Battery Degradation Estimation Using Hidden Markov Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for monitoring and predicting the degradation state of rechargeable batteries in electric vehicles are limited by the accuracy of measuring instruments, which are often configured for short-term monitoring rather than long-term degradation assessment, leading to uncertain and noisy data.
Innovation Solution
A method using a trained Hidden Markov Model (HMM) to improve the accuracy of battery degradation state estimation by analyzing time sequences of measured values, such as clamping voltage and discharge current, and accounting for uncertainties in the physical model and measuring technology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If measuring instruments configured for short-term monitoring are used, then device complexity and cost are reduced, but measurement precision deteriorates due to noise and uncertainty in long-term degradation assessment
Solution Approach 1:
A Hidden Markov Model (HMM) is introduced as an intermediary computational layer between the simple measuring instruments and the degradation state assessment. The HMM processes the noisy measurements from standard instruments, separating signal from noise through probabilistic modeling, thereby achieving high measurement precision without requiring complex specialized measuring equipment
Solution Approach 2:
The patent replaces complex physical measuring systems with a computational information processing system. Instead of using sophisticated hardware designed for long-term monitoring, the solution uses software-based HMM algorithms to process data from simple voltage and current sensors, substituting mechanical/physical complexity with informational complexity
2Measurement precision
If higher-quality measuring instruments are used, then measurement precision improves, but cost increases and device complexity increases
Solution Approach 1:
The patent employs inexpensive standard measuring instruments that would normally be suitable only for short-term monitoring. By combining these cheap instruments with computational post-processing via HMM, the system achieves long-term degradation assessment accuracy without the high cost of specialized long-term monitoring equipment
Solution Approach 2:
The HMM acts as a cost-effective intermediary that enhances the capabilities of inexpensive measuring instruments. Rather than investing in expensive specialized hardware, the system uses computational resources to bridge the gap between simple measurements and accurate degradation assessment
3Reliability
If standard measuring instruments are used, then device complexity is reduced, but reliability deteriorates due to uncertain and noisy measurement data
Solution Approach 1:
The HMM implements a feedback mechanism where measurement results are continuously refined through probabilistic modeling. The model uses past measurements and their uncertainties to inform current state assessments, creating a self-correcting system that improves reliability over time rather than degrading with accumulated noise
Solution Approach 2:
The computational HMM model serves as a reliability-enhancing intermediary that processes uncertain measurements from simple instruments. It separates reliable signal from noisy interference through statistical methods, achieving high reliability without requiring complex specialized measuring equipment
Data Source
AI summary
A method for ascertaining an approximation and/or a prognosis for the true degradation state of a rechargeable battery. The method includes: providing a time sequence of values of the degradation state ascertained using measuring technology for past points in time; providing a trained HMM, which indicates, as a function of the true degradation state, at which probability during the ascertainment using measuring technology which particular value of the degradation state is monitored, and at which probability the true degradation state is maintained for what length of time, and/or at which probability this true degradation state transitions to which worse degradation state in the next time step; from the monitored time sequence and the HMM, the most probable characteristic of the true degradation state in the past that is in agreement with the monitored time sequence is ascertained; the desired approximation and/or prognosis is evaluated based on the most probable characteristic.