Fault prognosis of battery system based on accurate voltage abnormity prognosis using long short-term memory neural networks

Hong, Jichao; Wang, Zhenpo; Yao, Yongtao

doi:10.1016/j.apenergy.2019.113381

Cited by 252 publications

(95 citation statements)

References 25 publications

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“…The concept of the deep-learning-enabled fault prognosis method was introduced by Hong et al [56] where long short-term memory (LSTM) recurrent neural network (LSTM-RNN) was used for multi-forward-step voltage prediction to determine the advent of battery faults and mitigate runaway risk. To ensure the prediction accuracy and model robustness, a high volume of real-world operational data of an electric taxi was used.…”

Section: Ann-based Fault Diagnosis Methodsmentioning

confidence: 99%

Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review

2021

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Fault detection/diagnosis has become a crucial function of the battery management system (BMS) due to the increasing application of lithium-ion batteries (LIBs) in highly sophisticated and high-power applications to ensure the safe and reliable operation of the system. The application of Machine Learning (ML) in the BMS of LIB has long been adopted for efficient, reliable, accurate prediction of several important states of LIB such as state of charge, state of health and remaining useful life. Inspired by some of the promising features of ML-based techniques over the conventional LIB fault detection/diagnosis methods such as model-based, knowledge-based and signal processing-based techniques, ML-based data-driven methods have been a prime research focus in the last few years. This paper provides a comprehensive review exclusively on the state-of-the-art ML-based data-driven fault detection/diagnosis techniques to provide a ready reference and direction to the research community aiming towards developing an accurate, reliable, adaptive and easy to implement fault diagnosis strategy for the LIB system. Current issues of existing strategies and future challenges of LIB fault diagnosis are also explained for better understanding and guidance.

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Section: Ann-based Fault Diagnosis Methodsmentioning

confidence: 99%

Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review

2021

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“…If the pack SOC is lower than max min 1 ( ) ss −−, the weights and bias can be calculated according to (31 In the next step, the SOC estimation will be conducted based on the LSTM model and the proposed algorithm.…”

Section: B Pack Soc Estimationmentioning

confidence: 99%

Stage of Charge Estimation of Lithium-Ion Battery Packs Based on Improved Cubature Kalman Filter With Long Short-Term Memory Model

Shu

Zhang

et al. 2021

IEEE Trans. Transp. Electrific.

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Accurate estimation of state of charge (SOC) of lithium-ion battery packs remains challenging due to inconsistencies among battery cells. To achieve precise SOC estimation of battery packs, firstly, a long short-term memory (LSTM) recurrent neural network (RNN)-based model is constructed to characterize the battery electrical performance, and a rolling learning method is proposed to update the model parameters for improving the model accuracy. Then, an improved square root-cubature Kalman filter (SRCKF) is designed together with the multi-innovation technique to estimate battery cell's SOC. Next, to cope with inconsistencies among battery cells, the SOC estimation value from the maximum and minimum cells are combined with a smoothing method to estimate the pack SOC. The robustness and accuracy of the proposed battery model and cell SOC estimation method are verified by exerting the experimental validation under time-varying temperature conditions. Finally, real operation data are collected from an electric-scooter (ES) monitoring platform to further validate the generalization of the designed pack SOC estimation algorithm. The experimental results manifest that the SOC estimation error can be limited within 2% after convergence.

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“…A backpropagation neural network (BPNN) is deployed in [119] to predict the current and thus to diagnose the external short-circuit fault of the battery. The long short-term memory recurrent neural network (LSTM) is used to predict battery voltage for early fault warning [120].…”

Section: H Battery Management Systemmentioning

confidence: 99%

AI-powered Energy Internet Towards Carbon Neutrality: Challenges and Opportunities

Li¹

2021

Preprint

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From self-driving vehicles, voice recognition based virtual digital assistants, smart thermostats to recommendation systems, Artificial Intelligence (AI) is becoming a crucial part of the carbon neutral society that has drawn considerable interest from energy supply firms, startups, technology developers, financial institutions, national governments and the academic community. The emergence of AI initiates numerous opportunities to transform energy industry to AI-powered smart system which can revolutionize traditional approaches of creativity thinking, strategical operation, and solution seeking, especially for accelerating carbon neutrality of our society. This survey provides a comprehensive overview of fundamental principles that underpin applications of big data analysis in Energy Internet (EI), such as smart energy supply and consumption, smart health and Fintech. Next, we focus on intelligent decision-making for the energy industry and inform the state-of-the-art by thoroughly reviewing the literature. Subsequently, cybersecurity issues for AI system related to EI are discussed with recent advancements from vulnerability analysis of AI system to differential privacy and to blockchain based security technology. To our knowledge, this is one of the first academic, peer-reviewed works to provide a systematic review of AI applications for EI research and initiatives in terms of big data analysis, intelligent decision-making and AI related cybersecurity These initiatives were systematically classified into different groups according to the field of application, methodology and contribution Afterwards, potential challenges, limitations for existing research and opportunities for future directions are discussed, ranging from emerging explainable AI, to localized multi-energy marketplaces, self-driving electric vehicle charging and e-mobility. This paper can help us understand how to build smart cities and critical infrastructure for a climate-changed world towards the UN’s sustainable development goals.

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Fault prognosis of battery system based on accurate voltage abnormity prognosis using long short-term memory neural networks

Cited by 252 publications

References 25 publications

Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review

Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review

Stage of Charge Estimation of Lithium-Ion Battery Packs Based on Improved Cubature Kalman Filter With Long Short-Term Memory Model

AI-powered Energy Internet Towards Carbon Neutrality: Challenges and Opportunities

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