Battery State of Health Estimation via Proxy Tests
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Solution Overview
Problem
Existing methods for evaluating the State of Health (SOH) of electric vehicle batteries are invasive and difficult for owners to perform under real-world conditions, requiring strict and often unachievable conditions for reliable results.
Innovation Solution
A method using a system with sensors and a test module that performs proxy tests, which are less invasive than full tests, to estimate the SOH by collecting data on battery and environmental parameters, and employs Bayesian calculations to estimate the battery's health without the need for a full discharge-charge cycle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a full discharge-charge cycle is performed to determine SOH, then measurement precision is improved, but battery degradation increases and operation complexity worsens
Solution Approach 1:
The patent applies partial action by performing only a partial discharge-charge cycle (e.g., 20-80% state of charge range) instead of a complete 0-100% cycle. This partial cycling provides sufficient data for SOH estimation through Bayesian inference while significantly reducing the harmful degradation effects associated with full discharge-charge operations.
Solution Approach 2:
The patent introduces Bayesian inference as an intermediary computational method that bridges the gap between partial test data and accurate SOH estimation. The Bayesian algorithm processes incomplete measurements from partial cycles and combines them with prior knowledge to produce reliable SOH estimates, eliminating the need for complete discharge-charge cycles.
2Measurement precision
If a full discharge-charge cycle is performed to determine SOH, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent enables owners to perform simpler partial discharge-charge cycles within normal operating ranges (e.g., 20-80% SOC) rather than requiring strict full cycling conditions. This makes the test much easier to perform under real-world conditions while the Bayesian inference compensates for the reduced test completeness to maintain measurement precision.
Solution Approach 2:
The patent uses Bayesian inference to create a virtual model of battery health that copies the information obtainable from full tests but derives it from easier partial tests. The algorithm replicates the SOH estimation function using less demanding measurement conditions.
3Ease of operation
If proxy tests with partial charge-discharge are used, then ease of operation is improved and battery degradation is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent implements feedback through Bayesian inference that continuously updates SOH estimates by combining prior knowledge with new measurements from partial tests. The algorithm processes the limited data from proxy tests and iteratively refines the SOH estimation, maintaining precision despite the reduced test intensity.
Solution Approach 2:
The patent changes the measurement parameters from complete discharge-charge cycles to partial cycles with specific state of charge ranges. By carefully selecting the partial cycling parameters and combining them with Bayesian statistical methods, the system maintains measurement precision while improving ease of operation and reducing battery stress.
Data Source
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AI summary
The invention relates to a method for evaluating a state of health (SOH) of an electric battery of an electric vehicle, said method comprising a step of: - performing on said electric battery, under given conditions, a test, called proxy test, said proxy test including a partial charge and/or discharge action of the battery, - comparing the results of the proxy test with data stored in a dataset, and - estimating the SOH in function of the comparison result.