Bayesian hierarchical modelling for battery lifetime early prediction

• URL: http://arxiv.org/abs/2211.05697v1
• Date: Thu, 10 Nov 2022 17:02:39 GMT
• Title: Bayesian hierarchical modelling for battery lifetime early prediction
• Authors: Zihao Zhou, David A. Howey
• Abstract summary: A hierarchical Bayesian linear model is proposed for battery life prediction. It combines both individual cell features (reflecting manufacturing variability) with population-wide features (reflecting the impact of cycling conditions on the population average) The model is able to predict end of life with a root mean square error of 3.2 days and mean absolute percentage error of 8.6%, measured through 5-fold cross-validation.
• Score: 1.84926694477846
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate prediction of battery health is essential for real-world system management and lab-based experiment design. However, building a life-prediction model from different cycling conditions is still a challenge. Large lifetime variability results from both cycling conditions and initial manufacturing variability, and this -- along with the limited experimental resources usually available for each cycling condition -- makes data-driven lifetime prediction challenging. Here, a hierarchical Bayesian linear model is proposed for battery life prediction, combining both individual cell features (reflecting manufacturing variability) with population-wide features (reflecting the impact of cycling conditions on the population average). The individual features were collected from the first 100 cycles of data, which is around 5-10% of lifetime. The model is able to predict end of life with a root mean square error of 3.2 days and mean absolute percentage error of 8.6%, measured through 5-fold cross-validation, overperforming the baseline (non-hierarchical) model by around 12-13%.

Related papers

• GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation [90.53485251837235]
  GIFT-Eval is a pioneering benchmark aimed at promoting evaluation across diverse datasets. GIFT-Eval encompasses 28 datasets over 144,000 time series and 177 million data points. We also provide a non-leaking pretraining dataset containing approximately 230 billion data points.
  arXiv  Detail & Related papers  (2024-10-14T11:29:38Z)
• Accurate battery lifetime prediction across diverse aging conditions with deep learning [20.832988614576983]
  Accurately predicting the lifetime of battery cells in early cycles holds tremendous value for battery research and development as well as numerous downstream applications. Here we introduce a universal deep learning approach that is capable of accommodating various aging conditions and facilitating effective learning under low-resource conditions. A benchmark is built for evaluation, encompassing 401 battery cells utilizing 5 prevalent electrode materials across 168 cycling conditions.
  arXiv  Detail & Related papers  (2023-10-08T07:25:27Z)
• Improve in-situ life prediction and classification performance by capturing both the present state and evolution rate of battery aging [0.0]
  This study develops a methodology by capturing both the battery aging state and degradation rate for improved life prediction performance. The aging state is indicated by six physical features of an equivalent circuit model that are extracted from the voltage relaxation data. And the degradation rate is captured by two features extracted from the differences between the voltage relaxation curves within a moving window.
  arXiv  Detail & Related papers  (2023-08-27T03:24:37Z)
• Predicting Battery Lifetime Under Varying Usage Conditions from Early Aging Data [3.739266290083215]
  We use capacity-voltage data in early life to predict the lifetime of cells cycled under widely varying charge rates, discharge rates, and depths of discharge. Our approach highlights the importance of using domain knowledge of lithium-ion battery degradation modes to inform feature engineering.
  arXiv  Detail & Related papers  (2023-07-17T10:42:21Z)
• Predicting Li-ion Battery Cycle Life with LSTM RNN [2.4738790490814213]
  This work trains a long short-term memory recurrent neural network model to learn from sequential data of discharge capacities at various cycles and voltages. Using experimental data of first 60 - 80 cycles, our model achieves promising prediction accuracy on test sets of around 80 samples.
  arXiv  Detail & Related papers  (2022-07-08T04:49:17Z)
• Interpretable Battery Cycle Life Range Prediction Using Early Degradation Data at Cell Level [0.8137198664755597]
  Quantile Regression Forest (QRF) model is introduced to make cycle life range prediction with uncertainty quantified. Data-driven methods have been proposed for point prediction of battery cycle life with minimum knowledge of the battery degradation mechanisms. The interpretability of the final QRF model is explored with two global model-agnostic methods.
  arXiv  Detail & Related papers  (2022-04-26T16:26:27Z)
• Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score [59.75318183140857]
  Estimating causal effects from randomized experiments is central to clinical research. Most methods for historical borrowing achieve reductions in variance by sacrificing strict type-I error rate control.
  arXiv  Detail & Related papers  (2020-12-17T21:10:10Z)
• Two-step penalised logistic regression for multi-omic data with an application to cardiometabolic syndrome [62.997667081978825]
  We implement a two-step approach to multi-omic logistic regression in which variable selection is performed on each layer separately. Our approach should be preferred if the goal is to select as many relevant predictors as possible. Our proposed approach allows us to identify features that characterise cardiometabolic syndrome at the molecular level.
  arXiv  Detail & Related papers  (2020-08-01T10:36:27Z)
• Individual Calibration with Randomized Forecasting [116.2086707626651]
  We show that calibration for individual samples is possible in the regression setup if the predictions are randomized. We design a training objective to enforce individual calibration and use it to train randomized regression functions.
  arXiv  Detail & Related papers  (2020-06-18T05:53:10Z)
• A General Framework for Survival Analysis and Multi-State Modelling [70.31153478610229]
  We use neural ordinary differential equations as a flexible and general method for estimating multi-state survival models. We show that our model exhibits state-of-the-art performance on popular survival data sets and demonstrate its efficacy in a multi-state setting.
  arXiv  Detail & Related papers  (2020-06-08T19:24:54Z)
• Survival Cluster Analysis [93.50540270973927]
  There is an unmet need in survival analysis for identifying subpopulations with distinct risk profiles. An approach that addresses this need is likely to improve characterization of individual outcomes.
  arXiv  Detail & Related papers  (2020-02-29T22:41:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.