Automatic Transmission Control via Kinematic Chain State Ranking
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Solution Overview
Problem
Current methods for controlling automatic motor vehicle gearboxes fail to effectively determine the optimal kinematic chain state that balances consumption, depollution, and driving pleasure, especially in hybrid vehicles with complex powertrain configurations.
Innovation Solution
A method that ranks kinematic chain states based on depollution efficiency and consumption, using an energy management law to identify the optimal driveline state, and then consolidates this into a stable target state through adaptive ratio control, considering changes in eligibility and ranking to prioritize approval and amenity constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If multiple kinematic chain states are considered for optimal selection, then consumption and depollution efficiency improve, but device complexity increases
Solution Approach 1:
The control method segments the selection process into distinct phases: first determining a preliminary optimal state based on energy criteria (consumption and depollution), then evaluating approval constraints separately, and finally consolidating these assessments to determine the final optimal kinematic chain state. This segmentation allows complex multi-criteria optimization to be broken down into manageable steps.
Solution Approach 2:
The method performs preliminary determination of optimal states based on energy management laws before applying approval constraints. By pre-calculating which states are optimal from an energy perspective, the system narrows down the search space early, reducing the computational burden of subsequent constraint evaluations.
2Reliability
If approval constraints are strictly enforced to ensure reliability, then vehicle performance reliability improves, but the range of viable driveline states decreases
Solution Approach 1:
The system dynamically adjusts the set of viable driveline states based on current operating conditions and approval constraints. Rather than statically limiting available states, the method evaluates constraints in real-time and determines which states remain viable, allowing the system to adapt its state selection to current vehicle conditions while maintaining reliability.
Solution Approach 2:
The method changes the parameters defining state viability based on approval constraints. By adjusting which states are considered viable according to constraint satisfaction, the system maintains reliability while preserving adaptability within the constrained state space.
3Loss of energy
If frequent changes in optimal state are allowed to respond to varying conditions, then energy optimization improves, but system stability deteriorates
Solution Approach 1:
The method implements a stabilization mechanism that cushions against frequent state changes by comparing consecutive optimal state determinations. When the same optimal state is determined in successive evaluation cycles, the system consolidates this into a stable target state, preventing unnecessary transitions and providing stability while still allowing changes when truly optimal.
4Measurement precision
If comprehensive ranking of all kinematic chain states is performed, then selection precision improves, but computational time increases
Solution Approach 1:
The system performs preliminary ranking of kinematic chain states based on energy management laws before applying approval constraints. This preliminary action identifies the most promising states early, allowing the system to focus subsequent computational effort on evaluating only those states that are both energy-optimal and constraint-satisfied, rather than comprehensively evaluating all possible states.
Solution Approach 2:
The ranking process is segmented into multiple stages: initial energy-based ranking, followed by approval constraint filtering, and finally consolidation to determine the optimal state. This segmentation allows the system to achieve comprehensive selection precision through systematic evaluation while reducing computational time by processing states in ordered stages rather than evaluating all combinations simultaneously.
Data Source
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Figure 3
AI summary
A method for controlling an automatic transmission for a motor vehicle, the automatic transmission comprising at least two distinct kinematic chain states, the vehicle comprising an energy management law and adaptive gear ratio control, comprising the following steps: a ranking (Rank_dls_stt) of the kinematic chain states is determined in terms of emissions control efficiency and fuel consumption via the energy management law (LGE); a list (Driv_dls_avl) of viable kinematic chain states is determined from an amenity point of view via adaptive gear ratio control (ASC); an optimal kinematic chain state (Opt_dls) is determined corresponding to the eligible kinematic chain state in the list (Driv_dls_avl) of viable kinematic chain states and having priority in the sense of the ranking (Rank_dls_stt) of kinematic chain states,we consolidate the optimal kinematic chain state (Opt_dls) into a stable optimal target kinematic chain state (Tgt_dls) for the gearbox as a function of the target kinematic chain state of the gearbox. characterized in that it determines whether the optimal kinematic chain state (Opt_dls) changes, and that, when this is the case, it stores the previous optimal kinematic chain state (Opt_dls_old), the list (Driv_dls_avl_old) of kinematic chain states viable from an agreeable point of view before the change of the optimal kinematic chain state (Opt_dls), it determines whether the previous optimal kinematic chain state (Opt_dls_old) was eligible in the previous list of viable kinematic chain states (Driv_dls_avl_old) and is no longer eligible in the list of viable kinematic chain states (Driv_dls_avl),We also determine whether the optimal kinematic chain state was not eligible in the previous list of viable kinematic chain states (Driv_dls_avl_old) but is eligible in the list of viable kinematic chain states (Driv_dls_avl). If so, we determine that the change is due to a change in the list of viable kinematic chain states (Driv_dls_avl) and not to a change in the ranking (Rank_dls_stt) of the kinematic chain states. We assign a first value to the Boolean value (Driv_dls_cge) of the change in agreement and a second value to the Boolean value (Rank_dls_cge) of the change in ranking. If not, we assign a first value to the Boolean value (Rank_dls_cge) of the change in ranking and a second value to the Boolean value (Driv_dls_cge) of the change in agreement.