Hybrid Vehicle Power Flow Control With Convex Mission Optimization
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Solution Overview
Problem
Existing optimization methods for controlling vehicle power flows are computationally intensive and time-consuming, making real-time implementation challenging due to the complexity of nonlinear and non-convex cost functions and constraints.
Innovation Solution
A method and system that utilize a control unit to solve a convex optimal control problem by minimizing fuel consumption and battery charge constraints, using a discrete and continuous variable control approach, and applying Pontryagin's minimum principle to reduce computational effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If known optimization methods are used to minimize fuel consumption, then optimal control inputs are achieved, but computational time increases heavily
Solution Approach 1:
The patent segments the control problem into two parts: an online control law that provides immediate control actions, and an offline optimization that computes optimal trajectories. This segmentation allows real-time control without heavy computational burden during execution.
Solution Approach 2:
The patent performs preliminary computation of optimal control trajectories offline before actual vehicle operation. The precomputed optimal paths and control inputs are stored and reused during real-time operation, eliminating the need for heavy real-time optimization calculations.
2Measurement precision
If nonlinear and non-convex cost functions are used to model fuel consumption, then accuracy is improved, but device complexity increases
Solution Approach 1:
The patent transforms the nonlinear non-convex optimization problem into a convex optimization problem by changing parameters and variables. This transformation maintains accuracy in fuel consumption modeling while enabling efficient solution through convex optimization techniques.
Solution Approach 2:
The patent replaces the complex nonlinear optimization mechanism with a convex optimization mechanism that has equivalent or superior performance. The convex formulation substitutes the difficult-to-solve nonlinear problem with a computationally tractable alternative that guarantees global optimality.
Data Source
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AI summary
A method for controlling a vehicle (101) on a mission, the vehicle (101) comprising a first and a second power source (ICE, EM) for driving the vehicle (101) itself, the method comprising the steps of: solving an optimal control problem based on a mathematical model of the vehicle (101), the optimal control problem involving at least one state variable (Eb); the solving including minimizing a cost function, with respect to first and second control variable (i, P) and subject to a set of constraints, based on Pontryagin's minimum principle and based on minimization of a Hamiltonian function (H), associated to the optimal control problem, with respect to the first control variable (i), the second control variable (P), and a costate variable (λ); and controlling the vehicle (101) based on the solution of the optimal control problem.