Genset Operating Point Selection via Dynamic Programming
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Solution Overview
Problem
Hybrid vehicles face challenges in selecting optimal operating conditions for gensets that minimize fuel consumption and emissions, as existing methods involve complex trade-offs between multiple variables and lack efficient algorithms for dynamic adjustment.
Innovation Solution
A method and apparatus that utilize dynamic programming and weight assignment to select genset operating points based on demand frequency, with a golden section search to minimize cost values, ensuring monotonic engine speed and power output changes, and adaptive weight updating for real-time optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If dynamic programming with weight assignment is used to select genset operating points, then fuel consumption and emissions are minimized, but computational complexity increases
Solution Approach 1:
The patent segments the continuous operating space into discrete operating points with specific engine speeds and power outputs. By dividing the problem into discrete segments, dynamic programming can efficiently evaluate and compare different operating point combinations without dealing with infinite continuous variables, thus reducing computational complexity while still achieving optimal fuel consumption and emissions minimization.
Solution Approach 2:
The patent pre-calculates and stores cost values for each operating point before runtime optimization. By performing preliminary calculations of fuel consumption and emissions characteristics for all possible operating points, the system creates a lookup table that speeds up real-time decision-making, reducing the computational burden during actual operation while maintaining optimization effectiveness.
2Productivity
If adaptive weight updating is implemented for frequently demanded power outputs, then operational optimization improves, but system complexity increases
Solution Approach 1:
The patent implements feedback by monitoring the frequency of demand for different power outputs and using this information to adaptively update weights in the cost function. The system continuously learns from operational patterns and adjusts its optimization criteria accordingly, improving operational efficiency over time while keeping the adaptation logic relatively simple through incremental weight adjustments rather than complete re-optimization.
Solution Approach 2:
The patent changes the weight parameters in the cost function dynamically based on observed demand patterns. By adjusting these parameters adaptively, the system optimizes for frequently demanded power outputs without requiring a complete redesign of the control system, thus improving productivity with moderate increases in system complexity through parameter adaptation rather than structural changes.
3Stability of the object's composition
If monotonic constraints are applied to engine speed and power output, then operational stability improves, but flexibility in optimization is reduced
Solution Approach 1:
The patent applies monotonic constraints that allow engine speed and power output to change in a controlled, step-wise manner rather than allowing arbitrary jumps. This dynamic constraint structure provides operational stability by preventing erratic changes while still allowing the system to adapt to different power demands through sequential adjustments, thus maintaining a balance between stability and flexibility.
Solution Approach 2:
The patent changes operating parameters (engine speed and power output) in a monotonic fashion, allowing systematic exploration of the operating space while maintaining stability. This approach provides flexibility by enabling the system to reach different operating states through controlled parameter changes, rather than being locked into fixed operating modes, thus balancing stability requirements with optimization flexibility.
Data Source
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AI summary
A method and apparatus for selecting optimal operating conditions of a genset is disclosed. The genset includes an engine coupled to an electrical power generator, the genset having a plurality of operating points each including an engine speed value and a generator electrical power output value, and having a plurality of cost values associated with operating the genset at respective operating points. The method involves selecting a set of operating points from the plurality of operating points such that a sum of cost values associated with operating points in the set is minimized and such that the engine speed and generator electrical power output values of the operating points in the set increase or decrease monotonically.