Electric Range Impact Factor Display Algorithms
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Solution Overview
Problem
Current systems for estimating the range per full charge (RPC) of electric vehicles do not effectively account for various factors impacting energy consumption, such as driving style, battery age, and ambient conditions, leading to inaccurate predictions.
Innovation Solution
A method that uses sensors and a controller to detect predefined conditions affecting energy consumption, outputting RPC and indicia to an interface, which are based on projected consumption rates learned during a predefined interval, including graphical elements showing energy consumption relative to the state of charge and distance traveled.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional RPC estimation methods are used, then the system is simple, but the prediction accuracy is poor because various factors impacting energy consumption are not effectively accounted for
Solution Approach 1:
The system segments the RPC estimation into multiple independent factor analyses: auxiliary load factor, propulsive factor due to driving style, propulsive factor due to battery age, and propulsive factor due to ambient conditions. Each factor is calculated separately using specific algorithms and then combined to produce the final RPC estimation, allowing for improved accuracy while maintaining manageable system complexity through modular design
Solution Approach 2:
The system performs preliminary learning during a predefined interval of the drive cycle to establish baseline consumption rates before calculating the final RPC. This preliminary action involves learning energy consumption patterns under current conditions and comparing them against nominal values, which prepares the system to provide more accurate real-time RPC predictions without requiring complex real-time calculations
2Measurement precision
If multiple factors are considered for RPC estimation, then the prediction accuracy improves, but the calculation complexity increases
Solution Approach 1:
The calculation process is divided into distinct segments: detecting presence of predefined conditions, calculating auxiliary load factor, calculating propulsive factors for driving style and battery age, calculating ambient condition factors, and combining these to determine RPC. Each segment has its own algorithm and data requirements, making the overall complex process manageable through systematic breakdown
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual energy consumption and comparing it against predicted consumption based on the various factors. This feedback is used to refine the learning process during the predefined interval and adjust the RPC estimation dynamically, improving accuracy while providing a structured approach to handling calculation complexity
Data Source
AI summary
A method is provided for estimating range per full charge (RPC) for a vehicle. The method includes a controller which may, in response to detecting presence of a predefined condition impacting vehicle energy consumption, output to an interface by a controller a RPC and indicia indicative of an extent to which the predefined condition is affecting the RPC. An electrified vehicle including one or more vehicle components, a traction battery to supply energy to the vehicle components, one or more sensors, and a controller is also provided. The one or more sensors monitor the vehicle components, traction battery, and preselected ambient conditions. The controller is configured to, in response to input from the sensors, generate output for an interface which includes a RPC and indicia indicative of an extent of impact on the RPC by each of the ambient conditions and operation of the components and battery.