Hybrid Drive Train Mode Control via Unified Switching Variable
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Solution Overview
Problem
Hybrid drive trains face challenges in providing a comprehensive and easily expandable concept for controlling and selecting various operating modes, particularly due to limitations in the electrical energy source and the need for efficient mode transitions based on dynamic conditions.
Innovation Solution
A method that uses a switching variable based on a requested nominal power value, with power-dependent switching thresholds and recursive calculation, allowing for seamless transitions between operating modes including purely electric, combustion engine, boost, and braking modes, utilizing a state machine and power limit values to manage mode changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple operating modes are controlled by different control devices with multiple threshold values, then the control coverage is comprehensive, but the control concept becomes incomprehensive and difficult to expand
Solution Approach 1:
The patent merges multiple separate control devices and threshold-based control concepts into a single control device that uses a state machine with a unified switching variable. This switching variable integrates multiple influencing factors (battery state of charge, power demand, vehicle speed) into one comprehensive parameter that governs all operating mode transitions, thereby maintaining comprehensive control coverage while significantly simplifying the control concept and improving its expandability.
Solution Approach 2:
The state machine implemented in the control device serves multiple functions: it monitors the switching variable, determines current operating mode, decides mode transitions, and controls both electric machine and internal combustion engine operations. This universal control approach replaces multiple specialized control devices, reducing system complexity while maintaining comprehensive adaptability across all operating conditions.
2Productivity
If the electrical energy source is designed with limited capacity, then the vehicle can be driven predominantly in electric mode, but the energy availability becomes constrained
Solution Approach 1:
The patent implements dynamic energy management through the state machine, which continuously adjusts operating modes based on real-time conditions. When battery energy is充足, the system operates in electric mode to maximize productivity. When energy becomes constrained, the state machine dynamically transitions to hybrid or combustion engine modes to maintain vehicle operation. This dynamic adaptation allows the system to optimize electric driving capability while responding flexibly to energy availability changes.
Solution Approach 2:
The switching variable incorporates the state of charge of the battery as a key parameter. As the battery charge level changes, the switching variable value changes, triggering appropriate mode transitions. This parameter-based control enables the system to maximize electric driving when energy is available while seamlessly transitioning to alternative power sources when energy constraints arise, thereby optimizing both productivity and energy utilization.
3Reliability
If operating mode changes are based on multiple influencing factors, then the mode selection is optimized, but the control complexity increases
Solution Approach 1:
The patent introduces a switching variable as an intermediary that mediates between multiple influencing factors (battery state of charge, power demand, vehicle speed, temperature) and the operating mode selection. Instead of directly processing multiple complex factors, the control device calculates the switching variable based on these factors, then uses this single intermediate parameter to determine mode transitions. This intermediary approach maintains optimized mode selection while significantly reducing control complexity.
Solution Approach 2:
The patent replaces complex mechanical-style multi-device control systems with a software-based state machine implementation. The state machine uses algorithmic processing to evaluate the switching variable and determine appropriate operating modes, substituting physical control complexity with computational logic. This approach maintains reliable optimization based on multiple factors while reducing overall system complexity through software-based decision-making.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables efficient and reliable mode transitions by simplifying the state machine for operating mode changes, allowing for flexible adaptation to driver requests and environmental conditions, optimizing energy use and vehicle performance.
Implementation Method 1
power is provided by an electric machine in a second operating mode
Implementation Method 2
power is provided by an internal combustion engine in a first operating mode
Implementation Method 3
the moment of inertia of the moving vehicle is converted into generator power, which in turn can be used to charge the battery
Data Source
Figure 1~2
Figure 3
Figure 4~8
AI summary
The invention relates to a method for operating a hybrid drive train (10) for a motor vehicle (11), wherein in a first operating mode (60; 76) power is provided by an internal combustion engine (12) and in a second operating mode (56; 74) power is provided by an electric machine (40), wherein a change from one operating mode to the other operating mode is performed depending on the state of the drive train (10) and/or of the motor vehicle (11). The change of the operating mode is controlled according to at least one switching variable (sLj), which is a function of a requested target power value (PFW).