Hybrid Neural Networks for Enhanced Predictions of Remaining Useful Life in Lithium-Ion Batteries

With the proliferation of electric vehicles (EVs) and the consequential increase in EV battery circulation, the need for accurate assessments of battery health and remaining useful life (RUL) is paramount, driven by environmentally friendly and sustainable goals. This study addresses this pressing concern by employing data-driven methods, specifically harnessing deep learning techniques to enhance RUL estimation for lithium-ion batteries (LIB). Leveraging the Toyota Research Institute Dataset, consisting of 124 lithium-ion batteries cycled to failure and encompassing key metrics such as capacity, temperature, resistance, and discharge time, our analysis substantially improves RUL prediction accuracy. Notably, the convolutional long short-term memory deep neural network (CLDNN) model and the transformer LSTM (temporal transformer) model have emerged as standout remaining useful life (RUL) predictors. The CLDNN model, in particular, achieved a remarkable mean absolute error (MAE) of 84.012 and a mean absolute percentage error (MAPE) of 25.676. Similarly, the temporal transformer model exhibited a notable performance, with an MAE of 85.134 and a MAPE of 28.7932. These impressive results were achieved by applying Bayesian hyperparameter optimization, further enhancing the accuracy of predictive methods. These models were bench-marked against existing approaches, demonstrating superior results with an improvement in MAPE ranging from 4.01% to 7.12%

Lithium-ion battery digitalization: Combining physics-based models and machine learning

Digitalization of lithium-ion batteries can significantly advance the performance improvement of lithium-ion batteries by enabling smarter controlling strategies during operation and reducing risk and expenses in the design and development phase. Accurate physics-based models play a crucial role in the digitalization of lithium-ion batteries by providing an in-depth understanding of the system. Unfortunately, the high accuracy comes at the cost of increased computational cost preventing the employment of these models in real-time applications and for parametric design. Machine learning models have emerged as powerful tools that are increasingly being used in lithium-ion battery studies. Hybrid models can be developed by integrating physics-based models and machine learning algorithms providing high accuracy as well as computational efficiency. Therefore, this paper presents a comprehensive review of the current trends in integration of physics-based models and machine learning algorithms to accelerate the digitalization of lithium-ion batteries. Firstly, the current direction in explicit modeling methods and machine learning algorithms used in battery research are reviewed. Then a thorough investigation of contemporary hybrid models is presented addressing both battery design and development as well as real-time monitoring and control. The objective of this work is to provide details of hybrid methods including the various applications, type of employed models and machine learning algorithms, the architecture of hybrid models, and the outcome of the proposed models. The challenges and research gaps are discussed aiming to provide inspiration for future works in this field

Critical review on improved electrochemical impedance spectroscopy-cuckoo search-elman neural network modeling methods for whole-life-cycle health state estimation of lithium-ion battery energy storage systems.

Efficient and accurate health state estimation is crucial for lithium-ion battery (LIB) performance monitoring and economic evaluation. Effectively estimating the health state of LIBs online is the key but is also the most difficult task for energy storage systems. With high adaptability and applicability advantages, battery health state estimation based on data-driven techniques has attracted extensive attention from researchers around the world. Artificial neural network (ANN)-based methods are often used for state estimations of LIBs. As one of the ANN methods, the Elman neural network (ENN) model has been improved to estimate the battery state more efficiently and accurately. In this paper, an improved ENN estimation method based on electrochemical impedance spectroscopy (EIS) and cuckoo search (CS) is established as the EIS-CS-ENN model to estimate the health state of LIBs. Also, the paper conducts a critical review of various ANN models against the EIS-CS-ENN model. This demonstrates that the EIS-CS-ENN model outperforms other models. The review also proves that, under the same conditions, selecting appropriate health indicators (HIs) according to the mathematical modeling ability and state requirements are the keys in estimating the health state efficiently. In the calculation process, several evaluation indicators are adopted to analyze and compare the modeling accuracy with other existing methods. Through the analysis of the evaluation results and the selection of HIs, conclusions and suggestions are put forward. Also, the robustness of the EIS-CS-ENN model for the health state estimation of LIBs is verified

Li-Ion Battery State of Health Estimation based on an Improved Single Particle Model

Health-conscious battery management is one of the main facilitators for widespread commercialization of Li-ion batteries as the primary power source in electrified transportation and portable electronics and as the backup source in stationary energy storage systems. The majority of the existing Battery Management Systems (BMSs) define battery State of Health (SOH) in terms of internal resistance increase or battery capacity decay and use various open-loop criteria based on the battery cycle number and/or operating conditions to determine its SOH. However, considering the wide range of operating conditions and current profiles for Li-ion batteries, the use of a closed-loop SOH estimation approach based on the measureable quantities of the battery along with a battery model is of great importance. In this work, the battery internal resistance increase which can be attributed to various chemical and mechanical degradation mechanisms is considered as the measure of the battery SOH. In order to estimate the SOH, a modified reduced-order electrochemical model based on the Single Particle (SP) Li-ion battery model is proposed to improve the traditional SP model accuracy. This model not only incorporates an analytical expression for the electrolyte-phase potential difference, it is also capable of accurately predicting the battery performance over a wide range of operating currents by considering the effects of the unmodeled dynamics. Finally, this model integrated with an adaptive output-injection observer to estimate the SP model states and the output model uncertainties, can be used to estimate the internal resistance increase during the battery lifetime. The modeling and estimation results are validated via a comparison to the full-order electrochemical model simulations

Development and management of advanced batteries via additive manufacturing and modeling

The applications of Li-ion batteries require higher energy and power densities, improved safety, and sophisticated battery management systems. To satisfy these demands, as battery performances depend on the network of constituent materials, it is necessary to optimize the electrode structure. Simultaneously, the states of the battery have to be accurately estimated and controlled to maintain a durable condition of the battery system. For those purposes, this research focused on the innovation of 3D electrode via additive manufacturing, and the development of fast and accurate physical based models to predict the battery status for control purposes. Paper I proposed a novel 3D structure electrode, which exhibits both high areal and specific capacity, overcoming the trade-off between the two of the conventional batteries. Paper II proposed a macro-micro-controlled Li-ion 3D battery electrode. The battery structure is controlled by electric fields at the particle level to increase the aspect ratio and then improve battery performance. Paper III introduced a 3D model to simulate the electrode structure. The effect of electrode thickness and solid phase volume fraction were systematically studied. Paper IV proposed a low-order battery model that incorporates stress-enhanced diffusion and electrolyte physic into a Single Particle model that addresses the challenges of battery modeling for BMS: accuracy and computational efficiency. Paper V proposed a single particle-based degradation model by including Solid Electrolyte Interface (SEI) layer formation coupled with crack propagation. Paper VI introduced a single-particle-based degradation model by considering the dissolution of active materials and the Li-ion loss due to SEI layer formation with crack propagation for LiMn₂O₄/Graphite battery --Abstract, page iv

Advanced battery modeling for interfacial phenomena and optimal charging

Lithium-ion batteries are one of the most promising energy storage systems for portable devices, transportation, and renewable grids. To meet the increasing requirements of these applications, higher energy density and areal capacity, long cycle life, fast charging rate and enhanced safety for lithium-ion battery (LIBs) are urgently needed. To solve these challenges, the relevant physics at different length scale need to be understood. However, experimental study is time consuming and limited in small scale’s study. Modeling techniques provide us powerful tools to get a deep understanding of the relevant physics and find optimal solutions. This work focuses on studying the mechanism in advanced battery engineering techniques and developing a new charging algorithm by model-based optimization. The research topics are divided into six topics and each topic is reported as a form of journal publication. Paper Ⅰ provides a new aspect of how ALD coating improves the lithium-ion diffusion at electrode particles. Paper Ⅱ explains the mechanisms by which 3D electrodes enhance battery performance and reveals guidelines for optimized 3D electrode designs by a 3D electrochemical-mechanical battery model. Paper Ⅲ investigates the electrolyte concentration impact on SEI layer growth and Li plating, especially under high charge rates. Paper Ⅳ proposes an optimized charging protocol for fast charging for reducing the charging time with minimal degradation. Paper Ⅴ reports a comprehensive degradation model for degradation estimation and life predication of energy storage system (ESS). Paper Ⅵ is a study of temperature-dependent state of charge (SOC) estimation for battery pack -Abstract, p. i