Engine Control Module Parameter Optimization
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Solution Overview
Problem
Existing engine optimization techniques fail to effectively optimize a variable number of parameters and parameter sets during engine operation, limiting the potential for enhanced performance and efficiency.
Innovation Solution
A power system optimizer that iteratively performs optimization processes based on sensor measurements to identify and optimize multiple sets of adjustable parameters, allowing for the configuration of control devices with optimized values to enhance engine performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed set of parameters is optimized, then the optimization process is simple and resource-efficient, but the system cannot adapt to optimize different parameter sets during operation
Solution Approach 1:
The optimization system dynamically adjusts the set of parameters being optimized during engine operation. The ECM can select different parameter sets based on current operating conditions, allowing the system to adapt from optimizing fuel economy parameters to performance parameters as needed. This dynamic reconfiguration resolves the contradiction by making the optimization process flexible rather than fixed.
Solution Approach 2:
The parameter optimization is divided into discrete, manageable sets that can be independently selected and optimized. Rather than attempting to optimize all parameters simultaneously or using a fixed set, the system segments parameters into different groups (e.g., fuel economy parameters, performance parameters, emissions parameters) that can be addressed in separate optimization cycles.
2Productivity
If multiple parameters are optimized simultaneously, then comprehensive performance improvement is achieved, but computational resources and processing time are excessive
Solution Approach 1:
The system optimizes a subset of parameters at each optimization cycle rather than all parameters simultaneously. The ECM selects a manageable number of parameters to optimize in each iteration, achieving meaningful performance improvements through multiple partial optimization passes rather than attempting exhaustive simultaneous optimization that would consume excessive computational resources.
Solution Approach 2:
The optimization process is performed periodically in discrete cycles rather than continuously. Between optimization cycles, the system operates with the previously determined optimized parameters. This periodic approach allows the ECM to perform comprehensive optimization calculations at scheduled intervals while maintaining efficient real-time operation between cycles.
3Reliability
If optimization is performed continuously, then real-time performance enhancement is achieved, but system resources are depleted
Solution Approach 1:
The optimization process executes periodically at predetermined intervals or under specific triggering conditions rather than continuously. This allows the system to maintain real-time optimization capability by performing comprehensive calculations at regular intervals while conserving computational energy during periods between optimizations when the engine operates with previously optimized parameters.
Solution Approach 2:
The system uses feedback from sensor measurements to determine when optimization should be performed and what parameter sets to optimize. The ECM monitors engine operating conditions and selectively initiates optimization cycles based on actual performance needs, avoiding unnecessary optimization computations when the system is already operating optimally or when conditions don't warrant re-optimization.
Data Source
AI summary
Power system optimization is disclosed. An example power system described herein may include an engine control module that receives measurements associated with sensors, identifies settings associated with control devices, determines that a first set of parameters associated with the one or more control devices is to be optimized according to a first optimization process, iteratively performs the first optimization process until the first set of parameters are optimized, determines that a second set of parameters associated with the one or more control devices are to be optimized according to a second optimization process, iteratively performs the second optimization process until the second set of parameters are optimized, and, after the second set of parameters are optimized according to the second optimization process, configures one of the control devices to operate using an optimized value for the control device determined using the second optimization process.