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Data-Based Interpretable Modeling for Property Forecasting and Sensitivity Analysis of Li-ion Battery Electrode

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Automotive Innovation 2022年第2期5卷 121-133页

作者： kailong liu Qiao Peng Kang Li Tao Chen WMG The University of WarwickCoventryUK Queen’s University Belfast BelfastUK School of Electronic and Electric Engineering University of LeedsLeedsUK School of Electrical Engineering Southeast UniversityNanjingChina

Lithium-ion batteries have become one of the most promising technologies for speeding up clean automotive applications,where electrode plays a pivotal role in determining battery performance.Due to the strongly-coupled and highly complex processes to produce battery electrode,it is imperative to develop an effective solution that can predict the properties of battery electrode and perform reliable sensitivity analysis on the key features and parameters during the production process.This paper proposes a novel tree boosting model-based framework to analyze and predict how the battery electrode properties vary with respect to parameters during the early production stage.Three data-based interpretable models including AdaBoost,LPBoost,and TotalBoost are presented and compared.Four key parameters including three slurry feature variables and one coating process parameter are analyzed to quantify their effects on both mass loading and porosity of battery electrode.The results demonstrate that the proposed tree model-based framework is capable of providing efficient quantitative analysis on the importance and correlation of the related parameters and producing satisfying early-stage prediction of battery electrode properties.These can benefit a deep understanding of battery electrodes and facilitate to optimizing battery electrode design for automotive applications.

关键词： Li-ion batteries Battery electrode Battery modeling Data analysis

Online Identification of Lithium-ion Battery Model Parameters with Initial Value Uncertainty and Measurement Noise

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Chinese Journal of Mechanical Engineering 2023年第1期36卷 305-314页

作者： Xinghao Du Jinhao Meng kailong liu Yingmin Zhang Shunli Wang Jichang Peng Tianqi liu College of Electrical Engineering Sichuan UniversityChengdu610044China School of Electrical Engineering Xi’an Jiaotong University710049Xi’anChina Warwick Manufacturing Group University of WarwickCoventryUK Southwest University of Science and Technology Mianyang621010China Nanjing Institute of Technology Nanjing211103China

Online parameter identification is essential for the accuracy of the battery equivalent circuit model(ECM).The traditional recursive least squares(RLS)method is easily biased with the noise disturbances from sensors,which degrades the modeling accuracy in practice.Meanwhile,the recursive total least squares(RTLS)method can deal with the noise interferences,but the parameter slowly converges to the reference with initial value uncertainty.To alleviate the above issues,this paper proposes a co-estimation framework utilizing the advantages of RLS and RTLS for a higher parameter identification performance of the battery ECM.RLS converges quickly by updating the parameters along the gradient of the cost function.RTLS is applied to attenuate the noise effect once the parameters have converged.Both simulation and experimental results prove that the proposed method has good accuracy,a fast convergence rate,and also robustness against noise corruption.

关键词： Li-ion battery Equivalent circuit model Recursive least squares Recursive total least squares

Knowledge-Guided Data-Driven Model With Transfer Concept for Battery Calendar Ageing Trajectory Prediction

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 272-274页

作者： kailong liu Qiao Peng Remus Teodorescu Aoife M.Foley IEEE the Warwick Manufacturing Group University of WarwickCoventry CV47ALUK the School of Control Science and Engineering Shandong UniversityChina the Queen’s University Belfast Belfast BT71NNUK the Department of Energy Technology Aalborg UniversityAalborg 9220Denmark

Dear Editor, Lithium-ion(Li-ion) battery has become a promising source to supply and absorb energy/power for many energy-transportation applications. However, Li-ion battery capacity would inevitably degrade over time... 详细信息

关键词： battery Battery Calendar

Towards Long Lifetime Battery:AI-Based Manufacturing and Management

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1139-1165页

作者： kailong liu Zhongbao Wei Chenghui Zhang Yunlong Shang Remus Teodorescu Qing-Long Han Warwick Manufacturing Group University of WarwickCoventry CV47ALUnited Kingdom National Engineering Laboratory for Electric Vehicles School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China School of Control Science and Engineering Shandong UniversityJinan 250061China Department of Energy Technology Aalborg UniversityAalborg 9220Denmark School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia IEEE

Technologies that accelerate the delivery of reliable battery-based energy storage will not only contribute to decarbonization such as transportation electrification,smart grid,but also strengthen the battery supply chain.As battery inevitably ages with time,losing its capacity to store charge and deliver it efficiently.This directly affects battery safety and efficiency,making related health management necessary.Recent advancements in automation science and engineering raised interest in AI-based solutions to prolong battery lifetime from both manufacturing and management perspectives.This paper aims at presenting a critical review of the state-of-the-art AI-based manufacturing and management strategies towards long lifetime battery.First,AI-based battery manufacturing and smart battery to benefit battery health are showcased.Then the most adopted AI solutions for battery life diagnostic including state-of-health estimation and ageing prediction are reviewed with a discussion of their advantages and drawbacks.Efforts through designing suitable AI solutions to enhance battery longevity are also presented.Finally,the main challenges involved and potential strategies in this field are suggested.This work will inform insights into the feasible,advanced AI for the health-conscious manufacturing,control and optimization of battery on different technology readiness levels.

关键词： Artificial intelligence battery health management battery life diagnostic battery manufacturing smart battery

A brief review on key technologies in the battery management system of electric vehicles

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Frontiers of Mechanical Engineering 2019年第1期14卷 47-64页

作者： kailong liu Kang LI Qiao PENG Cheng ZHANG School of Electronics Electrical Engineering and Computer ScienceQueen's University BelfastBT9 5AH BelfastUK School of Physics and Optoelectronic Engineering Nanjing University of Information Science and TechnologyNanjing 210044China IDL Warwick Manufacturing GroupUniversity of WarwickCV4 7AL CoventryUK

Batteries have been widely applied in many high-power applications,such as electric vehicles(EVs)and hybrid electric vehicles,where a suitable battery management system(BMS)is vital in ensuring safe and reliable operation of batteries.This paper aims to give a brief review on several key technologies of BMS,including battery modelling,state estimation and battery charging.First,popular battery types used in EVs are surveyed,followed by the introduction of key technologies used in BMS.Various battery models,including the electric model,thermal model and coupled electro-thermal model are reviewed.Then,battery state estimations for the state of charge,state of health and internal temperature are comprehensively surveyed.Finally,several key and traditional battery charging approaches with associated optimization methods are discussed.

关键词： battery management system battery modelling battery state estimation battery charging

A novel binary/real-valued pigeon-inspired optimization for economic/environment unit commitment with renewables and plug-in vehicles

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Science China(Information Sciences) 2019年第7期62卷 139-141页

作者： Zhile YANG kailong liu Jianping FAN Yuanjun GUO Qun NIU Jianhua ZHANG Shenzhen Institute of Advanced Technology Chinese Academy of Sciences WMG International Digital Laboratory The University of Warwick University of Chinese Academy of Sciences Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronic Engineering and Automation Shanghai University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University

Dear editor,Among multiple levels of power and energy scheduling, day-ahead unit commitment (UC) has long been a challenging task for power system operators owing to the strong constrained, mixedinteger, and high-dimensional characteristics. The objective of UC is to minimize the fossil fuel cost in thermal power generation by determining the on–off status and power generation output while maintaining physical constraints, such as the demand

关键词： pro A novel binary/real-valued pigeon-inspired optimization for economic/environment unit commitment with renewables and plug-in vehicles

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重大突发事件后高校网格化管理的重新审视

杭州电子科技大学学报（社会科学版） 2024年第2期20卷 72-78页

作者：吴小英刘凯龙浙江水利水电学院浙江杭州310018 杭州电子科技大学马克思主义学院浙江杭州310018

基层网格化管理是社会治理现代化的重要手段,也广泛应用于高校学生管理工作。但是基于高校学生的特殊性,在面临重大突发事件时,高校网格化管理存在着应急措施滞后、多元协同不力、管理柔性不够等具体问题。疫情期间,杭州电子科技大学通... 详细信息

基层网格化管理是社会治理现代化的重要手段,也广泛应用于高校学生管理工作。但是基于高校学生的特殊性,在面临重大突发事件时,高校网格化管理存在着应急措施滞后、多元协同不力、管理柔性不够等具体问题。疫情期间,杭州电子科技大学通过数字技术加持的高效化管理、联防互动机制的协同化管理以及智慧思政平台的人性化管理,为高校网格化管理探索出了一条新路。在杭州电子科技大学方案的基础上复盘总结,后疫情时代,高校应深化“三全育人”综合改革,以育人为本、以统筹为要、以精细为策,不断提升网格化管理效能,实现管理育人的现代化。

关键词：突发事件高校学生网格化管理管理育人

Quantifying key factors for optimised manufacturing of Li-ion battery anode and cathode via artificial intelligence

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能源与人工智能(英文)

能源与人工智能(英文) 2022年第1期7卷 75-90页

作者： Mona Faraji Niri kailong liu Geanina Apachitei Luis A.A Román-Ramírez Michael Lain Dhammika Widanage James Marco Warwick Manufacturing Group University of WarwickCV47AL CoventryUK The Faraday Institution Quad OneHarwell Science and Innovation CampusDidcotUK

Li-ion battery is one of the key players in energy storage technology empowering electrified and clean transportation systems.However,it is still associated with high costs due to the expensive material as well as high fluctuations of the manufacturing process.Complicated production processes involving mechanical,chemical,and electrical operations makes the predictability of the manufacturing process a challenge,hence the process is optimised through trial and error rather systematic simulation.To establish an in-depth understanding of the interconnected processes and manufacturing parameters,this paper combines data-mining techniques and real production to offer a method for the systematic analysis,understanding and improving the Li-ion battery electrode manufacturing chain.The novelty of this research is that unlike most of the existing research that are focused on cathode manufacturing only,it covers both of the cathode and anode case studies.Furthermore,it is based on real manufacturing data,proposes a systematic design of experiment method for generating high quality and representative data,and leverages the artificial intelligence techniques to identify the dependencies in between the manufacturing parameters and the key quality factors of the electrode.Through this study,machine learning models are developed to quantify the predictability of electrode and cell properties given the coating process control parameters.Moreover,the manufacturing parameters are ranked and their contribution to the electrode and cell characteristics are quantified by models.The systematic data acquisition approach as well as the quantified interdependencies are expected to assist the manufacturer when moving towards an improved battery production chain.

关键词： Li-ion battery electrodes Anode Cathode Battery manufacturing Machine learning Artificial intelligence

Stochastic speed prediction for connected vehicles using improved bayesian networks with back propagation

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Science China(Technological Sciences) 2022年第7期65卷 1524-1536页

作者： WANG LiHua CUI YaHui ZHANG FengQi COSKUN Serdar liu kailong LI GuangLei School of Mechanical and Precision Instrument Engineering Xi'an University of TechnologyXi'an 710048China Department of Mechanical Engineering Tarsus UniversityTarsusMersin 33400Turkey WMG University of WarwickCoventryCV47ALUK

Advanced vehicular control technologies rely on accurate speed prediction to make ecological and safe decisions. This paper proposes a novel stochastic speed prediction method for connected vehicles by incorporating a Bayesian network(BN) and a Back Propagation(BP) neural network. A BN model is first designed for predicting the stochastic vehicular speed in a priori. To improve the accuracy of the BN-based speed prediction, a BP-based predicted speed error compensation module is constructed by formulating a mapping between the predicted speed and its corresponding prediction error. In the end, a filtering algorithm is developed to smoothen the compensated stochastic vehicular speed. To validate the workings of the proposed approaches in experiments, two typical scenarios are considered: one predecessor vehicle in a double-vehicle scenario and two predecessor vehicles in a multi-vehicle scenario. Simulation results under the considered scenarios demonstrate that the proposed BN-BP fusion method outperforms the BN-based method with respect to the root mean square error, standardized residuals, and R-squared, and the online prediction time of proposed fusion prediction can satisfy a real-time application requirement. The main highlighted contributions of this article are threefold:(1) We put forward an improved BN method, which is combined with a BP neural network, to construct a stochastic vehicular speed prediction method under connected driving;(2) different from existing methods, a unique interconnected framework that consists of a stochastic vehicular speed prediction module, a compensation module, and a speed smoothing module is proposed;(3) extensive simulation studies based on a set of evaluation metrics are illustrated to reveal the advantages and merits of the proposed approaches.

关键词： connected vehicles stochastic vehicular speed prediction Bayesian network back-propagation