Battery Vehicle Range Estimation Using Trip Energy Weighting
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Solution Overview
Problem
Existing battery-powered vehicles lack accurate methods to predict driving range, especially in regions with limited charging infrastructure, and hybrid vehicles need efficient battery power range estimation for optimal operation.
Innovation Solution
A method and system for estimating energy consumption in battery-powered vehicles by analyzing trip data to determine energy loss due to friction, temperature control, and air resistance, using weighted sums and incorporating regenerative braking, with data collection and simulation capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional range estimation methods are used in battery-powered vehicles, then the vehicles can operate with simple systems, but the range prediction accuracy deteriorates especially in regions with limited charging infrastructure
Solution Approach 1:
The energy consumption calculation is segmented into multiple distinct components: energy loss due to vehicle friction (based on distance), energy loss due to temperature control (based on temperature difference from optimal), and energy loss due to air resistance (based on speed). Each component is calculated separately using specific trip data parameters and then aggregated to provide comprehensive range estimation.
Solution Approach 2:
The system incorporates regenerative braking energy recovery as a feedback mechanism that reduces overall energy consumption. By capturing and reusing energy during deceleration events, the system improves range prediction accuracy while accounting for energy that would otherwise be lost, creating a self-correcting energy balance calculation.
2Measurement precision
If detailed trip data analysis is performed to improve energy consumption accuracy, then range estimation improves, but data processing time and computational resources increase
Solution Approach 1:
The system pre-identifies and categorizes trip data into essential parameters (geographic positions for distance, timestamps for duration, temperature data, speed data) before performing energy consumption calculations. This preliminary organization of data structures enables efficient processing during the actual energy calculation phase without requiring complex real-time analysis.
Solution Approach 2:
The system extracts only the necessary trip data parameters needed for energy consumption calculation (distance, duration, temperature, speed) from the complete set of available vehicle data. By selectively extracting and processing only relevant parameters, the system achieves accurate energy consumption estimates while minimizing data processing time and computational resource requirements.
Data Source
AI summary
Systems, methods, devices, and models for range estimation and analysis in battery powered vehicles are described. Energy consumption over vehicle trips is collected, to evaluate weighting factors for a weighted sum. The weighted sum is evaluated based on determined weighting factors and expected trip data, to determine an energy consumption of a trip or trips of a vehicle. Determined energy consumption for trips is used for evaluating suitability of the vehicle for performing the trips.