A review on methods for state of health forecasting of lithium-ion batteries applicable in real-world operational conditions

Bülow, Friedrich von; Meisen, Tobias

doi:10.1016/j.est.2022.105978

Cited by 25 publications

(11 citation statements)

References 147 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In real-world polyester industrial processes, building a time series prediction model for new production lines or equipment with new sensors can be challenging due to a lack of historical data [15]. The time-series data collected from sensors cross-production-line often exhibit varying distributions, leading to reduced efficiency of previously trained models [16].…”

Section: Introductionmentioning

confidence: 99%

Multi-level deep domain adaptive adversarial model based on tensor-train decomposition for industrial time series forecasting

Yang,

Peng,

Chen

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

The polyester industry is a complex process industry, building a time series prediction model for new production lines or equipment with new sensors can be challenging due to a lack of historical data. The time-series data collected from sensors cross-production-line often exhibit varying distributions. Current domain adaptation (DA) approaches in data-driven time series forecasting primarily concentrate on adjusting either the features or the models, neglecting the intricacies of industrial time series data. Furthermore, constructing deep neural networks for industrial data necessitates substantial computational resources and runtime due to their large and high-dimensional nature. In order to tackle these obstacles, we propose a novel Multi-level deep domain adaptive adversarial model based on tensor-train decomposition (TT-MDAM). Our model aims to strike a dynamic balance between prediction accuracy and runtime efficiency. By integrating multiple perspectives at the feature, trend, and model levels, we leverage DA to enhance the prediction accuracy of our model in the target domain. Additionally, by analyzing the weight matrix of the neural network, we generate a low-rank model to improve operational efficiency. The application of the proposed TT-MDAM approach to both the three-phase flow facility process (TPFF) dataset and a real-world polyester esterification process dataset reveals promising results, outperforming state-of-the-art methodologies in terms of prediction performance. The results indicate that the approach provides a viable solution for building time series prediction models in industrial processes with new equipment or production lines.

show abstract

Section: Introductionmentioning

confidence: 99%

Multi-level deep domain adaptive adversarial model based on tensor-train decomposition for industrial time series forecasting

Yang,

Peng,

Chen

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…For BEV users, owners, and fleet operators, the state of health (SOH) of the batteries is one of their main concerns, because it reflects the aging of the battery depending on its usage and environmental conditions [4][5][6]. The task of determining the current SOH with the battery data available at the current point in time is called SOH estimation [7][8][9]. When the battery ages, the SOH decreases.…”

Section: Introductionmentioning

confidence: 99%

“…Modeling this change of the SOH from a current SOH to a future SOH due to aging causes is called SOH forecasting. These aging causes are encoded in the battery operational load through parameters like state of charge (SOC), temperature, and current [9]. SOH forecasting is also called "battery aging prediction" [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…Forecasting the future SOH enables assessments of the future residual value of the battery and planning of battery replacements. It also may help to derive operational strategies for BEV fleets as one of the proposed applications [9]. Until now, only a few forecasting methods have been applicable in real-world vehicle operation [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…It also may help to derive operational strategies for BEV fleets as one of the proposed applications [9]. Until now, only a few forecasting methods have been applicable in real-world vehicle operation [9,10]. Apart from the transferability of models in the case of new battery types or battery operation, also the black box behavior of most machine learning (ML) models, especially of deep learning (DL) models, is problematic: When running in prediction mode, only the model's input and the model's prediction are available-without any further explanation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Transparent and Interpretable State of Health Forecasting of Lithium-Ion Batteries with Deep Learning and Saliency Maps

von Bülow,

Hahn,

Meyes

et al. 2023

International Journal of Energy Research

Self Cite

View full text Add to dashboard Cite

Batteries are the most expensive component of battery electric vehicles (BEVs), but they degrade over time and battery operation. State of health (SOH) forecasting models learn how battery operation over long-time periods of weeks or months influences battery aging. Currently, existing methods for SOH forecasting of lithium-ion batteries based on deep neural network (DNN) models lack explainability of their forecasts due to their inherent black box character. However, the explainability of forecasts is essential to build user trust into the forecasting models. In this work, we address this problem from two perspectives: First, we compared four machine learning (ML) models like decision tree and random forest, which are inherently transparent, to two new DNN architectures with a more inherent black box character. Second, we proposed a new method using Gaussian-filtered saliency maps to visualize battery operational states that are relevant to DNN models. This method is applied to the best DNN models previously trained. We used an extensive data corpus consisting of five public data sets with different operational conditions, battery types, and aging trajectories. Furthermore, we show that the Gaussian-filtered saliency maps meaningfully visualize battery operational states that are consistent with findings from controlled laboratory aging experiments. Thus, this work was able to add transparency and interpretability to the SOH forecasting results of two state-of-the-art DNNs, while maintaining their superior performance compared to transparent ML models, while mitigating their inherent black box character.

show abstract

Accurate state of health estimation for lithium-ion batteries under random charging scenarios

Shen

Shu

et al. 2023

Energy

View full text Add to dashboard Cite

A review on methods for state of health forecasting of lithium-ion batteries applicable in real-world operational conditions

Cited by 25 publications

References 147 publications

Multi-level deep domain adaptive adversarial model based on tensor-train decomposition for industrial time series forecasting

Multi-level deep domain adaptive adversarial model based on tensor-train decomposition for industrial time series forecasting

Transparent and Interpretable State of Health Forecasting of Lithium-Ion Batteries with Deep Learning and Saliency Maps

Accurate state of health estimation for lithium-ion batteries under random charging scenarios

Contact Info

Product

Resources

About