MODACS Battery String Switching for SoC and Thermal Balance
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Solution Overview
Problem
The MODACS system in vehicles faces challenges in optimally switching the configurations of energy storage strings to meet changing power demands without overheating, which requires a system and method for dynamically adjusting capacity and managing temperature differences across multiple strings.
Innovation Solution
A method and system that operate a battery with multiple modules, each containing multiple strings, by calculating a model-predicted path vector to balance the states of charge and temperature across strings, and selecting between this path and a default path based on operating parameters such as temperature and power demand, to switch between different configurations and modes of operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the MODACS system rotates power duties through strings at a high rate to meet changing power demands, then the power demand responsiveness is improved, but the system temperature increases causing overheating
Solution Approach 1:
The patent implements dynamic switching between two control modes: a model-predictive control (MPC) mode for normal operation that optimizes string configuration based on predicted future states, and a default mode for transient conditions. This dynamic adaptation allows the system to respond to power demands while considering thermal constraints, resolving the contradiction between responsiveness and temperature control.
Solution Approach 2:
The model-predictive control calculates optimal string configurations by predicting future system states and power demands before they occur. This preliminary action allows the system to proactively manage power distribution and temperature, rather than reactively responding to thermal constraints after overheating begins.
2Productivity
If the MODACS system uses model-predictive control to optimize string configurations, then the power distribution efficiency is improved, but the control system complexity increases
Solution Approach 1:
The control system is segmented into two distinct modes: a computationally intensive model-predictive control mode for optimization, and a simpler default mode for transient handling. This segmentation allows the complex MPC to be used only when beneficial, reducing overall system complexity while maintaining efficiency benefits.
Solution Approach 2:
A high-speed loop acts as an intermediary between the model-predictive controller and the power switching system. This intermediary rapidly determines whether to apply MPC or default control based on current operating conditions, filtering out unnecessary complexity while preserving the efficiency advantages of predictive control.
3Adaptability or versatility
If the system switches between different control modes frequently to adapt to changing conditions, then the adaptability is improved, but the switching power loss increases
Solution Approach 1:
The system maintains continuous model-predictive control operation during steady-state conditions, avoiding frequent switching between modes. The high-speed loop continuously monitors conditions and only triggers mode transitions when necessary, ensuring that the beneficial MPC control action continues uninterrupted while minimizing switching events that cause power loss.
Solution Approach 2:
The high-speed loop provides continuous feedback on system conditions to determine when to switch between MPC and default modes. This feedback mechanism ensures that mode switching occurs only when adaptability benefits outweigh the switching losses, optimizing the trade-off between adaptability and energy efficiency.
Data Source
AI summary
A vehicle, system and method of operating a battery having a plurality of modules, each module having a plurality of strings. The vehicle includes the battery. A processor operates the plurality of modules in a first phase of a mode of operation, calculates a model-predicted path vector for the first phase based on a time for reducing a difference between states of charges of the plurality of strings, selects between the model-predicted path vector and a default path vector based on an operating parameter of the battery, and switches operation of the plurality of modules from the first phase to a second phase using the selected one of the model-predicted path vector and the default path vector.