Cycle life test optimization for different Li-ion power battery formulations using a hybrid remaining-useful-life prediction method

Ma, Jian; Xu, Shujie; Shang, Pengchao; Ding, Yu; Qin, Wei-Li; Cheng, Yujie; Lu, Chen; Su, Yuzhuan; Jia, Chong; Jin, Hao; Lin, Yongshou

doi:10.1016/j.apenergy.2020.114490

Cited by 43 publications

(14 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Comparing the field of RBS and fixed battery topology designs, we have found extensive applications of state estimation in the latter one, while the former one has very limited applications. The models built for cells include the fusion models such as the recurrent neural networks for parameterisation of the electrochemical model for real-time model updating and battery state estimation, the SoC model for battery considering electrochemical parameters in the P2D model and vehicle dynamics [13,14], second-order RC model, adaptive H-infinity filter algorithm and particle swarm optimisation for SoC and SoH estimation [15], extracting the health indicator during the partial discharge process and using this as input in long shortterm memory (LSTM) networks [16], extracting the health indicator during the fast charging process and using this as an indicator in LSTM combined with transfer learning [16,17] etc. From these works, it is clear that battery modelling has evolved over the years, and the data-driven methods comprising AI combined with fewer experiments and electrochemical/ equivalent circuit models are becoming a more popular approach to leverage the advantages of each of them [18].…”

Section: Introductionmentioning

confidence: 99%

Reconfigurable battery systems: Challenges and safety solutions using intelligent system framework based on digital twins

Garg

Mou

et al. 2022

IET Collab Intel Manufact

View full text Add to dashboard Cite

Research on Reconfigurable Battery Systems (RBS) is gaining emphasis over the traditional fixed topology of the battery pack due to its advantages of adapting flexible topology (series‐parallel) during its operation in the pack for meeting the non‐linear time‐dependent load requirements. There could emerge serious issues such as those related to safety due to malfunction of the switching circuit, heat generation from switches during frequent switching of circuits, charging temperature rise, increased charging time, sensing issues arising from the use of low‐accuracy voltage/current sensors, state of charge/state of health estimation, and cost issues due to the use of increasing number of switches, fuses, contactors, relays, circuit breakers etc. To address these mentioned issues, the problem of optimal switching circuit topology for RBS is formulated as a mathematical multi‐objective optimisation problem. An intelligent system framework based on digital twins is proposed. The proposed framework is further extended to a life cycle management approach that includes the interactions among pack design, pack assembly and operational and recycling levels. This could provide greater access of real‐time big data cloud storage to the battery designers, manufacturers and recycling industries, who can make use of it to optimise their designs, systems and operations.

show abstract

Section: Introductionmentioning

confidence: 99%

Reconfigurable battery systems: Challenges and safety solutions using intelligent system framework based on digital twins

Garg

Mou

et al. 2022

IET Collab Intel Manufact

View full text Add to dashboard Cite

show abstract

“…Generally, when the capacity drops to 80% of the initial value, the battery reaches the end of its service life [11]. The remaining useful lifetime (RUL) is defined as the amount of operation time in a certain application until the battery reaches the predefined end-of-life criterion; it represents the period from the present observation to the end of life (EOL) [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Overview of Machine Learning Methods for Lithium-Ion Battery Remaining Useful Lifetime Prediction

et al. 2021

View full text Add to dashboard Cite

Lithium-ion batteries play an indispensable role, from portable electronic devices to electric vehicles and home storage systems. Even though they are characterized by superior performance than most other storage technologies, their lifetime is not unlimited and has to be predicted to ensure the economic viability of the battery application. Furthermore, to ensure the optimal battery system operation, the remaining useful lifetime (RUL) prediction has become an essential feature of modern battery management systems (BMSs). Thus, the prediction of RUL of Lithium-ion batteries has become a hot topic for both industry and academia. The purpose of this work is to review, classify, and compare different machine learning (ML)-based methods for the prediction of the RUL of Lithium-ion batteries. First, this article summarizes and classifies various Lithium-ion battery RUL estimation methods that have been proposed in recent years. Secondly, an innovative method was selected for evaluation and compared in terms of accuracy and complexity. DNN is more suitable for RUL prediction due to its strong independent learning ability and generalization ability. In addition, the challenges and prospects of BMS and RUL prediction research are also put forward. Finally, the development of various methods is summarized.

show abstract

“…ese can extract abstract information and features from massive datasets while building and discovering complex functional and temporal relationships from the data [9]. Deep learning approaches have been implemented in a great variety of systems for prognostics purposes, such as lithium-ion batteries state of health (SOH) and state of charge (SOC) estimation, [10][11][12][13], RUL estimation in rolling bearings [14][15][16], and turbofan engines [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Estimation through Deep Learning Partial Differential Equation Models: A Framework for Degradation Dynamics Interpretation Using Latent Variables

Cofre-Martel

Droguett

Modarres

2021

Shock and Vibration

View full text Add to dashboard Cite

Remaining useful life (RUL) estimation is one of the main objectives of prognostics and health management (PHM) frameworks. For the past decade, researchers have explored the application of deep learning (DL) regression algorithms to predict the system’s health state behavior based on sensor readings from the monitoring system. Although the state-of-art results have been achieved in benchmark problems, most DL-PHM algorithms are treated as black-box functions, giving little-to-no control over data interpretation. This becomes an issue when the models unknowingly break the governing laws of physics when no constraints are imposed. The latest research efforts have focused on applying complex DL models to achieve low prediction errors rather than studying how they interpret the data’s behavior and the system itself. This paper proposes an open-box approach using a deep neural network framework to explore the physics of a complex system’s degradation through partial differential equations (PDEs). This proposed framework is an attempt to bridge the gap between statistic-based PHM and physics-based PHM. The framework has three stages, and it aims to discover the health state of the system through a latent variable while still providing a RUL estimation. Results show that the latent variable can capture the failure modes of the system. A latent space representation can also be used as a health state estimator through a random forest classifier with up to a 90% performance on new unseen data.

show abstract

Cycle life test optimization for different Li-ion power battery formulations using a hybrid remaining-useful-life prediction method

Cited by 43 publications

References 37 publications

Reconfigurable battery systems: Challenges and safety solutions using intelligent system framework based on digital twins

Reconfigurable battery systems: Challenges and safety solutions using intelligent system framework based on digital twins

Overview of Machine Learning Methods for Lithium-Ion Battery Remaining Useful Lifetime Prediction

Remaining Useful Life Estimation through Deep Learning Partial Differential Equation Models: A Framework for Degradation Dynamics Interpretation Using Latent Variables

Contact Info

Product

Resources

About