Hybrid Powertrain SOC Trajectory Control for Low-Compute Route Optimization
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Solution Overview
Problem
Existing methods for optimizing the operation of hybrid powertrain systems in vehicles require significant computational effort and data processing, especially for extended forecast periods, leading to suboptimal adjustments to adverse operating events and increased pollutant emissions.
Innovation Solution
A method that retrieves an experience-based state-of-charge trajectory from an external database for a prediction period, adjusts it with optimization constraints to account for expected vehicle propulsion power and adverse events, and controls the hybrid powertrain system to minimize energy consumption and emissions, using a control unit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If long-term route predictions are performed to optimize energy efficiency, then energy consumption is reduced, but computational effort and data processing needs increase significantly
Solution Approach 1:
The system performs route predictions and determines state of charge trajectories in advance, before the actual driving occurs. By pre-calculating the optimal energy management strategy based on expected route conditions, the system reduces real-time computational requirements while maintaining optimization quality. The control device stores these pre-determined trajectories and simply retrieves them during actual operation.
Solution Approach 2:
The system creates a virtual model or copy of the expected route conditions and uses this copied information for optimization calculations. Instead of performing complex real-time predictions during actual driving, the system works with a replicated representation of the route, allowing efficient retrieval and adjustment of optimization parameters without heavy computational burden.
2Adaptability or versatility
If real-time optimization is performed during driving, then adaptability to current conditions is improved, but computational time and processing power increase
Solution Approach 1:
The system pre-determines state of charge trajectories for expected route conditions before actual driving occurs. During real-time operation, the control device only needs to retrieve the pre-calculated trajectory and make minor adjustments based on actual conditions, rather than performing complete optimization calculations from scratch. This maintains adaptability while dramatically reducing computational time.
Solution Approach 2:
The system dynamically adjusts the pre-determined state of charge trajectory based on actual driving conditions that differ from expectations. The control device continuously compares actual conditions with predicted conditions and modifies the trajectory accordingly, allowing the system to remain adaptive to real-time conditions while avoiding the computational burden of complete real-time optimization.
3Use of energy by moving object
If the state of charge trajectory is frequently adjusted to optimize energy efficiency, then energy management is improved, but system complexity and control overhead increase
Solution Approach 1:
The system determines the state of charge trajectory in advance based on expected route conditions and stores it in the control device. During actual operation, the system retrieves this pre-determined trajectory and makes only necessary adjustments, rather than continuously calculating optimal trajectories. This approach maintains good energy management while significantly reducing control system complexity and computational overhead.
Data Source
Figure 1~2
Figure 3~4
Figure 5a~6b
AI summary
In a method for operating a vehicle with a hybrid powertrain system (1), the operation of the hybrid powertrain system (1) is optimized using an optimization procedure with regard to a desired state-of-charge trajectory (28), taking into account the estimated expected vehicle drive power, and is controlled by the control unit (8). The hybrid powertrain system (1) comprises an internal combustion engine (2) and an electrically driven torque machine (3), which is connected to an energy storage device (7) for energy transfer. The internal combustion engine (2) and the torque machine (3) are controlled by a control unit (8) and are connected to an output element (5) via a hybrid transmission (4). Before the start of the prediction period Δt, an experience-based state-of-charge trajectory (27) is retrieved from an external database (12) for the expected driving route, which covers at least the prediction period Δt.The desired state-of-charge trajectory (28) is determined from the experience-based state-of-charge trajectory (27) by modification with at least one optimization constraint. The experience-based state-of-charge trajectory (27) can be determined based on operating data from hybrid powertrain systems of several vehicles and/or on operating data from several comparable journeys with the same vehicle.