检索结果-南通市图书馆

Machine learning based state-of-charge prediction of electrochemical green hydrogen production:Zink-Zwischenschritt-Elektrolyseur(ZZE)

在线全文

学校读者我要写书评

暂无评论

Energy and AI 2024年第2期16卷 269-276页

作者： Daniel Vila Elisabeth Hornberger Christina Toigo STOFF2 GmbH–Gebäude H/Flughafen Tegel 1 13405 BerlinGermany University of Applied Sciences Upper Austria Stelzhamerstraße 23A-4600 WelsAustria Centre for Economic Research on Inclusivity and Sustainability at the University of Galway University RoadGalwayIreland

The intermittency of renewable energy is a key limiting factor for the successful decarbonization of both energy producing and consuming sectors. Green hydrogen has the potential to act as the central energy vector connecting hard-to-abate sectors to renewable power. However, combining energy storage and conversion for a holistic electrolyzer system remains challenging. Here, we show the innovative Zink-Zwischenschritt Elektrolyseur (ZZE), or Zinc Intermediate step Electrolyzer in English, that temporarily decouples the water splitting reaction and uses zinc to store electrical energy in chemical form. To perform optimal operation of a ZZE system, machine learning models were applied to predict the state of charge of a lab scale ZZE system. Using various models, we were able to determine the effectiveness of the prediction and contrast it to state of charge predictions of other energy storage systems. We show that a bi-directional long short-term memory neural network approach has the lowest error within the testing environment. This work serves to perform further ZZE development as well as state of charge prediction for other novel energy storage technologies.

关键词： Electrolyzer Battery ZZE Energy storage state of charge Machine learning Sequence-to-sequence model

state of charge estimation by finite difference extended Kalman filter with HPPC parameters identification

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Science China(Technological Sciences) 2020年第3期63卷 410-421页

作者： HE Lin HU MinKang WEI YuJiang LIU BingJiao SHI Qin Automotive Research Institute Hefei University of TechnologyHefei 230009China School of Automotive and Transportation Engineering Hefei University of TechnologyHefei 230009China

state of charge(SOC) is a key parameter of lithium-ion battery. In this paper, a finite difference extended Kalman filter(FDEKF)with Hybrid Pulse Power Characterization(HPPC) parameters identification is proposed to estimate the SOC. The finite difference(FD) algorithm is benefit to compute the partial derivative of nonlinear function, which can reduce the linearization error generated by the extended Kalman filter(EKF). The FDEKF algorithm can reduce the computational load of controller in engineering practice without solving the Jacobian matrix. It is simple of dynamic model of lithium-ion battery to adopt a secondorder resistor-capacitor(2 RC) network, the parameters of which are identified by the HPPC. Two conditions, both constant current discharge(CCD) and urban dynamometer driving schedule(UDDS), are utilized to validate the FDEKF *** convergence rate and accuracy between the FDEKF and the EKF algorithm, it can be seen that the former is a better candidate to estimate the SOC.

关键词： state of charge lithium-ion battery parameters identification finite difference algorithm extended Kalman filter

state of charge Estimation for Lithium-Ion Battery Based on Improved Cubature Kalman Filter Algorithm

在线全文

学校读者我要写书评

暂无评论

Automotive Innovation 2021年第2期4卷 189-200页

作者： Guochun Li Chang Liu Enlong Wang Limei Wang Automotive Engineering Research Institute Jiangsu UniversityZhenjiang 212013China Institute for Energy Research Jiangsu UniversityZhenjiang 212013China

An improved cubature Kalman filter(CKF)algorithm for estimating the state of charge of lithium-ion batteries is *** improved algorithm implements the diagonalization decomposition of the covariance matrix and a strong tracking ***,a first-order RC equivalent circuit model is first established and verified,whose voltage estimation error is within 1.5%;this confirms that the model can be used to describe the characteristics of a *** the calculation processes of the traditional and proposed CKF algorithms are ***,the improved CKF algorithm is applied to the state of charge estimation under the constant-current discharge and dynamic stress test *** average errors for these two conditions are 0.76%and 1.2%,respectively,and the maximum absolute error is only 3.25%.The results indicate that the proposed method has higher filter stability and estimation accuracy than the extended Kalman filter(EKF),unscented Kalman filter(UKF)and traditional CKF ***,the convergence rates of the above four algorithms are compared,among which the proposed algorithm track the referenced values at the highest speed.

关键词： Lithium-ion battery state of charge Cubature Kalman filter Strong tracking filter Covariance matrix diagonalization decomposition

ESTIMATION METHOD ON THE BATTERY state of charge FOR HYBRID ELECTRIC VEHICLE

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Chinese Journal of Mechanical Engineering 2008年第3期21卷 20-25页

作者： QIANG Jiaxi AO Guoqiang YANG Lin School of Mechanical Engineering Shanghai Jiaotong University Shanghai 200030 China

A combined algorithm for battery state of charge （SOC） estimation is proposed to solve the critical issue of hybrid electric vehicle （HEV）. To obtain a more accurate SOC, both coulomb-accumulation and battery resistance-capacitor （RC） model are weighted combined to compensate the deficiencies of individual methods. In order to solve the key issue of coulomb-accumulation, the battery thermal model is used. Based on the principle of energy conservation, the heat generated from battery charge and discharge process is converted into the equivalent electricity to calculate charge and discharge efficiency under variable current. The extended Kalman filter （EKF） as a closed loop algorithm is applied to estimate the parameters of resistance-capacitor model. The input variables do not increase much computing difficulty. The proposed combined algorithm is implemented by adjusting the weighting factor of coulomb- accumulation and resistance-capacitor model. In the end, four different methods including Ah-efficiency, Ah-Equip, RC-SOC and Combined-SOC are compared in federal testing procedure （FTP） drive cycle. The experiment results show that the proposed method has good robustness and high accuracy which is suitable for HEV application.

关键词： state of charge Coulomb-accumulation Resistance-capacitor modelHybrid electric vehicle

A Comparative Study of Fractional Order Models on state of charge Estimation for Lithium Ion Batteries

在线全文

学校读者我要写书评

暂无评论

Chinese Journal of Mechanical Engineering 2020年第4期33卷 98-112页

作者： Jinpeng Tian Rui Xiong Weixiang Shen Ju Wang Department of Vehicle Engineering School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China Faculty of Science Engineering and TechnologySwinburne University of TechnologyHawthornVictoria 3122Australia

state of charge(SOC)estimation for lithium ion batteries plays a critical role in battery management systems for electric *** fractional order models(FOMs)which come from frequency-domain modelling have provided a distinct insight into SOC *** this article,we compare five state-of-the-art FOMs in terms of SOC *** this end,firstly,characterisation tests on lithium ion batteries are conducted,and the experimental results are used to identify FOM *** identification results show that increasing the complexity of FOMs cannot always improve *** model R(RQ)W shows superior identification accuracy than the other four ***,the SOC estimation based on a fractional order unscented Kalman filter is conducted to compare model accuracy and computational burden under different profiles,memory lengths,ambient temperatures,cells and voltage/current *** evaluation results reveal that the SOC estimation accuracy does not necessarily positively correlate to the complexity of *** more complex models can have better robustness against temperature variation,R(RQ),the simplest FOM,can overall provide satisfactory *** results on different cells demonstrate the generalisation ability of FOMs,and R(RQ)outperforms other ***,R(RQ)shows better robustness against truncation error and can maintain high accuracy even under the occurrence of current or voltage sensor drift.

关键词： Electric vehicle Lithium ion battery Fractional order model state of charge

A review of deep learning approach to predicting the state of health and state of charge of lithium-ion batteries

在线全文

学校读者我要写书评

暂无评论

Journal of Energy Chemistry 2022年第11期31卷 159-173,I0006页

作者： Kai Luo Xiang Chen Huiru Zheng Zhicong Shi School of Materials and Energy Guangdong University of TechnologyGuangzhou 510006GuangdongChina Department of Chemical Engineering Tsinghua UniversityBeijing 100084China School of Computing Ulster UniversityBelfast BT151EDUnited Kingdom

In the field of energy storage,it is very important to predict the state of charge and the state of health of lithium-ion *** this paper,we review the current widely used equivalent circuit and electrochemical models for battery state *** review demonstrates that machine learning and deep learning approaches can be used to construct fast and accurate data-driven models for the prediction of battery *** details,advantages,and limitations of these approaches are presented,compared,and ***,future key challenges and opportunities are discussed.

关键词： Lithium-ion battery state of health state of charge Remaining useful life Data-driven

Study about thermal runaway behavior of high specific energy density Li-ion batteries in a low state of charge

在线全文

学校读者我要写书评

暂无评论

Journal of Energy Chemistry 2021年第1期30卷 20-27,I0002页

作者： Shiqiang Liu Tianyi Ma Zhen Wei Guangli Bai Huitian Liu Dapeng Xu Zhongqiang Shan Fang Wang School of Chemical Engineering and Technology Tianjin UniversityTianjin 300350China Tianjin Key Laboratory of Evaluation Technology for Electric Vehicles China Automotive Technology and Research Center Co.Ltd.(CATARC)Tianjin 300300China

Lithium-ion batteries are widely used in electric vehicles and electronics, and their thermal safety receives widespread attention from consumers. In our study, thermal runaway testing was conducted on the thermal stability of commercial lithium-ion batteries, and the internal structure of the battery was analyzed with an in-depth focus on the key factors of the thermal runaway. Through the study of the structure and thermal stability of the cathode, anode, and separator, the results showed that the phase transition reaction of the separator was the key factor affecting the thermal runaway of the battery for the condition of a low state of charge.

关键词： Lithium-ion battery Thermal runaway state of charge Thermal stability

Co-Estimation of state of charge and Capacity for Lithium-Ion Batteries with Multi-Stage Model Fusion Method

在线全文

学校读者我要写书评

暂无评论

Engineering 2021年第10期7卷 1469-1482页

作者： Rui Xiong Ju Wang Weixiang Shen Jinpeng Tian Hao Mu Department of Vehicle Engineering School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China Faculty of Science Engineering and TechnologySwinburne University of TechnologyHawthornVIC 3122Australia

Lithium-ion batteries(LIBs)have emerged as the preferred energy storage systems for various types of electric transports,including electric vehicles,electric boats,electric trains,and electric *** energy management of LIBs in electric transports for all-climate and long-life operation requires the accurate estimation of state of charge(SOC)and capacity in *** study proposes a multistage model fusion algorithm to co-estimate SOC and ***,based on the assumption of a normal distribution,the mean and variance of the residual error from the model at different ageing levels are used to calculate the weight for the establishment of a fusion model with stable ***,a differential error gain with forward-looking ability is introduced into a proportional–integral observer(PIO)to accelerate convergence ***,a fusion algorithm is developed by combining a multistage model and proportional–integral–differential observer(PIDO)to co-estimate SOC and capacity under a complex application ***,the convergence and anti-noise performance of the fusion algorithm are ***,the hardware-in-the-loop platform is set up to verify the performance of the fusion *** validation results of different aged LIBs over a wide range of temperature show that the presented fusion algorithm can realize a high-accuracy estimation of SOC and capacity with the relative errors within 2%and 3.3%,respectively.

关键词： state of charge Capacity estimation Model fusion Proportional-integral-differential observer Hardware-in-the-loop

Experimental Study on the Effect of state of charge on Failure Propagation Characteristics within Battery Modules

在线全文

学校读者我要写书评

暂无评论

Chinese Journal of Electrical Engineering 2023年第3期9卷 3-14页

作者： Kuijie Li Yalun Li Xinyu Rui Yuancheng Cao Liyun Fan Xuning Feng School of Electrical and Electronic Engineering Huazhong University of Science and TechnologyWuhan 430074China state Key Laboratory of Automotive Safety and Energy Tsinghua UniversityBeijing 100086China College of Power and Energy Engineering Harbin Engineering UniversityHarbin 150001China Department of Electrical Engineering Tsinghua UniversityBeijing 100086China

To investigate the effect of different states of charge(SOC)on the thermal runaway(TR)propagation behaviors within lithium-ion-batteries based energy storage modules,an experimental setup was developed to conduct failure propagation tests on battery modules at an SOC of 97%,85%,and 50%.The result indicates that an increase in the SOC of batteries can decrease the TR trigger temperature,making batteries trigger TR earlier and reducing the average failure propagation time between two adjacent *** addition,the failure propagation tests reveal that at higher SOCs,the TR reaction becomes more violent,the maximal reaction temperature is also much higher,and the damage to the battery module is *** to the battery module with 97%SOC,the TR trigger time of the battery module with 50%SOC was postponed by approximately 57.8%.Meanwhile,the average failure propagation time got prolonged by approximately 36.0%.Thus,this study can provide references for the thermal safety design of energy-storage battery modules.

关键词： Energy storage battery module thermal runaway failure propagation state of charge battery safety