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Explainable Neural Network for Sensitivity Analysis of Lithium-ion Battery Smart Production

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1944-1953页

作者： kailong liu Qiao Peng Yuhang liu Naxin Cui Chenghui Zhang School of Control Science and Engineering Shandong UniversityJinan 250061China Information Technology Analytics&Operations groupQueen’s University BelfastBelfast BT71NNUK

Battery production is crucial for determining the quality of electrode,which in turn affects the manufactured battery *** battery production is complicated with strongly coupled intermediate and control parameters,an efficient solution that can perform a reliable sensitivity analysis of the production terms of interest and forecast key battery properties in the early production phase is urgently *** paper performs detailed sensitivity analysis of key production terms on determining the properties of manufactured battery electrode via advanced data-driven *** be specific,an explainable neural network named generalized additive model with structured interaction(GAM-SI)is designed to predict two key battery properties,including electrode mass loading and porosity,while the effects of four early production terms on manufactured batteries are explained and *** experimental results reveal that the proposed method is able to accurately predict battery electrode properties in the mixing and coating *** addition,the importance ratio ranking,global interpretation and local interpretation of both the main effects and pairwise interactions can be effectively visualized by the designed neural *** to the merits of interpretability,the proposed GAM-SI can help engineers gain important insights for understanding complicated production behavior,further benefitting smart battery production.

关键词： Battery management battery manufacturing data science explainable artificial intelligence sensitivity analysis

Adaptive battery thermal management systems in unsteady thermal application contexts

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Journal of Energy Chemistry 2024年第10期97卷 650-668页

作者： kailong liu Qiao Peng Zhuoran liu Wei Li Naxin Cui Chenghui Zhang School of Control Science and Engineering Shandong University Information Technology Analytics & Operations Group Queen's University Belfast College of Design and Engineering National University of Singapore

With the increasing attention paid to battery technology,the microscopic reaction mechanism and macroscopic heat transfer process of lithium-ion batteries have been further studied and understood from both academic and industrial ***,as one of the key parameters in the physical fra mework of batteries,affects the performa nce of the multi-physical fields within the battery,a nd its effective control is *** the heat generation in the battery is determined by the real-time operating conditions,the battery temperature is essentially controlled by the real-time heat dissipation conditions provided by the battery thermal management *** battery thermal management systems have basic temperature control capabilities for most conventional application ***,with the current development of la rge-scale,integrated,and intelligent battery technology,the adva ncement of battery thermal management technology will pay more attention to the effective control of battery temperature under sophisticated situations,such as high power and widely varied operating *** this context,this paper presents the latest advances and representative research related to battery thermal management ***,starting from battery thermal profile,the mechanism of battery heat generation is discussed in ***,the static characteristics of the traditional battery thermal management system are ***,considering the dynamic requirements of battery heat dissipation under complex operating conditions,the concept of adaptive battery thermal management system is proposed based on specific research ***,the main challenges for battery thermal management system in practice are identified,and potential future developments to overcome these challenges are presented and discussed.

关键词： Battery safety Battery thermal management system Cooling methods Heat generation mechanism Lithium-ion batteries

Alternating current heating techniques for lithium-ion batteries in electric vehicles:Recent advances and perspectives

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Journal of Energy Chemistry 2024年第9期96卷 679-697页

作者： Xinrong Huang Jinhao Meng Wei Jiang Wenjie liu kailong liu Yipu Zhang Daniel-Ioan Stroe Remus Teodorescu School of Energy and Electrical Engineering Chang’an UniversityXi’an 710018ShaanxiChina School of Electrical Engineering Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina School of Highway Chang’an UniversityXi’an 710018ShaanxiChina School of Automation Northwestern Polytechnical UniversityXi’an 710072ShaanxiChina School of Control Science and Engineering Shandong UniversityJinan 250100ShandongChina Department of Energy Aalborg UniversityAalborg 9220Denmark

The significant decrease in battery performance at low temperatures is one of the critical challenges that electric vehicles(EVs)face,thereby affecting the penetration rate in cold *** current(AC)heating has attracted widespread attention due to its low energy consumption and uniform heating *** paper introduces the recent advances in AC heating from the perspective of practical EV ***,the performance degradation of EVs in low-temperature environments is introduced *** concept of AC heating and its research methods are ***,the effects of various AC heating methods on battery heating performance are *** on existing studies,the main factors that affect AC heating performance are ***,various heating circuits based on EVs are categorized,and their cost,size,complexity,efficiency,reliability,and heating rate are elaborated and *** evolution of AC heaters is presented,and the heaters used in brand vehicles are sorted ***,the perspectives and challenges of AC heating are *** paper can guide the selection of heater implementation methods and the optimization of heating effects for future EV applications.

关键词： Lithium-ion battery Low temperature Alternating current heating Heater Electric vehicle

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基于变参数结构的锂离子电池建模方法

机械工程学报 2024年第14期60卷 298-305页

作者：彭纪昌刘凯龙孟锦豪刘海涛南京工程学院江苏省配电网智能技术与装备协同创新中心南京211167 华威大学制造工程学院考文垂CV47AL英国四川大学电气工程学院成都610065

电动汽车及电网储能电站的快速发展对电池管理系统控制性能提出更高的要求,准确的电池模型及参数是保障电池管理系统中锂离子电池状态监测及高效能量管理的必要条件。传统等效电路模型受限于模型结构只能呈现单一时间尺度的阻抗特性,难... 详细信息

电动汽车及电网储能电站的快速发展对电池管理系统控制性能提出更高的要求,准确的电池模型及参数是保障电池管理系统中锂离子电池状态监测及高效能量管理的必要条件。传统等效电路模型受限于模型结构只能呈现单一时间尺度的阻抗特性,难以有效表征锂离子电池冲放电过程中内部复杂的物理、化学状态变化。基于锂电池充放电过程中的多时间尺度特征,提出一种采用变参数结构的一阶等效电路模型及相应的参数提取方法,该模型将时间因子引入容、阻网络,将容、阻参数可变范围从二维曲线扩展为三维曲面。所提出模型容阻参数能够在充放电及松弛周期内随电池状态的变化而改变,因此该变参数结构能够更为精准地描述电池内部电化学状态的变化。基于变参数特征建立了一阶变参数结构等效电路模型,并通过试验验证了模型的有效性。

关键词：荷电状态变参数结构锂离子电池等效电路模型

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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

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 *** 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 *** 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 *** data-based interpretable models including AdaBoost,LPBoost,and TotalBoost are presented and *** 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 *** 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 *** 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

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 *** paper aims to give a brief review on several key technologies of BMS,including battery modelling,state estimation and battery ***,popular battery types used in EVs are surveyed,followed by the introduction of key technologies used in *** battery models,including the electric model,thermal model and coupled electro-thermal model are ***,battery state estimations for the state of charge,state of health and internal temperature are comprehensively ***,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

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 ***,the recursive total least squares(RTLS)method can deal with the noise interferences,but the parameter slowly converges to the reference with initial value *** 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 *** converges quickly by updating the parameters along the gradient of the cost *** is applied to attenuate the noise effect once the parameters have *** 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

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