Cloud BMS Fault Diagnosis Using Triggered High-Frequency Battery Data
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Solution Overview
Problem
Current battery fault diagnosis methods in electric vehicles rely on limited data from vehicle battery management systems (BMS), leading to inaccurate fault diagnosis and potential safety risks.
Innovation Solution
A cloud-based battery management system (BMS) collects and analyzes more comprehensive data sets from vehicles to perform preliminary and deep diagnostics, enhancing fault detection accuracy through increased data volume and frequency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data collection from vehicle BMS is limited to standard parameters, then data transmission burden is reduced, but fault diagnosis accuracy deteriorates
Solution Approach 1:
The patent segments the data collection process into two stages: preliminary diagnosis using standard BMS parameters (first original data set) and deep diagnosis using expanded parameters (second original data set). This segmentation allows the system to transmit minimal data during normal operation while collecting comprehensive data only when needed, resolving the contradiction between data volume and diagnosis accuracy
Solution Approach 2:
The system performs preliminary fault screening using standard BMS data before triggering deep diagnosis. This preliminary action filters out normal cases that don't require extensive data collection, ensuring that large volumes of detailed data are only collected when actually necessary for accurate diagnosis
2Measurement precision
If comprehensive data sets are collected for deep diagnosis, then fault diagnosis accuracy is improved, but data transmission and processing burden increases
Solution Approach 1:
The patent divides the diagnostic system into cloud-based data processing and vehicle-based execution. The cloud server handles complex data analysis and model training, while the vehicle BMS only needs to collect and transmit data, significantly reducing the complexity burden on the vehicle system
Solution Approach 2:
The patent introduces an intermediary layer (cloud server) between the vehicle BMS and the diagnostic analysis. This intermediary handles the complex processing of comprehensive data sets, allowing the vehicle system to remain relatively simple while still achieving high-accuracy diagnosis through cloud-based analysis
3Reliability
If standard BMS data is used for fault diagnosis, then system implementation is simplified, but diagnostic reliability deteriorates
Solution Approach 1:
The system performs preliminary diagnosis using standard BMS data to identify cases requiring deep diagnosis. This preliminary screening ensures that complex data collection is only activated when necessary for reliable diagnosis, maintaining simplicity for normal operations while ensuring reliability when needed
Solution Approach 2:
The patent dynamically changes the parameter set based on diagnostic needs: using standard parameters for preliminary diagnosis and expanding to comprehensive parameters for deep diagnosis. This adaptive parameter selection maintains reliability by using appropriate data depth while managing system complexity
Data Source
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AI summary
A battery fault diagnosis method and apparatus are provided. The method includes: A vehicle uploads a collected first original data set to a cloud BMS. The cloud BMS preliminarily diagnoses a battery based on the first original data set and sends a risk warning to the vehicle based on the preliminary diagnosis for the battery The vehicle obtains a second original data set based on the received risk warning. The second original data set is obtained from the vehicle through collection. Data items included in the second original data set are more than data items included in the first original data set or a data collection frequency of the second original data set is higher than a data collection frequency of the first original data set. The vehicle and/or the cloud BMS deeply diagnose/diagnoses the battery based on the second original data set, to determine a fault level of the battery When it is determined through the preliminary diagnosis for the battery that the battery has a fault risk, the vehicle is deeply diagnosed based on richer data, to more accurately determine the fault level of the battery