Road Vehicle Velocity Profiles With Two-Level Energy Optimization
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Solution Overview
Problem
Existing methods for vehicle speed control face challenges in integrating route-level optimization with local-level optimization, leading to sub-optimal solutions due to reliance on heuristics and inequality-based constraints, which are computationally complex and result in strong variations.
Innovation Solution
A two-level optimization approach is employed, where a full-horizon optimization determines a reference profile for the entire route segment, followed by short-horizon optimizations that minimize a cost function parameterized by values derived from the full-horizon optimization, ensuring smooth and coherent speed profiles without inequality constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If a full-horizon optimization is performed for the entire route segment, then the optimal energy profile is determined, but the computational complexity increases significantly
Solution Approach 1:
The route segment is divided into multiple local sections, allowing the optimization problem to be broken down into smaller, more manageable sub-problems that can be solved sequentially rather than requiring a single complex full-horizon optimization
Solution Approach 2:
A reference energy profile is pre-computed for the entire route segment using full-horizon optimization. This reference profile serves as a guide for subsequent local optimizations, eliminating the need to re-solve the entire problem from scratch and significantly reducing computational complexity
2Reliability
If inequality-based constraints are used to integrate route-level optimization with local-level optimization, then the integration is achieved, but the computational complexity increases
Solution Approach 1:
The approach transforms the constrained optimization problem into an unconstrained one by changing the parameterization method. Instead of using inequality-based constraints, the solution uses a cost function that incorporates the reference profile through parameter updates, making the problem computationally more tractable while maintaining integration coherence
3Productivity
If heuristics are used to determine parameters in multi-objective schemes, then the computation is simplified, but the solution quality becomes sub-optimal
Solution Approach 1:
The method uses feedback from the reference energy profile to guide local optimizations. The reference profile, computed from full-horizon optimization, provides feedback information that shapes the cost function for local sections, ensuring that local decisions are aligned with the global optimal solution without requiring complex heuristics
4Reliability
If terminal energy constraints are imposed on local optimizations, then the integration with high-level solution is achieved, but strong variations in local solutions occur
Solution Approach 1:
Instead of imposing hard terminal energy constraints that cause abrupt changes in local solutions, the method changes the approach by using a soft constraint through the cost function parameterization. The reference profile influences local optimizations through a continuous parameter update mechanism, resulting in smoother and more stable local solutions while maintaining integration consistency
Data Source
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AI summary
The invention is notably directed to a computer-implemented method for controlling a speed of a vehicle given energy-related quantities that pertain to moving the vehicle. Each of said energy-related quantities is being referred to as an energy expense in the following. The method involves two key operations (a full-horizon optimization and multiple, nested short-horizon optimizations), which are performed based on route parameters of a route segment and real-time signals capturing current states of the vehicle. First, a target function of a profile of an energy expense is optimized for a route segment, to determine a reference profile formula (I) of this energy expense. The optimization is constrained with respect to objectives in respect of a total travel time and/or a total travel energy expense for the vehicle to travel said route segment. A profile of parameter values is obtained according to a by-product of the optimization. Next, short-horizon speed profiles V ref of the vehicle are repeatedly determined to plan speeds V ref of the vehicle along local sections of the route segment. Such predictions are achieved by minimizing, for each of the local sections, a cost function as parameterized by one of the parameter values. The cost function captures a cost of deviating from an output of the optimized target function due to such speed profiles; said output and said one of the parameter values pertain, each, to said each of the local sections. Finally, control signals are obtained based on the planned speeds V ref.Such control signals can be used for controlling a speed of the vehicle along each of said local sections of the route segment. The invention is further directed to related systems, vehicles, and computer program products.