Battery SOC-OCV Profiling for Degradation-Aware BMS Control
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Solution Overview
Problem
The state of charge-open circuit voltage (SOC-OCV) profile in manganese-rich nickel-cobalt-manganese (NCM) batteries changes due to degradation, leading to inaccuracies in battery management systems (BMS) control algorithms.
Innovation Solution
A battery management apparatus and method that estimates a second SOC-OCV profile at the middle-of-life (MOL) point by identifying boundary voltages and applying different capacity degradation rates to a first SOC-OCV profile generated at the beginning-of-life (BOL) point, using low-rate discharge data and dQ/dV profiles to correct for compositional element-specific degradation in NCM batteries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single capacity degradation rate is applied to the entire SOC-OCV profile, then the estimation process is simplified, but the accuracy deteriorates due to different degradation rates in upper and lower voltage sections
Solution Approach 1:
The SOC-OCV profile is divided into upper and lower sections based on boundary voltage. Different capacity degradation rates are applied to each section: a first degradation rate for the upper section and a second degradation rate for the lower section. This segmentation resolves the contradiction by maintaining estimation accuracy through differentiated treatment while keeping the process manageable through clear segmentation criteria.
Solution Approach 2:
Different degradation characteristics are recognized for different voltage regions. The upper voltage section exhibits different degradation behavior compared to the lower voltage section. By applying locally appropriate degradation rates to each section rather than a uniform rate, the estimation accuracy is improved while accounting for the heterogeneous nature of battery degradation across different operating regions.
2Ease of operation
If degradation is not accounted for in the SOC-OCV profile, then the system operation is simpler, but the control algorithm accuracy deteriorates due to profile changes from degradation
Solution Approach 1:
The system pre-establishes multiple SOC-OCV profiles corresponding to different degradation stages (beginning-of-life, middle-of-life, end-of-life). During operation, the appropriate profile is selected based on the battery's degradation state, allowing the control algorithm to maintain high accuracy without complex real-time degradation modeling. This preliminary preparation resolves the contradiction by enabling accurate control while keeping operational complexity low.
Solution Approach 2:
The system changes the SOC-OCV profile parameters based on degradation stage. By maintaining multiple profiles with different characteristics and selecting the appropriate one based on degradation level, the system adapts to degradation without requiring complex real-time adjustments, thus maintaining accuracy while preserving operational simplicity.
3Measurement precision
If multiple degradation points are measured and analyzed, then the SOC-OCV profile estimation accuracy is improved, but the measurement and data processing time increases
Solution Approach 1:
Battery data is collected and analyzed at multiple predetermined degradation points (beginning-of-life, middle-of-life, end-of-life) during battery aging. SOC-OCV profiles are pre-established at each degradation point based on measurements taken at that specific time. This preliminary action allows the system to maintain high estimation accuracy by using degradation-appropriate profiles while avoiding time-consuming real-time analysis during normal operation, as the profiles are prepared in advance.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Accurately estimates the SOC-OCV profile at MOL, improving the precision of BMS control algorithms and addressing the changes caused by cell degradation in Mn-rich NCM batteries.
Implementation Method 1
The battery data includes low-rate discharge data measured through low-rate discharges of about 0.1 C or less at the multiple degradation points
Implementation Method 2
The upper voltage degradation characteristic is determined based on capacity degradation caused by a redox reaction of nickel and cobalt, and the lower voltage degradation characteristic is determined based on capacity manifestation caused by a redox reaction of manganese
Data Source
AI summary
A battery management apparatus includes an interface configured to acquire battery data of a management target battery measured at multiple degradation points, and a controller. The controller is configured to: generate a first state of charge-open circuit voltage (SOC-OCV) profile at a beginning-of-life (BOL) point and voltage-capacity profiles of the management target battery at the multiple degradation points, based on the battery data; identify a boundary voltage that distinguishes an upper voltage degradation characteristic with an upper capacity degradation rate and a lower voltage degradation characteristic with a lower capacity degradation rate, based on the voltage-capacity profiles; and estimating a second SOC-OCV profile at a middle-of-life (MOL) point, based on the upper capacity degradation rate, the lower capacity degradation rate, and the first SOC-OCV profile.