Cloud Battery Management for Multi-Vehicle State Estimation
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Solution Overview
Problem
Conventional battery management systems (BMS) face challenges in accurately estimating battery state and predicting lifespan or abnormal phenomena due to reliance on limited data from individual vehicles and hardware limitations, leading to simplistic SOC estimation algorithms and difficulty in long-term data collection.
Innovation Solution
A cloud server device and battery management system that collects and stores battery information, using pre-registered service commands to create resulting information for state estimation, including a framework module for determining conditions and executing service commands, enabling more accurate state estimation of battery health and charging status.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional BMS relies only on data collected from individual vehicles with preloaded algorithms, then the system can operate independently with simple hardware, but it cannot high-dimensionally estimate battery state or ameliorate battery lifespan and abnormal phenomena
Solution Approach 1:
The patent transitions from single-vehicle data collection to multi-vehicle cloud-based data aggregation, adding a spatial dimension to data collection. The cloud server collects battery information from multiple vehicles, enabling high-dimensional analysis that cannot be achieved with individual vehicle data alone.
Solution Approach 2:
The cloud server acts as an intermediary between individual BMS systems and the final state estimation. It aggregates data from multiple vehicles, applies complex algorithms, and returns results to individual BMS systems, enabling advanced estimation without requiring complex hardware in each vehicle.
2Measurement precision
If conventional BMS uses simple current integration and open-circuit voltage table algorithms, then the system can estimate SOC with limited computational resources, but it cannot achieve accurate long-term battery state prediction or lifespan estimation
Solution Approach 1:
The cloud server serves as a computational intermediary, executing complex AI-based algorithms and statistical analyses that would be too resource-intensive for embedded BMS hardware. It processes aggregated data from multiple vehicles and provides refined state estimates back to individual systems.
Solution Approach 2:
The patent replaces simple mechanical/current integration algorithms with AI-based learning models and statistical methodologies. These advanced algorithms are executed on the cloud server, substituting the limitations of embedded board computational capabilities with cloud-based processing power.
3Duration of action of moving object
If conventional BMS is equipped with embedded board hardware, then the system can collect and process data locally, but hardware limitations prevent long-term data collection and application of statistical methodologies
Solution Approach 1:
The patent adds a cloud-based storage and processing dimension to the system architecture. Instead of relying on limited embedded board storage, the cloud server provides virtually unlimited data retention capacity, enabling long-term collection and analysis of battery information from multiple vehicles.
Data Source
AI summary
Provided is a battery management system including: a battery management device, disposed on a mobile vehicle, for measuring battery information from a battery; a cloud server device for collecting the battery information from the battery management device and storing the battery information, and for, based on determination condition and service command of pre-registered battery management service, if it is determined that there exists battery information matching the determination condition, controlling a resulting information to be created from the extracted information in accordance with the service command; and a terminal device for receiving and outputting the resulting information.