Vehicle Braking Parameter Tuning With AI Simulation Feedback
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Solution Overview
Problem
Existing methods for tuning functional parameters of vehicle braking systems, such as IPB and ESP, face limitations due to resource constraints, inconsistent working conditions, and the lack of objective standards, making it difficult to optimize parameters across multiple performance dimensions effectively.
Innovation Solution
A simulation-based optimization method and system that utilizes a simulation environment to set goals and parameters, employs AI algorithms to evaluate braking performances, and iteratively adjusts parameters until optimal values are achieved, incorporating user preferences to balance performance dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real vehicle testing is used for parameter tuning, then measurement data can be obtained directly, but vehicle resources and test sites are restricted and costs increase
Solution Approach 1:
The patent creates a virtual copy of the vehicle braking system through high-fidelity simulation models that replicate real vehicle behavior. These digital twins allow comprehensive parameter testing without consuming physical vehicle resources, resolving the contradiction between measurement accuracy and resource availability by providing unlimited virtual testing instances.
Solution Approach 2:
The patent replaces the mechanical real-vehicle testing system with a computational simulation system. By substituting physical testing infrastructure with software-based simulation environments, the system eliminates restrictions on vehicle and test site availability while maintaining measurement capability through virtual sensor data generation.
2Ease of operation
If manual tuning by application engineers is used, then parameter adjustments can be made based on experience, but execution consistency of working conditions is low and objective standards are lacking
Solution Approach 1:
The patent systematically varies braking system parameters within defined ranges and boundaries during simulation experiments. This structured parameter exploration replaces ad-hoc manual adjustments with a methodical approach that ensures consistent execution of testing conditions while maintaining the flexibility to optimize across multiple performance dimensions.
Solution Approach 2:
The patent implements automated feedback loops where simulation results are evaluated against objective performance criteria, and parameter adjustments are automatically generated based on performance gaps. This closed-loop system eliminates human subjectivity and ensures consistent execution of tuning procedures while preserving operational flexibility through adaptive parameter modification.
3Manufacturing precision
If extensive parameter tuning is performed to achieve balance across multiple performance dimensions, then optimization quality improves, but time consumption increases due to iterative testing
Solution Approach 1:
The patent performs preliminary simulation experiments to establish performance models and identify critical parameter relationships before final optimization. This pre-analysis phase reduces the dimensionality of the optimization problem, allowing comprehensive multi-dimensional parameter balancing to be achieved with fewer iterative cycles and reduced time consumption.
Solution Approach 2:
The patent implements continuous optimization processes where simulation and evaluation run seamlessly in automated sequences without manual intervention between iterations. This continuous action approach maintains high optimization quality across multiple performance dimensions while minimizing idle time and accelerating the overall tuning process through uninterrupted computational workflows.
Data Source
AI summary
An optimization method and an optimization system for vehicle braking system parameters is disclosed. The method includes (i) performing a setting step that includes setting a goal for optimization of the vehicle braking system parameters and determining vehicle braking system parameters to be optimized, and setting working conditions according to the vehicle braking parameters to be optimized, (ii) performing a simulation step that includes establishing a simulation environment and simulating the set working conditions, (iii) performing a calculating step that includes, for each set of vehicle braking system parameters, extracting vehicle signals associated with braking performances from a simulation result, and calculating evaluation values of the braking performances of each set of vehicle braking system parameters based on the vehicle signals and the goal, (iv) performing a judging step that includes judging whether the evaluation values of the braking performances reach the goal; if no, adjusting the vehicle braking system parameters and repeatedly iterating the simulation step and the calculating step until the goal is reached; if yes, performing an output step that includes outputting the vehicle brake system parameters and the corresponding evaluation values. The simulation environment can be used to replace a real vehicle, and the simulation efficiency is improved.