Commercial Vehicle Control Unit Route Adaptation
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Solution Overview
Problem
Current vehicle operating strategies in commercial vehicles, such as city buses, are largely driver-dependent and not optimized for specific route profiles, leading to inefficiencies in operating costs and emissions due to individual driving styles and deviations from optimal strategies.
Innovation Solution
A method that uses existing vehicle measurement technology and control units to record and analyze driving operation variables, applying a learning algorithm to identify route sections and provide optimized operating strategy instructions for cost- and consumption-reduced driving, integrating these instructions into the vehicle's control system for automatic or driver-assisted interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If driver-dependent operating strategies are used, then individual driving freedom and adaptability are maintained, but operating costs and emissions increase due to suboptimal driving styles
Solution Approach 1:
The control unit continuously receives measured driving operation variables from measurement technology, compares them against stored route profiles and optimized operating strategies, and provides feedback to guide the driver toward more efficient driving behaviors while preserving driver autonomy
Solution Approach 2:
The control unit acts as an intermediary between the driver and the vehicle systems, translating measured driving variables into optimized operating strategy recommendations that reduce energy consumption without directly controlling the vehicle
2Loss of energy
If route-specific optimized operating strategies are implemented, then energy consumption and emissions are reduced, but system complexity increases due to learning algorithms and data processing
Solution Approach 1:
The control unit performs multiple functions: it stores route profiles, processes measured driving variables, executes learning algorithms, and generates operating strategy recommendations, consolidating these capabilities into a single multi-functional device rather than requiring separate systems for each function
Solution Approach 2:
The system uses the vehicle's existing measurement technology to provide the data needed for optimization, and the control unit automatically learns and adapts to specific routes through embedded learning algorithms without requiring external intervention or complex additional hardware
3Productivity
If measurement technology is added to capture driving variables, then operating strategy optimization is enabled, but production costs increase
Solution Approach 1:
The patent merges the control unit with existing vehicle systems and integrates measurement technology that is already present in modern vehicles, combining multiple functions into existing components rather than adding separate dedicated systems for each function
Data Source
Figure 1
AI summary
The invention relates to a method for operating a vehicle, in particular a commercial vehicle, with a control unit and with measurement technology that records current driving parameters supplied to the control unit, wherein the vehicle is always used and driven on the same route or at least repeatedly on the same route with the same route profile. According to the invention, driving parameters are assigned and stored in the control unit by a learning algorithm to the route profile of this repeatedly driven route. During each journey, the control unit recognizes, based on the temporal profiles of the currently recorded driving parameters and a comparison with the route-related, learned and stored driving parameters, on which current section of the route and with which route profile the vehicle is currently located (1, 2, 3).The control unit contains operating strategy instructions for cost-optimized driving and/or obtains and stores them through a learning algorithm, which are issued for intervention in the vehicle's driving operation when a current section of the route is detected (5, 6, 7), preferably taking into account and reflecting the route profile of the detected section.