Battery Health Estimation Using Operational Data Analysis
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Solution Overview
Problem
Existing methods for estimating the state of health of batteries in battery energy storage systems require manual capacity tests, causing system unavailability, stress to the battery, and are not adaptive to current operation conditions, leading to inaccurate and non-real-time monitoring of battery degradation.
Innovation Solution
A method that collects data from normal battery operation to estimate state of health using an Equivalent Circuit Model, minimizing differences between the model and measurements, allowing for continuous monitoring without interrupting system operation and accounting for deviations in battery performance over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual capacity tests are performed to determine state of health, then measurement precision is improved, but system availability deteriorates and productivity decreases
Solution Approach 1:
The system performs continuous state of health estimation during normal battery operation by processing measurement data from voltage, current, and temperature sensors. The estimation algorithm runs continuously without interrupting battery discharge/charge cycles, maintaining both system availability and measurement accuracy through real-time data analysis from ongoing operational measurements.
2Measurement precision
If manual capacity tests are performed to determine state of health, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system continuously accumulates and processes measurement data during normal operation to maintain up-to-date state of health estimates. By the time a capacity threshold is approached, the estimation is already complete and available, eliminating the need for separate pre-test measurement phases and avoiding downtime entirely.
3Manufacturing precision
If standard capacity tests are performed to determine state of health, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The battery management system automatically performs state of health estimation using its own operational measurement data from voltage, current, and temperature sensors during normal discharge/charge cycles. The system processes this data through an estimation algorithm to generate state of health values without requiring external testing equipment or manual intervention, thereby maintaining accuracy while reducing complexity.
4Ease of operation
If battery parameters are stored beforehand in BMS for estimation, then ease of operation is improved, but adaptability deteriorates
Solution Approach 1:
The system uses pre-stored battery parameters as initial values but continuously adapts the state of health estimation by processing real-time measurement data from voltage, current, and temperature sensors during actual operation. The estimation algorithm dynamically adjusts the state of health values based on observed operational behavior, allowing the system to maintain simplicity while adapting to specific usage patterns and degradation trajectories.
Data Source
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AI summary
In order to estimate states of health of batteries storing electrical energy significantly indicated by battery capacity and battery internal resistance, by which the estimation of states of health of batteries, deteriorated over time usage, is automated and carried out without a need of any specific test such as a capacity test and the usage of data out of normal operation, it is proposed to collect data (MDDC,ti, MDDV,ti, MDT,ti) from normal operation of a battery (B) storing electrical energy relating to battery-internal physical properties (PP) such as a terminal direct current IDC, a terminal direct voltage UDV and a battery cell temperature T due to battery measurements (BME) and - the determination or estimation, based on this data (MDDC,ti, MDDV,ti, MDT,ti) and a battery model (BMO, ECMO) , of a model parameter (ztp) by solving an optimization-/model parameter estimation-problem and minimizing a difference between the battery model (BMO, ECMO) and the battery measurements (BME). The estimation/determination of the model parameter is executed based or performed on batches of data during a time period (tp) in which the battery aging is negligible, e.g. duration of one day up to one week. By repeating the estimation over the lifetime, e.g. over numerous time periods (tp) with constant or variable time durations, a state-of-heath degradation can be monitored. The used battery model relates different battery parameters, technical features to battery internal states and the different measurable physical properties.