EV Charging State Analysis Using Electrical Sequences and TRNN
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Solution Overview
Problem
Existing methods for electric vehicle charging state monitoring cannot effectively determine if the charging state is normal or abnormal, leading to potential accidents such as battery leakage, charging short circuits, and electrical fires.
Innovation Solution
A charging state analysis method based on electrical characteristic sequence analysis using a Recurrent Neural Network (RNN) method, which involves obtaining voltage and current sampling data, calculating electrical characteristic vectors, and inputting them into a trained Time Recurrent Neural Network (TRNN) to determine the charging state as normal or abnormal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional temperature monitoring methods are used for electric vehicle charging, then the monitoring system is simple to implement, but the ability to detect abnormal charging states (such as battery leakage, charging short circuit, overvoltage charging) is insufficient
Solution Approach 1:
The patent replaces conventional temperature-based monitoring with an electrical characteristic-based monitoring system using Time Recurrent Neural Network (TRNN) to analyze voltage, current, and power sequences. This substitution enables detection of abnormal charging states through electrical parameter analysis rather than thermal monitoring, significantly improving detection accuracy for issues like battery leakage and charging short circuits while maintaining system feasibility
Solution Approach 2:
The patent introduces an intermediary computational layer (TRNN algorithm) that processes electrical characteristic sequences to identify abnormal charging states. This intermediary system bridges the gap between raw electrical measurements and diagnostic conclusions, enabling accurate detection of subtle anomalies without requiring direct physical intervention or overly complex hardware modifications
2Measurement precision
If electrical characteristic sequence analysis with TRNN is implemented, then the accuracy of charging state analysis is improved, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-training the Time Recurrent Neural Network model with historical charging data before actual monitoring deployment. This pre-training phase establishes the computational framework and parameter relationships in advance, allowing the system to achieve high measurement precision during actual operation without requiring complex real-time computations, thus reducing operational computational complexity
Solution Approach 2:
The patent segments the charging monitoring process into distinct phases: data collection (voltage, current, power sampling), sequence construction (organizing measurements into time-series), and analysis (TRNN processing). This segmentation allows each component to be optimized independently, managing computational complexity by breaking down the overall complex task into manageable stages with specific functions
Data Source
AI summary
A charging state analysis method of an electric vehicle based on electrical characteristic sequence analysis is provided. The method includes following steps: step S1, obtaining voltage sampling data and current sampling data of the electric vehicle during charging; step S2, setting a time interval, so as to divide the voltage sample data and the current sampling data obtained in step S1 into multiple data sets; step S3, calculating an electrical characteristic vector of each time interval; step S4. adding the calculation results of Step S3 to a temperature sensing value T, and generate an electrical characteristic sequence of whole charging cycle; step S5, inputting the electrical characteristic sequence of the electric vehicle into a trained TRNN in sequence to obtain corresponding results; if the result is 1, it is normal; if the result is 0, it is abnormal.