Battery Vehicle Range Estimation Using Weighted Energy Loss Models
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Solution Overview
Problem
Accurately predicting the range of battery-powered vehicles is challenging, especially in regions with limited charging infrastructure, as existing methods fail to account for various factors like friction, temperature, and air resistance, leading to unreliable energy consumption estimates.
Innovation Solution
A method and system that estimate energy consumption by calculating a weighted sum of energy loss due to friction, temperature control, and air resistance, using trip data, ambient temperature, and speed, while also considering kinetic energy and regenerative braking, to provide a more accurate range prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing range prediction methods are used, then the prediction process is simple, but the accuracy of energy consumption estimates is poor because they fail to account for friction, temperature, and air resistance
Solution Approach 1:
The patent segments the energy consumption calculation into distinct components: energy loss due to friction (proportional to distance), energy loss due to temperature control (proportional to temperature difference and time), and energy loss due to air resistance (proportional to speed). Each component is calculated separately using specific formulas and then summed to obtain the total energy consumption, thereby improving accuracy while maintaining manageable complexity
Solution Approach 2:
The patent introduces multiple physical parameters (friction coefficient, temperature difference, speed, distance, time) to transform the simple range prediction into a multi-parameter energy consumption model. By changing from a single-parameter estimation to a multi-parameter physical model, the accuracy of energy consumption estimation is significantly improved
2Reliability
If a comprehensive model accounting for multiple factors is used, then energy consumption estimation accuracy improves, but the computational complexity increases
Solution Approach 1:
The comprehensive energy consumption model is segmented into three independent calculation modules: friction loss calculation (E1 = k1 × d), temperature control loss calculation (E2 = k2 × ΔT × t), and air resistance loss calculation (E3 = k3 × v² × d). Each module processes specific inputs and produces a separate energy loss component, which are then summed to obtain the total energy consumption. This segmentation improves reliability by ensuring all factors are considered while managing computational complexity through modular structure
Solution Approach 2:
The patent transforms the range prediction problem into an energy consumption calculation problem by introducing multiple physical parameters (friction coefficient k1, temperature difference ΔT, speed v, distance d, time t). This parameter transformation enables a more reliable prediction model that accounts for real-world physical factors while maintaining computational tractability through well-defined mathematical relationships
Data Source
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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.