Battery SoX Diagnosis Using a Unified Electrochemical Model
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Solution Overview
Problem
Current battery management systems face challenges in accurately diagnosing the State-of-Charge (SoC), State-of-Health (SoH), State-of-Energy (SoE), State-of-Power (SoP), and State-of-Safety (SoS) of batteries due to limitations in traditional models that lack physicochemical consistency, leading to inadequate monitoring and potential unsafe operating conditions.
Innovation Solution
A computer-implemented method using a single underlying physicochemically consistent model that processes data from both DC and AC domains with identical model parameters, incorporating mechanistic, machine learning, and artificial intelligence methods to diagnose SoX values and update the battery model, ensuring accurate state estimation and degradation mechanism identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional battery management systems use simple voltage and current measurement models, then the device complexity is reduced and ease of operation is improved, but the measurement precision and reliability of battery state diagnosis deteriorate
Solution Approach 1:
The patent transforms the battery diagnosis approach by changing from simple voltage-current parameters to a comprehensive set of parameters including impedance spectrum data, electrochemical model parameters, and degradation indicators. This parameter transformation enables precise measurement of battery states (SoC, SoH, SoP, SoF, SoS) by incorporating impedance spectroscopy and electrochemical models that capture the underlying physics of battery degradation mechanisms.
2Difficulty of detecting and measuring
If traditional battery management systems use simple measurement models, then the device complexity is reduced, but the ability to detect and diagnose degradation mechanisms deteriorates
Solution Approach 1:
The patent introduces an intermediary electrochemical model that acts as a bridge between simple voltage-current measurements and complex degradation mechanisms. The model includes intermediate parameters such as impedance spectrum characteristics, reaction kinetics parameters, and transport parameters that mediate the relationship between measurable electrical quantities and underlying degradation processes, enabling detection of SEI growth, lithium plating, and other degradation mechanisms.
Solution Approach 2:
The patent replaces traditional empirical measurement models with a physics-based electrochemical model that incorporates fundamental electrochemical principles. This substitution enables the system to detect and diagnose degradation mechanisms by modeling the actual electrochemical processes occurring in the battery, including charge transfer reactions, ion transport, and electrode degradation, rather than relying on simple electrical measurements.
3Reliability
If a single physicochemically consistent model is used for both DC and AC domain processing, then the manufacturing precision and model reliability are improved, but the device complexity increases
Solution Approach 1:
The patent implements a universal electrochemical model that serves multiple functions: it processes both DC and AC domain data, estimates various battery states (SoC, SoH, SoP, SoF, SoS), and characterizes degradation mechanisms. The model's multi-functionality is achieved through a unified mathematical framework that can handle different input types (voltage, current, impedance) and produce comprehensive diagnostic outputs, eliminating the need for separate models for different measurement domains.
Data Source
AI summary
A computer-implemented method of diagnosing one or more SoXs, such as State-of-Charge (SoC), State-of-Health (SoH), State-of-Energy (SoE), State-of-Power (SoP), State-of-Function (SoF) and State-of-Safety (SoS), of at least one battery, comprises a SoX diagnostics loop, which includes a given battery model, and a model update loop, which is configured to update the battery model, the method comprising:receiving, by the SoX diagnostics loop from at least one sensor, at least one measured battery parameter of the battery,determining, by the SoX diagnostics loop, at least one SoX descriptor of the battery using at least one of the following: at least one SoX parameter, at least one comparison of the at least one SoX parameter: to its previous value, to the at least one another SoX parameter, to the at least one threshold value, wherein the at least one SoX parameter is one of the following: the at least one measured battery parameter, at least one simulated battery parameter provided by the battery model, the at least one state variable and at least one model parameter of the battery model,determining, by the SoX diagnostics loop, at least one SoX value associated with the SoX in dependence on the at least one of the following: SoX descriptor and SoX parameter,determining, by the SoX diagnostics loop, based on at least one of the following: the at least one SoX parameter, the at least one SoX descriptor and the at least one SoX value, whether an update is to be carried out on the model,in response to determining that the update is to be carried out, updating, by the model update loop, the battery model, andproviding, by the model update loop, the updated battery model to the SoX diagnostics loop.