Vehicle Predictive Control Using Slope-Based Horizon Segmentation
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Solution Overview
Problem
Conventional cruise control systems are inefficient in terms of energy efficiency and computational load, which is a concern for eco-friendly vehicles, and there is a need for improved computational processing to optimize driving control for vehicles powered by batteries.
Innovation Solution
A vehicle predictive control method that determines a driving prediction horizon, divides it into steps, integrates sloped sections, and applies a driving prediction model based on vehicle speed, traction force, and braking force, using a relationship between states to calculate control values efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If model predictive control uses a longer prediction horizon to obtain better solutions, then control accuracy is improved, but computational load increases
Solution Approach 1:
The prediction horizon is divided into multiple time steps, allowing the control problem to be solved sequentially rather than all at once. This segmentation reduces the computational burden at each step while maintaining the benefits of a longer overall prediction horizon for improved control accuracy.
Solution Approach 2:
Road information such as slope and curvature is obtained in advance from map data before the vehicle reaches those sections. This preliminary acquisition of information allows the predictive control system to prepare control strategies ahead of time, reducing real-time computational requirements while improving control accuracy.
2Ease of operation
If conventional cruise control is designed for driving convenience and safety, then driving performance is improved, but energy efficiency deteriorates
Solution Approach 1:
The system obtains road information in advance from map data and uses this information to plan energy-efficient driving strategies before the vehicle reaches specific road sections. This allows the system to optimize energy consumption while maintaining driving convenience and safety.
Solution Approach 2:
The predictive control system continuously monitors actual vehicle state and compares it with predicted values, adjusting control actions to optimize energy efficiency while maintaining driving performance. The system provides feedback-based adjustments to achieve both convenience and energy efficiency.
Data Source
AI summary
Disclosed herein is a vehicle predictive control method that includes determining a driving prediction horizon in front of a vehicle, dividing the driving prediction horizon into a plurality of steps, at least some of the steps corresponding to a sloped section being integrated into one step according to slopes, and applying a driving prediction model based on a relationship between states of vehicle speed, traction force, and braking force for each step and collectively computing the driving prediction model over the entire prediction horizon to calculate a control value for the vehicle.