Route Energy Consumption Estimation Using Physical Models
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Solution Overview
Problem
Current methods lack an efficient way to predict energy consumption for vehicles, especially when historical driving data is unavailable or unreliable, and fail to adapt to changing driving habits and ambient conditions.
Innovation Solution
A system that uses a combination of historical, vehicle, external, and road segment information to predict energy consumption by employing physical models, such as speed and stop prediction models, and incorporating road segment attributes like speed limits and elevation, to generate an energy consumption profile for vehicles traveling along specific routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical models are used to predict energy consumption, then prediction accuracy is improved when historical data is unavailable, but system complexity increases due to multiple input requirements
Solution Approach 1:
The energy consumption prediction system is designed to function with multiple types of input data (historical driving data, vehicle parameters, road segment information, ambient conditions). When historical data is unavailable, the system universally switches to using physical models with alternative inputs, maintaining prediction capability across different data availability scenarios without requiring separate systems.
Solution Approach 2:
The system dynamically changes the input parameters based on data availability. When historical driving data is unavailable, it transitions to using vehicle parameters (mass, drag coefficient, rolling resistance), road segment information (elevation, distance, speed limits), and ambient conditions (temperature, pressure) as alternative parameters for the physical models, thereby maintaining prediction accuracy through parameter substitution.
2Adaptability or versatility
If multiple data sources are integrated for prediction, then adaptability to changing conditions is improved, but information processing requirements increase
Solution Approach 1:
The prediction system segments information processing into distinct modules: historical data processing, vehicle parameter processing, road segment information processing, and ambient condition processing. Each module handles specific types of data independently, then results are integrated. This segmentation reduces the cognitive load on the system by breaking down complex multi-source data integration into manageable discrete processing tasks.
Solution Approach 2:
The system performs preliminary processing of each data source separately before integration. Vehicle parameters are pre-processed and stored, road segment information is pre-calculated (elevation profiles, distance segments), and ambient conditions are pre-filtered. This preliminary action reduces the complexity of real-time integration by having data ready in standardized formats before the prediction calculation begins.
Data Source
AI summary
This disclosure generally relates to a system, apparatus, method, and process for generating a route based energy consumption estimation based on physical models. More particularly, the disclosure describes the generation of the route based energy consumption estimation based on physical models for a vehicle traveling on a specific road segment. The energy consumption estimation may be based on information related to historical energy consumption information for the vehicle, external information that may affect energy consumption by the vehicle, vehicle systems information that may affect energy consumption by the vehicle, and/or selected road segment information that may be used to predict energy consumption by the vehicle.