Hybrid Vehicle Characteristic Curve Online Adaptation
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Solution Overview
Problem
Existing methods for selecting an optimal operating mode in hybrid vehicles are complex and require high computing effort, especially when dealing with multiple target criteria and deviations between planned and actual vehicle use, making online adaptation of characteristic curves difficult.
Innovation Solution
A method for online adaptation of characteristic curves in hybrid vehicles, where the state of charge range is divided into areas with variably definable subdivision limits, allowing the characteristic curves to be shifted and scaled dynamically based on current conditions and forward-looking information, reducing complexity and computing effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If online adaptation of characteristic curves is implemented to handle deviations between planned and actual vehicle use, then adaptability is improved, but device complexity and computing effort increase
Solution Approach 1:
The state of charge range is divided into multiple areas with defined subdivision limits. Each area has associated characteristic curves that can be independently selected and adapted. This segmentation allows the system to handle adaptability requirements through modular area-based management rather than complex global curve adaptation.
Solution Approach 2:
The characteristic curves and subdivision limits are pre-defined and stored in memory before vehicle operation. During online operation, the system only needs to select and switch between pre-prepared curves based on current operating conditions and predictive information, avoiding the need for complex real-time curve generation and reducing computing effort.
2Measurement precision
If multiple characteristic curves with numerous dependencies are used to model multiple target criteria, then measurement precision is improved, but device complexity increases
Solution Approach 1:
Multiple target criteria are evaluated by dividing the state of charge range into multiple areas, each with its own characteristic curves. This segmentation allows precise modeling of different criteria within specific operating ranges while managing complexity through localized curve definitions rather than numerous interdependent global curves.
Solution Approach 2:
Different characteristic curves are applied to different areas of the state of charge range, allowing each area to have optimized curves tailored to specific operating conditions and target criteria. This local optimization maintains high measurement precision for each criterion while reducing overall system complexity through specialized local models.
Data Source
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AI summary
The invention relates to a method for online adaptation of at least one characteristic curve of a hybrid vehicle comprising a hybridized powertrain with an electric drive which can be supplied with current from an electrical energy storage device, wherein the at least one characteristic curve is used to select an operating mode and/or to determine an operating point of the powertrain, characterized by the steps: (a) defining several subdivision limits (2-7) for dividing a state-of-charge range (1) of the energy storage device into several ranges, wherein at least one of the subdivision limits (3-6) can be variably defined during operation of the vehicle; (b) specifying a profile (10, 11) of the at least one characteristic curve as a function of a state-of-charge range of the energy storage device, wherein the values (13-16) of the at least one characteristic curve are fixed at the subdivision limits (2-7);and (c) Online adaptation of the at least one characteristic curve in the event of a shift of at least one of the variable subdivision limits (3-6), wherein the at least one characteristic curve is shifted and scaled taking into account the fixed predefined values (13-16) at the subdivision limits (2-7) and corresponding to the shifted variable subdivision limit(s).