Battery SOH Estimation Using Fleet Data and Vehicle Grouping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional battery state of health (SOH) estimation techniques are limited, as they require specific vehicle states or conditions, leading to incomplete SOH calculations for vehicles not meeting these criteria.
Innovation Solution
A system utilizing a big data server to process vehicle driving-related data, generate and store factors related to battery SOH, and a controller in the vehicle to calculate SOH by referencing these factors, allowing calculations regardless of specific vehicle states or conditions, using a layered cloud structure to group similar vehicles and assign weight values for reliable estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional SOH estimation techniques are used that require specific vehicle states or conditions, then calculation accuracy for vehicles meeting criteria is improved, but completeness of SOH coverage across all vehicles deteriorates
Solution Approach 1:
The system segments SOH estimation into two parts: vehicle-specific real-time calculation using preset algorithms, and fleet-wide pattern recognition using big data analysis. This segmentation allows each method to operate independently and complement each other, resolving the contradiction between accuracy for specific vehicles and coverage for all vehicles.
Solution Approach 2:
The big data server acts as an intermediary between individual vehicles and the SOH estimation system. It collects data from multiple vehicles, identifies common patterns and factors, and provides reference information back to individual vehicles. This intermediary enables vehicles not meeting preset conditions to still obtain accurate SOH estimates by referencing fleet-wide patterns.
2Adaptability or versatility
If big data collection and processing is implemented across the vehicle fleet, then SOH estimation completeness is improved, but system complexity increases
Solution Approach 1:
The big data server performs multiple functions: collecting vehicle data, processing data to identify patterns, storing reference information, and providing feedback to vehicles. By consolidating these functions into a single multi-functional system, the patent reduces overall complexity compared to having separate systems for each function.
Solution Approach 2:
The system performs preliminary data collection and pattern recognition in advance through the big data server, so that when individual vehicles need SOH estimation, the heavy processing work has already been completed. This preliminary action reduces real-time complexity for individual vehicle controllers.
3Reliability
If vehicle grouping based on similarity is implemented, then reliability of SOH estimation is improved, but data processing requirements increase
Solution Approach 1:
Instead of processing and comparing all possible vehicle data combinations, the system implements partial action by grouping vehicles into categories based on key similar characteristics. This partial grouping approach provides sufficient reliability for SOH estimation without requiring exhaustive data processing of every vehicle parameter.
Data Source
AI summary
A system for estimating a state of health (SOH) of a battery using big data, may include a big data server receiving vehicle driving-related data generated from a vehicle and a result of determining the SOH of the battery, processing the received vehicle driving-related data, and generating and storing a factor related to the SOH of the battery mounted in the vehicle; and a controller mounted in the vehicle and determining the SOH of the battery referring to the factor stored in the big data server.