Road Gradient Estimation Arbitration Controller
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Solution Overview
Problem
Existing vehicle systems lack an efficient method to accurately estimate road gradient and vehicle mass across various conditions, which affects powertrain control, energy management, and stability control, particularly in micro-hybrid vehicles employing start/stop strategies.
Innovation Solution
A vehicle system with a controller that arbitrates between static, kinematic, and dynamic estimations of road gradient, using input from sensors like inertial sensors and navigation systems, to provide a comprehensive and accurate road gradient estimation and vehicle mass calculation, enabling improved fuel efficiency and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single estimation method is used for road gradient, then the system complexity is low, but the measurement precision deteriorates across different vehicle conditions
Solution Approach 1:
The patent segments the road gradient estimation problem into three distinct estimation methods (static, kinematic, and dynamic), each optimized for specific vehicle operating conditions. The controller selects among these segmented approaches based on current vehicle state, thereby achieving high measurement precision across diverse conditions without requiring a single overly complex unified method.
Solution Approach 2:
The system dynamically adapts the estimation method based on real-time vehicle conditions such as speed, acceleration availability, and operational state. This dynamic selection mechanism allows the system to maintain high measurement precision by choosing the most appropriate estimation approach for each specific condition, rather than using a fixed single-method approach.
2Measurement precision
If multiple estimation methods are used for road gradient, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The patent divides the estimation system into three specialized segments (static, kinematic, dynamic estimators), each designed for specific conditions. This segmentation allows each component to remain relatively simple while the collective system achieves high precision through conditional selection, rather than requiring one complex universal estimator.
Solution Approach 2:
The controller acts as an intermediary that arbitrates between multiple estimation methods based on vehicle conditions. This intermediary layer manages the complexity by implementing a decision logic that selects the most appropriate estimation approach, thereby enabling high measurement precision without exposing the full complexity of multiple methods to the rest of the system.
3Loss of energy
If engine shutdown is implemented in micro-hybrid vehicles, then fuel consumption is reduced, but brake pressure control complexity increases
Solution Approach 1:
The system performs preliminary estimation of road gradient and vehicle mass before engine shutdown occurs. By having this information available in advance, the brake control system can immediately adjust brake pressure to maintain vehicle position when the engine shuts down, without requiring complex real-time calculations during the transition. This preliminary action simplifies the overall brake pressure control complexity.
Solution Approach 2:
The brake system uses the estimated road gradient and vehicle mass parameters to automatically determine the required brake pressure for maintaining vehicle position during engine shutdown. This self-service capability allows the system to handle the increased complexity of brake pressure control internally, enabling fuel-efficient engine shutdown operation without requiring complex external intervention or control mechanisms.
Data Source
AI summary
A vehicle and vehicle system are provided with a controller that is configured to generate output indicative of a road gradient based on at least one of a first estimation, a second estimation and a third estimation. The road gradient is based on the first estimation when a vehicle speed is less than a speed threshold and an input indicative of a longitudinal acceleration is available. The road gradient is based on the second estimation when the vehicle speed is greater than the speed threshold and the longitudinal acceleration is available. The road gradient is based on the third estimation when the longitudinal acceleration is not available.