Vehicle Predictive Optimization With Safe-State Horizon Constraints
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Solution Overview
Problem
Existing predictive optimization methods in vehicles are computationally intense and use is limited to a relatively short prediction horizon, which have not been addressed in the field of environmental pollution control and purification.
Innovation Solution
The use of predictive optimization methods are used to a relatively short prediction horizon, which have not been addressed in the field of environmental pollution control and purification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predictive optimization is used with a longer prediction horizon, then the ability to plan for safe vehicle states is improved, but computational intensity increases making it infeasible for real-time control
Solution Approach 1:
The prediction horizon is segmented into multiple shorter sub-horizons. The optimization problem is divided into sequential sub-problems, each solving for a shorter time window. This segmentation reduces the computational complexity of each individual optimization problem while collectively covering the longer prediction horizon needed for safety assurance.
Solution Approach 2:
Feasibility checks and constraints are prepared in advance based on the longer prediction horizon requirements. The system pre-computes safe state regions and feasibility conditions that must be satisfied at each sub-horizon step, allowing the real-time optimizer to work with pre-defined safety boundaries rather than computing everything from scratch.
2Reliability
If predictive optimization is constrained by safety requirements, then the risk of unfeasible vehicle states is reduced, but the feasible region for optimization is limited
Solution Approach 1:
The feasible region boundaries are made dynamic rather than static. The optimization constraints adapt based on current vehicle state, environmental conditions, and predicted future states. This allows the feasible region to expand or contract dynamically, providing more flexibility when conditions permit while maintaining safety boundaries when risks are present.
Solution Approach 2:
The system continuously monitors the vehicle state and optimization results, using feedback to adjust the feasible region constraints. When the optimizer approaches boundary conditions or when safety margins are reduced, feedback mechanisms modify the constraints to prevent unfeasible states while allowing maximum optimization freedom when safety is not at risk.
Data Source
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AI summary
A computer system (15) comprising processing circuitry (21) configured to: determine a set of vehicle control parameters, using predictive optimization (29) of a vehicle model with a first prediction horizon (h1), the predictive optimization (29) being constrained by a set of allowed vehicle states at the first prediction horizon (h1); and control an operation of a vehicle (1) using the determined set of vehicle control parameters, wherein: the set of allowed vehicle states at the first prediction horizon (h1) is a set of initial vehicle states (S0) estimated to result in a set of safe vehicle states (Sh2) at a second prediction horizon (h2) longer than the first prediction horizon (h1).