Battery Capacity Estimation From Partial-Charging Data Using Gaussian Process Regression

2017 ◽  
Author(s):  
Robert R. Richardson ◽  
Christoph R. Birkl ◽  
Michael A. Osborne ◽  
David A. Howey
Keyword(s):  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Diagnostic Technique ◽  
Lithium Ion ◽  
Constant Current ◽  
Time Data ◽  
Capacity Estimation ◽  
Voltage Range ◽  
Battery Capacity

Accurate on-board capacity estimation is of critical importance in lithium-ion battery applications. Battery charging/discharging often occurs under a constant current load, and hence voltage vs. time measurements under this condition may be accessible in practice. This paper presents a novel diagnostic technique, Gaussian Process regression for In-situ Capacity Estimation (GP-ICE), which is capable of estimating the battery capacity using voltage vs. time measurements over short periods of galvanostatic operation. The approach uses Gaussian process regression to map from voltage values at a selection of uniformly distributed times, to cell capacity. Unlike previous works, GP-ICE does not rely on interpreting the voltage-time data through the lens of Incremental Capacity (IC) or Differential Voltage (DV) analysis. This overcomes both the need to differentiate the voltage-time data (a process which amplifies measurement noise), and the requirement that the range of voltage measurements encompasses the peaks in the IC/DV curves. Rather, GP-ICE gives insight into which portions of the voltage range are most informative about the capacity for a particular cell. We apply GP-ICE to a dataset of 8 cells, which were aged by repeated application of an ARTEMIS urban drive cycle. Within certain voltage ranges, as little as 10 seconds of charge data is sufficient to enable capacity estimates with ∼ 2% RMSE.

scholarly journals Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries

2019 ◽  
Vol 15 (1) ◽  
pp. 127-138 ◽  
Author(s):  
Robert R. Richardson ◽  
Christoph R. Birkl ◽  
Michael A. Osborne ◽  
David A. Howey
Keyword(s):  
Gaussian Process ◽  
Lithium Ion Batteries ◽  
Gaussian Process Regression ◽  
Lithium Ion ◽  
Capacity Estimation

scholarly journals Research on Online Capacity Estimation of Power Battery Based on EKF-GPR Model

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Di Zhou ◽  
Hongtao Yin ◽  
Wei Xie ◽  
Ping Fu ◽  
Wenbin Lu
Keyword(s):  
Estimation Method ◽  
Gaussian Process Regression ◽  
Lithium Ion ◽  
Normal Operation ◽  
Constant Current ◽  
Capacity Estimation ◽  
Power Battery ◽  
Long Time ◽  
Aging Conditions ◽  
Constant Current Charge

Capacity degrading over repeated charge/discharge cycles is a main parameter for evaluating battery performance, which is commonly used for determining the state of health. However, it is difficult to measure the available capacity because it requires the normal operation to be terminated and a long time-consuming detection process. This study presents an online available-capacity estimation method by combining extended Kalman filter (EKF) with Gaussian process regression (GPR) for the daily partial charge data of lithium-ion batteries. First, GPR is used to establish an empirical model of the time-voltage curve in the constant current charge cases. Second, by analyzing the characteristics of the charge curve, the daily piecewise partially charge data are registered with the piecewise complete charge data to update GPR model and preestimate the equivalent complete charge time. On this basis, the equivalent complete charge time is refined by EKF. Furthermore, the available capacity estimation of the battery with constant current charge processes under different aging conditions is achieved. It is verified by experiments that the estimated error can be controlled within 5% when the actual available capacity is greater than 90% of the initial capacity.

scholarly journals SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 375 ◽  
Author(s):  
Jianfang Jia ◽  
Jianyu Liang ◽  
Yuanhao Shi ◽  
Jie Wen ◽  
Xiaoqiong Pang ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Lithium Ion Batteries ◽  
Gaussian Process Regression ◽  
Lithium Ion ◽  
Health Indicators ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Capacity Degradation ◽  
Battery Capacity ◽  
High Prediction

The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are two important factors which are normally predicted using the battery capacity. However, it is difficult to directly measure the capacity of lithium-ion batteries for online applications. In this paper, indirect health indicators (IHIs) are extracted from the curves of voltage, current, and temperature in the process of charging and discharging lithium-ion batteries, which respond to the battery capacity degradation process. A few reasonable indicators are selected as the inputs of SOH prediction by the grey relation analysis method. The short-term SOH prediction is carried out by combining the Gaussian process regression (GPR) method with probability predictions. Then, considering that there is a certain mapping relationship between SOH and RUL, three IHIs and the present SOH value are utilized to predict RUL of lithium-ion batteries through the GPR model. The results show that the proposed method has high prediction accuracy.

Human-knowledge-augmented Gaussian Process Regression for State-of-Health Prediction of Lithium-ion Batteries with Charging Curves

2021 ◽  
pp. 1-10
Author(s):  
Quan Zhou ◽  
Chongming Wang ◽  
Zeyu Sun ◽  
Ji Li ◽  
Huw Williams ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Lithium Ion Batteries ◽  
Gaussian Process Regression ◽  
Lithium Ion ◽  
Learning Performance ◽  
Transport Systems ◽  
State Of Health ◽  
Human Knowledge ◽  
Voltage Profile ◽  
Inference System

Abstract Lithium-ion batteries have been widely used in renewable energy storage and electrified transport systems, and State-of-Health (SoH) prediction is critical for safe and reliable operation of the lithium-ion batteries. Following the standard routine which predicts battery SoH based on charging curves, a human-knowledge-augmented Gaussian process regression (HAGPR) model is newly proposed for SoH prediction by incorporating two promising artificial intelligence techniques, i.e., the Gaussian process regression (GPR) and the adaptive neural fuzzy inference system (ANFIS). Based on human knowledge on voltage profile during battery degradation, a ANFIS is developed for feature extraction that helps improve machine learning performance and reduce the need of physical testing. Then, the ANFIS is integrated with a GPR model to enable SoH prediction with the extracted feature from battery aging test data. With a conventional GPR model as the baseline, a comparison study is conducted to demonstrate the advantage and robustness of the proposed HAGPR model. It indicates that the proposed HAGPR model can reduce at least 12% root mean square error with 31.8% less battery aging testing compared to the GPR model.

scholarly journals State-of-Health Estimate for the Lithium-Ion Battery Using Chi-Square and ELM-LSTM

2021 ◽  
Vol 12 (4) ◽  
pp. 228
Author(s):  
Jianfeng Jiang ◽  
Shaishai Zhao ◽  
Chaolong Zhang
Keyword(s):  
Neural Network ◽  
Lithium Ion Battery ◽  
Short Term Memory ◽  
Lithium Ion ◽  
Constant Current ◽  
State Of Health ◽  
Chi Square ◽  
Mean Temperature ◽  
Battery Capacity ◽  
Learning Machine

The state-of-health (SOH) estimation is of extreme importance for the performance maximization and upgrading of lithium-ion battery. This paper is concerned with neural-network-enabled battery SOH indication and estimation. The insight that motivates this work is that the chi-square of battery voltages of each constant current-constant voltage phrase and mean temperature could reflect the battery capacity loss effectively. An ensemble algorithm composed of extreme learning machine (ELM) and long short-term memory (LSTM) neural network is utilized to capture the underlying correspondence between the SOH, mean temperature and chi-square of battery voltages. NASA battery data and battery pack data are used to demonstrate the estimation procedures and performance of the proposed approach. The results show that the proposed approach can estimate the battery SOH accurately. Meanwhile, comparative experiments are designed to compare the proposed approach with the separate used method, and the proposed approach shows better estimation performance in the comparisons.

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