EV Energy Consumption Estimation Using Drive-Pattern Weighting
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Solution Overview
Problem
Current methods for estimating the present energy consumption of electrically propelled vehicles are inaccurate, leading to uncertainties in distance-to-empty estimates, which is a concern for electric vehicle drivers due to the sparsity of charging stations and longer charging times compared to combustion vehicles.
Innovation Solution
A method using a weighted moving average model that incorporates previous energy consumption values and drive pattern parameters, with modelled gain factors adjusted based on parameters like battery state of charge, terminal voltage, and temperature, to provide a more accurate estimation of present energy consumption, accounting for unpredictable factors such as air resistance and driver behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a weighted moving average model with modelled gain factors is used to estimate present energy consumption, then measurement precision of energy consumption estimation is improved, but device complexity increases
Solution Approach 1:
The gain factors are pre-calculated and stored in lookup tables based on drive pattern parameters (vehicle speed, acceleration, route characteristics) before runtime. During energy consumption estimation, the system simply retrieves these pre-computed factors rather than calculating them in real-time, significantly reducing computational complexity while maintaining high estimation accuracy
Solution Approach 2:
The patent replaces complex real-time physical modeling of energy consumption with a data-driven weighted moving average model that uses historical energy consumption data and drive pattern parameters. This substitution of physical models with statistical models reduces computational burden while improving accuracy by capturing actual vehicle behavior patterns
2Reliability
If long term energy efficiency is calculated over time to estimate distance-to-empty, then reliability of energy consumption data is improved, but loss of time increases
Solution Approach 1:
The system dynamically adjusts the weighting factors in the moving average model based on current drive pattern parameters and vehicle operating conditions. Recent energy consumption data receives higher weights when driving conditions are stable, while older data is weighted more heavily when conditions change rapidly, enabling the system to adapt quickly to changing conditions without requiring long calculation periods
Solution Approach 2:
The model continuously compares estimated energy consumption with actual measured values and adjusts the weighting factors and gain factors accordingly. This feedback mechanism allows the system to rapidly converge to accurate estimates even with limited historical data, reducing the time needed to achieve reliable distance-to-empty predictions
Data Source
AI summary
A method for estimating a present energy consumption of an electrically propelled vehicle powered by a propulsion battery. The method includes obtaining previous energy consumption values for a set of previous time instants, and a present drive pattern parameter value; estimating a present energy consumption based on a weighted moving average model fed with the energy consumption values, wherein, the weighted moving average model includes a modelled gain factor for each of at least a portion of the previous energy consumption values, where the modelled gain factors are modelled as a function of the drive pattern parameter.