EV State-of-Charge Estimation Using Bluetooth Trip Detection
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Solution Overview
Problem
Estimating the state of charge (SoC) of electric vehicle batteries between charging sessions is challenging due to the difficulty in determining the distance traveled by the vehicle, which affects accurate SoC estimation and efficient power allocation during charging.
Innovation Solution
A system using Bluetooth identification and location information from user devices to estimate the distance traveled by the electric vehicle, incorporating GPS and machine learning models to determine the SoC, and allowing for semi-automatic vehicle selection and charging control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Bluetooth identification and location information are used to estimate distance traveled, then SoC estimation accuracy is improved, but device complexity increases
Solution Approach 1:
The patent uses Bluetooth identification as an intermediary to track vehicle location and distance traveled. Instead of directly measuring SoC, the system mediates through Bluetooth device detection, location tracking, and distance calculation to infer SoC changes, thereby improving estimation accuracy while managing system complexity through modular data collection
2Measurement precision
If GPS and machine learning models are incorporated to determine SoC, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the SoC estimation process into distinct functional modules: GPS location tracking, Bluetooth device identification, distance calculation, and machine learning-based SoC prediction. Each module handles a specific aspect of the measurement, improving overall precision while allowing independent optimization and maintenance of each component
Solution Approach 2:
The system performs preliminary data collection and processing by continuously tracking GPS locations and Bluetooth device connections before needing to estimate SoC. Historical travel distance data is accumulated and pre-processed, enabling more accurate SoC predictions without requiring complex real-time calculations when charging decisions are made
3Ease of operation
If semi-automatic vehicle selection and charging control are implemented, then ease of operation is improved, but extent of automation decreases
Solution Approach 1:
The system implements partial automation where the machine learning model automatically predicts SoC and suggests charging actions, but the user retains final control over charging decisions. This partial automation approach provides convenient estimates and recommendations while allowing users to override or adjust settings, balancing ease of operation with user control
Data Source
AI summary
Techniques for estimating a vehicle state of charge (“SoC”) are disclosed. A computing device may determine a first instance of connectivity between the computing device and an electric vehicle and determine a first location of the computing device associated with the first instance of connectivity. The computing device may detect completion of a first trip of the electric vehicle, wherein the first trip reflects movement of the electric vehicle from the first location to a second location, and determine a first distance traveled by the electric vehicle during the first trip based on the first location and the second location. The computing device may select the electric vehicle for charging using the determined first instance of connectivity between the computing device and the electric vehicle, estimate a SoC of the selected electric vehicle using the determined first distance, and transmit the estimated SoC to a charging station.