Hybrid Energy Storage Control for Marine Vessel Fuel Efficiency
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Solution Overview
Problem
Existing power system management technologies for marine vessels fail to determine an optimal distribution of power demand among multiple power sources based on dynamic variables, health, and remaining useful life, leading to inefficiencies in fuel consumption and system performance.
Innovation Solution
A hybrid energy storage system optimization strategy with intelligent adaptive control that uses a power controller to receive load and BSFC map data, determine performance indicators, and generate commands for optimal power distribution among engines and batteries, minimizing energy costs and considering health and operational parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If multiple power sources with different operating ranges and efficiencies are used to drive loads, then system performance and fuel consumption are improved, but determining optimal power distribution becomes challenging
Solution Approach 1:
The patent implements dynamic power distribution control that continuously adapts to varying load conditions, power source health states, and operational parameters. The system dynamically adjusts the operating points of multiple power sources (engines and batteries) in real-time to maintain optimal efficiency while responding to changing demands, rather than using fixed or static distribution strategies.
Solution Approach 2:
The control system incorporates feedback mechanisms that monitor the actual state of power sources (including health indicators, remaining useful life, and current operating conditions) and use this information to continuously optimize power distribution. The system receives feedback on load requirements and power source performance, then adjusts distribution strategies to minimize fuel consumption while extending power source longevity.
2Use of energy by moving object
If power distribution is optimized for fuel efficiency, then energy cost is reduced, but system adaptability to varying load conditions and power source health deteriorates
Solution Approach 1:
The system employs dynamic optimization that continuously adapts power distribution strategies based on real-time conditions including load variations, power source health states, and environmental factors. This allows the system to maintain fuel efficiency while simultaneously adapting to changing operational requirements and power source capabilities, rather than relying on predetermined or static distribution schedules.
Solution Approach 2:
The control system adjusts multiple operational parameters simultaneously (power distribution ratios, operating points, charge/discharge rates) based on changing conditions. By dynamically modifying these parameters in response to load requirements and power source health, the system maintains both energy efficiency and operational flexibility across diverse operating scenarios.
3Duration of action of stationary object
If dynamic optimization considering health and remaining useful life is implemented, then power source longevity is extended, but control system complexity increases
Solution Approach 1:
The control system incorporates self-service mechanisms where power distribution decisions are automatically adjusted based on monitored health indicators and remaining useful life estimates of each power source. The system autonomously modifies its own control strategy to protect aging components, distribute load according to current capabilities, and extend overall system longevity without requiring external intervention or complex manual management.
Solution Approach 2:
The patent replaces complex mechanical or manual power distribution management with an intelligent control system that uses computational algorithms to optimize power allocation. By substituting automated digital control for traditional mechanical or human-operated systems, the solution manages the complexity of health-aware optimization while providing extended power source life through precise, adaptive control.
4Speed
If real-time load ramp calculation and power capacity adaptation are implemented, then system response to load changes is improved, but measurement and control difficulty increases
Solution Approach 1:
The system implements real-time feedback mechanisms that continuously measure actual load conditions and power source capabilities, then use this information to rapidly adjust power distribution. By monitoring the state of charge, charge/discharge rates, and health indicators of battery systems along with engine operating conditions, the system calculates available load ramp capacity and adapts power distribution in real-time to maintain optimal response speed while managing measurement complexity through integrated sensing and control.
Data Source
AI summary
A control system implementing a hybrid energy storage system (ESS) optimization strategy is disclosed. The hybrid ESS optimization strategy may be implemented in a machine that comprises a power system that includes a plurality of power sources and a power controller that includes one or more processors. The power controller may receive information related to a set of brake-specific fuel consumption (BSFC) maps associated with the plurality of power sources, determine a performance indicator using a cost function associated with the plurality of power sources, and generate a command to operate the power system based on a power distribution that minimizes an energy cost to operate the power system based on the information related to the set of BSFC maps, the performance indicator, and a load associated with the power system.