Sealed EV Battery Pack SOH Prediction via BMS Cell Diagnostics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
There is a lack of methods for diagnosing the state of health and predicting the remaining lifetime of used electric vehicle batteries without opening the battery pack, which is necessary for reusing the batteries in energy storage applications.
Innovation Solution
A diagnostic device that communicates with the internal battery monitoring system (BMS) of the EV battery, allowing it to gather performance data, determine the state of health, and predict the remaining lifetime of the battery by comparing the data to stored information from deployed second-life batteries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the battery pack is opened to access cells or modules for sorting and reuse, then cell-level diagnostic information can be obtained, but the complexity of the process increases and the battery pack cannot be reused as a complete system
Solution Approach 1:
The patent uses the Battery Management System (BMS) as an intermediary to access cell-level diagnostic information without physical disassembly. The BMS provides electrical and data access to individual cells through existing communication interfaces, allowing diagnostic data extraction while the battery pack remains sealed and intact.
Solution Approach 2:
The patent replaces the mechanical disassembly approach with an electrical and software-based diagnostic system. Instead of physically opening the pack and measuring cells with external equipment, the system uses electronic communication protocols to extract diagnostic information through the BMS, eliminating the need for mechanical intervention.
2Reliability
If the battery pack is disassembled to sort cells based on parameters, then cells can be matched for reuse, but the time and labor required increases significantly
Solution Approach 1:
The patent performs preliminary diagnostic assessment of all cells through the BMS before any physical handling or sorting is required. By obtaining complete diagnostic information (voltage, impedance, temperature, cycle history) electronically in advance, the system enables rapid decision-making about cell suitability without time-consuming manual measurements during the sorting process.
Solution Approach 2:
The system uses feedback from the BMS to automatically identify cells that meet predetermined benchmarks for reuse. The diagnostic data flows back to the sorting system, which automatically determines which cells are suitable for second-life applications, eliminating manual inspection and reducing sorting time while maintaining reliability.
3Device complexity
If average battery pack level state of health is used, then the assessment is simpler, but it fails to identify individual cells at risk of failure
Solution Approach 1:
The patent segments the battery pack assessment into individual cell-level evaluations while maintaining overall pack-level analysis. The BMS provides diagnostic data for each cell separately, allowing the system to identify specific cells that deviate from normal parameters and are at risk of failure, rather than treating the pack as a homogeneous unit.
Solution Approach 2:
The patent applies local quality assessment by evaluating each cell's specific diagnostic parameters (voltage, impedance, temperature, cycle count) individually. This allows identification of local issues within specific cells that would be masked by average pack-level metrics, enabling targeted monitoring and predictive maintenance for at-risk cells.
Data Source
AI summary
Understanding a health status and expected remaining lifetime of an EV (electric vehicle) battery is important before repurposing the battery for second life applications. A device for connecting to an unopened EV battery pack via operable coupling to signal and power wiring is disclosed. The device enables access to diagnostic information from the unopened EV battery. The device measures cell and/or module voltages and currents within the battery pack for several different depths of discharge. A self-learning algorithm implemented by the diagnostic device, which uses historical data and diagnostic information from the battery pack, determines a condition of the battery and provide recommended operational conditions for future use of the battery. For example, a degradation slope and expected capacity loss over time can be determined based on measured variations of cell and/or modular voltages and subsequently used, with cell impedance data, to recommend an operational C-rate for the battery pack.