vehicle state amount and output a target damping force, and a damping force map configured to acquire a control instruction value for controlling a variable damper based on the target damping force. The calculation processing portion makes the calculation using a learning result that is acquired by causing the calculation processing portion to learn pairs of a plurality of target amounts acquired using a predetermined evaluation method prepared in advance with respect to a plurality of different vehicle state amounts as pairs of pieces of input and output data.