Pub Date : 2021-03-20DOI: 10.12783/DTEEES/PEEES2020/35471
Weiwei Jiang, Jiting Han, Ning Wang, Yang Liu, Mingliang Zhao, Shuai Guo, Kaiqiang Wang
The differential pressure of dust collector bag in a power plant was improved synthetically In this paper, the distribution of the smoke flow field in the unit dust collector system is numerically simulated and filed tested. The main technical parameters of the distribution of smoke flow field in the unit's overall dust removal system were measured on site. The actual operation results of the project show that the CFD calculation results are in good agreement with the actual situation, the airflow uniformity inside the modified dust collector is well, the dust removal efficiency is realized and the design expectation is achieved.
{"title":"Measurement and Numerical Simulation Analysis of Smoke Flow Field after Dust Collector Modification","authors":"Weiwei Jiang, Jiting Han, Ning Wang, Yang Liu, Mingliang Zhao, Shuai Guo, Kaiqiang Wang","doi":"10.12783/DTEEES/PEEES2020/35471","DOIUrl":"https://doi.org/10.12783/DTEEES/PEEES2020/35471","url":null,"abstract":"The differential pressure of dust collector bag in a power plant was improved synthetically In this paper, the distribution of the smoke flow field in the unit dust collector system is numerically simulated and filed tested. The main technical parameters of the distribution of smoke flow field in the unit's overall dust removal system were measured on site. The actual operation results of the project show that the CFD calculation results are in good agreement with the actual situation, the airflow uniformity inside the modified dust collector is well, the dust removal efficiency is realized and the design expectation is achieved.","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81925415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-20DOI: 10.12783/DTEEES/PEEES2020/35480
Fei Sun, Yong Chen, Weiwei Chen, Xiaolong Yang
{"title":"Research on Environmental Performance Evaluation of Petrochemical Enterprises Based on Balanced Scorecard-Entropy Method","authors":"Fei Sun, Yong Chen, Weiwei Chen, Xiaolong Yang","doi":"10.12783/DTEEES/PEEES2020/35480","DOIUrl":"https://doi.org/10.12783/DTEEES/PEEES2020/35480","url":null,"abstract":"","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74222433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.12783/dteees/peems2019/33986
Fengjiao Wang, Ji Fan, Bao-hua Wang, Yuzhu Zeng, Xiao-Ming Chen, Ying Lu, Hai-Wen Wang
Aiming at single mode of Underpan generator power supply, and the feature of being not meet the regular work of system under special working conditions, engine fuel exhaustion, unexpected faults, Design of Dual Mode Power Supply Distribution Circuit With Detection And Control Functions which includes Battery power, power distribution module, anti-diode, generator detection module, underpan on-off control module, Battery anti-loss detection module was proposed .The tests show that the generator power supply mode and battery power supply mode can convert freely, and the Supply Distribution Circuit is stable and reliable.
{"title":"Design of Dual Mode Power Supply Distribution Circuit with Detection and Control Functions","authors":"Fengjiao Wang, Ji Fan, Bao-hua Wang, Yuzhu Zeng, Xiao-Ming Chen, Ying Lu, Hai-Wen Wang","doi":"10.12783/dteees/peems2019/33986","DOIUrl":"https://doi.org/10.12783/dteees/peems2019/33986","url":null,"abstract":"Aiming at single mode of Underpan generator power supply, and the feature of being not meet the regular work of system under special working conditions, engine fuel exhaustion, unexpected faults, Design of Dual Mode Power Supply Distribution Circuit With Detection And Control Functions which includes Battery power, power distribution module, anti-diode, generator detection module, underpan on-off control module, Battery anti-loss detection module was proposed .The tests show that the generator power supply mode and battery power supply mode can convert freely, and the Supply Distribution Circuit is stable and reliable.","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88387366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.12783/dteees/peems2019/34001
Hui Cao, Liyang Yan
This paper studies the recommended algorithm for the Mahout machine learning platform. The principle analysis of the current mainstream recommendation algorithm is based on project-based collaborative filtering recommendation. The recommendation algorithm of Book-Crossing data set is implemented by using the collaborative filtering algorithm provided by Mahout. The similarity distance and other parameters in the general recommendation algorithm are used to compare and analyze the recommended results. Introduction With the rapid development of Internet technology, and with the progress of intelligent terminal equipment, mobile Internet has risen. It makes more convenient for people to publish and share information, at the same time, it brings a lot of data information to people. When we enjoy the convenience brought to us by the information age, we also produce various kinds of information. The application scenario of traditional search engine is that users can clearly know their needs through keywords and words to search. However, when users cannot express their needs or have no clear and effective search content, recommendation system emerges as an emerging technology to make up for the shortcomings of traditional information search engines. It uses different recommendation algorithms to model the user's preferences, and predicts the items or information that the user may be interested in according to the model to recommend a user. Project-based collaborative filtering recommendation algorithm is one of the most widely used and effective recommendation algorithms. Gradually, recommendation systems have become the main functions of IT companies that rely on information and data, such as Taobao, Today's headlines and NetEase Cloud Music. The development of recommendation algorithms has developed rapidly from collaborative filtering algorithm to implicit semantic model, and then to deep learning model. The goal of recommendation system is to predict users' preferences through accurate calculation, to achieve the best recommendation effect by coordinating algorithms, system functions and user experience, and to enhance consumers' user experience with more intelligence and humanity. By analyzing the Mahout Recommendation algorithm and taking the book recommendation system as an example, the results of the recommendation algorithm under different parameters are analyzed and compared. Recommendation Based on Collaborative Filtering The recommendation algorithm based on collaborative filtering is one of the most mature algorithms in the recommendation system. The core idea of recommendation based on collaborative filtering: using user behavior data information to extract features from users, which finds new user-to-item correlations by calculating user-to-item correlation to recommend for current users. Mainstream collaborative filtering algorithms include user-based filtering recommendation (User-Based CF) and Project-based Collaborative Filtering Reco
{"title":"Research on Collaborative Filtering Recommendation Algorithm Based on Mahout","authors":"Hui Cao, Liyang Yan","doi":"10.12783/dteees/peems2019/34001","DOIUrl":"https://doi.org/10.12783/dteees/peems2019/34001","url":null,"abstract":"This paper studies the recommended algorithm for the Mahout machine learning platform. The principle analysis of the current mainstream recommendation algorithm is based on project-based collaborative filtering recommendation. The recommendation algorithm of Book-Crossing data set is implemented by using the collaborative filtering algorithm provided by Mahout. The similarity distance and other parameters in the general recommendation algorithm are used to compare and analyze the recommended results. Introduction With the rapid development of Internet technology, and with the progress of intelligent terminal equipment, mobile Internet has risen. It makes more convenient for people to publish and share information, at the same time, it brings a lot of data information to people. When we enjoy the convenience brought to us by the information age, we also produce various kinds of information. The application scenario of traditional search engine is that users can clearly know their needs through keywords and words to search. However, when users cannot express their needs or have no clear and effective search content, recommendation system emerges as an emerging technology to make up for the shortcomings of traditional information search engines. It uses different recommendation algorithms to model the user's preferences, and predicts the items or information that the user may be interested in according to the model to recommend a user. Project-based collaborative filtering recommendation algorithm is one of the most widely used and effective recommendation algorithms. Gradually, recommendation systems have become the main functions of IT companies that rely on information and data, such as Taobao, Today's headlines and NetEase Cloud Music. The development of recommendation algorithms has developed rapidly from collaborative filtering algorithm to implicit semantic model, and then to deep learning model. The goal of recommendation system is to predict users' preferences through accurate calculation, to achieve the best recommendation effect by coordinating algorithms, system functions and user experience, and to enhance consumers' user experience with more intelligence and humanity. By analyzing the Mahout Recommendation algorithm and taking the book recommendation system as an example, the results of the recommendation algorithm under different parameters are analyzed and compared. Recommendation Based on Collaborative Filtering The recommendation algorithm based on collaborative filtering is one of the most mature algorithms in the recommendation system. The core idea of recommendation based on collaborative filtering: using user behavior data information to extract features from users, which finds new user-to-item correlations by calculating user-to-item correlation to recommend for current users. Mainstream collaborative filtering algorithms include user-based filtering recommendation (User-Based CF) and Project-based Collaborative Filtering Reco","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82990625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.12783/dteees/peems2019/33935
Yan Wu
(NH4)2SO4 roasting technology was used for extracting alumina from fly ash which located in Shandong Zibo power plant. The roasting temperature, the mole ratio of Al2O3 to (NH4)2SO4 and roasting time on the effect of the extraction rate of alumina was studied. The optimal roasting technology condition is 380°C for 120min with mole ratio of Al2O3 to (NH4)2SO4 of 1:6. Under the optimal roasting condition, the extraction rate of Al2O3 can reached 82% and a new phase NH4Al(SO4)2 was formed in clinker. NH4Al(SO4)2 in clinker was dissolved by distilled water and then separated from silicon-rich residue by filtration. Iron in NH4Al(SO4)2 solution was removed by goethite process, and then, NH4Al(OH)2CO3 was synthesized by adding (NH4)2CO3 in NH4Al(SO4)2 solution. Ultrafine α-Al2O3 powder was prepared by calcining NH4Al(OH)2CO3 in 1200°C for 120min, which was characterized by XRD and SEM. Introduction Fly ash is generated during high temperature combustion of coal in coal-fired power plants [1-2]. Currently, about 800 million tons of coal fly ash has been generated in the world [3-4]. Since coal is still the major energy resource in China, the output of fly ash has increased continuously over the past several years, reaching 480 million tons in 2010 [5]. The annual fly ash discharge is more than 4×10 8 tons in China [6−7]. Presently, fly ash is consumed on a large scale in cement and concrete fillers as well as roadway and pavement utilization [8−11]. However, despite these positive uses, the production rate of fly ash is much greater than its consumption. There is still a proportion which is disposed of in ponds or landfill, so fly ash has become the main waste of power plant [12]. Fly ash contains significant amounts of alumina, typically about 20-50%, which presents an exciting new alternative source of alumina other than bauxite. Thus, extraction of aluminum from coal fly ash is environmentally and scientifically significant for disposing and utilizing waste materials and exploring new aluminum source, and has attracted extensive attention recently [13]. The Bayer process is the dominating method of refining alumina from bauxite ores throughout the world. The Bayer process mainly involves the bauxite digestion, solid-liquid separation, gibbsite precipitation and calcination [14]. In fly ash, the main form of alumina is mullite, and the alumina-silica ratio (mass ratio of alumina to silica) is approximately 1-1.5. With the low alumina-silica ratio and the stable mineral phase of mullite, a traditional Bayer Process is not suitable for the extraction of alumina from fly ash [15-18]. By using the acid method, alumina is dissolved in acids at high temperature. However, the acid method has not been used in industry because acid-resistant equipment is expensive and iron oxide had has to be separated from the alumina prior to treatment [19]. In the alkali method, fly ash is calcined with soda and lime at 1200°C, and the silica is converted into stable calciu
{"title":"Preparation of Ultrafine -Al2O3 Powder from Fly Ash by Ammonium Sulfate Roasting Technology","authors":"Yan Wu","doi":"10.12783/dteees/peems2019/33935","DOIUrl":"https://doi.org/10.12783/dteees/peems2019/33935","url":null,"abstract":"(NH4)2SO4 roasting technology was used for extracting alumina from fly ash which located in Shandong Zibo power plant. The roasting temperature, the mole ratio of Al2O3 to (NH4)2SO4 and roasting time on the effect of the extraction rate of alumina was studied. The optimal roasting technology condition is 380°C for 120min with mole ratio of Al2O3 to (NH4)2SO4 of 1:6. Under the optimal roasting condition, the extraction rate of Al2O3 can reached 82% and a new phase NH4Al(SO4)2 was formed in clinker. NH4Al(SO4)2 in clinker was dissolved by distilled water and then separated from silicon-rich residue by filtration. Iron in NH4Al(SO4)2 solution was removed by goethite process, and then, NH4Al(OH)2CO3 was synthesized by adding (NH4)2CO3 in NH4Al(SO4)2 solution. Ultrafine α-Al2O3 powder was prepared by calcining NH4Al(OH)2CO3 in 1200°C for 120min, which was characterized by XRD and SEM. Introduction Fly ash is generated during high temperature combustion of coal in coal-fired power plants [1-2]. Currently, about 800 million tons of coal fly ash has been generated in the world [3-4]. Since coal is still the major energy resource in China, the output of fly ash has increased continuously over the past several years, reaching 480 million tons in 2010 [5]. The annual fly ash discharge is more than 4×10 8 tons in China [6−7]. Presently, fly ash is consumed on a large scale in cement and concrete fillers as well as roadway and pavement utilization [8−11]. However, despite these positive uses, the production rate of fly ash is much greater than its consumption. There is still a proportion which is disposed of in ponds or landfill, so fly ash has become the main waste of power plant [12]. Fly ash contains significant amounts of alumina, typically about 20-50%, which presents an exciting new alternative source of alumina other than bauxite. Thus, extraction of aluminum from coal fly ash is environmentally and scientifically significant for disposing and utilizing waste materials and exploring new aluminum source, and has attracted extensive attention recently [13]. The Bayer process is the dominating method of refining alumina from bauxite ores throughout the world. The Bayer process mainly involves the bauxite digestion, solid-liquid separation, gibbsite precipitation and calcination [14]. In fly ash, the main form of alumina is mullite, and the alumina-silica ratio (mass ratio of alumina to silica) is approximately 1-1.5. With the low alumina-silica ratio and the stable mineral phase of mullite, a traditional Bayer Process is not suitable for the extraction of alumina from fly ash [15-18]. By using the acid method, alumina is dissolved in acids at high temperature. However, the acid method has not been used in industry because acid-resistant equipment is expensive and iron oxide had has to be separated from the alumina prior to treatment [19]. In the alkali method, fly ash is calcined with soda and lime at 1200°C, and the silica is converted into stable calciu","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84471382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-04DOI: 10.12783/dteees/iceee2019/31824
Yukun Hu, J. Chowdhury, G. Katsaros, C. K. Tan, N. Balta-Ozkan, L. Varga, S. Tassou, Chunsheng Wang
This paper investigates the integration of biosyngas production, reheating furnace and heat recovery steam cycle, in order to use biosyngas directly as fuel in the furnace. A system model was developed to evaluate the feasibility of the proposed system from the perspective of heat and mass balance. To particularly study the impacts of fuel switching on the heating quality of the furnace, a three-dimensional furnace model considering detailed heat transfer processes was embedded into the system through an Aspen PlusTM user defined model. The simulation results show that biosyngas is suitable for direct use as fuel for reheating furnaces. Should CO2 capture be considered in the proposed system, it has a potential to achieve the capture without external energy input which results in so-called negative emissions of CO2.
{"title":"Feasibility Study of Biomass Gasification Integrated with Reheating Furnaces in Steelmaking Process","authors":"Yukun Hu, J. Chowdhury, G. Katsaros, C. K. Tan, N. Balta-Ozkan, L. Varga, S. Tassou, Chunsheng Wang","doi":"10.12783/dteees/iceee2019/31824","DOIUrl":"https://doi.org/10.12783/dteees/iceee2019/31824","url":null,"abstract":"This paper investigates the integration of biosyngas production, reheating furnace and heat recovery steam cycle, in order to use biosyngas directly as fuel in the furnace. A system model was developed to evaluate the feasibility of the proposed system from the perspective of heat and mass balance. To particularly study the impacts of fuel switching on the heating quality of the furnace, a three-dimensional furnace model considering detailed heat transfer processes was embedded into the system through an Aspen PlusTM user defined model. The simulation results show that biosyngas is suitable for direct use as fuel for reheating furnaces. Should CO2 capture be considered in the proposed system, it has a potential to achieve the capture without external energy input which results in so-called negative emissions of CO2.","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85868264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.12783/dteees/iccis2019/31719
Peng Ding, Jing Zhang, Yuele Jia
{"title":"Dynamic Simulation Model for Gas Transmission Pipeline Network System","authors":"Peng Ding, Jing Zhang, Yuele Jia","doi":"10.12783/dteees/iccis2019/31719","DOIUrl":"https://doi.org/10.12783/dteees/iccis2019/31719","url":null,"abstract":"","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77052940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Powertrain structure is introduced first; some important parameters of the power train are matched, according to the vehicle parameters and the driving requirements. The constraints of the transmission ratios are built up following the selecting of the motor parameters. For the purpose of the transmission ratios optimization, the transmission model and the whole vehicle dynamic mode is established on the software platform of MATLAB/Simulink. The optimal transmission ratios are identified by employing the multi-objective optimization genetic algorithm NSGA-II in the software ISIGHT, and the optimal transmission ratios are 7.34i1 13.9 and 5.44i2 8.49 .The optimum , .when it be taken as , , and the ratio of the main deceleration ratio is transmission ratio of the transmission system ig1 = 3.09 ig2 =1.83 i1 =11.75 i2 =6.98 iF =3.8 ig1 = 3.09 ig2 =1.83
{"title":"Matching Design and Optimization Research of Gearbox for Electric Vehicle","authors":"Chun-xiang Zhang, Zai-zhou Wang, Q. Song, Chengning Zhang","doi":"10.12783/dteees/iceee2019/31730","DOIUrl":"https://doi.org/10.12783/dteees/iceee2019/31730","url":null,"abstract":"Powertrain structure is introduced first; some important parameters of the power train are matched, according to the vehicle parameters and the driving requirements. The constraints of the transmission ratios are built up following the selecting of the motor parameters. For the purpose of the transmission ratios optimization, the transmission model and the whole vehicle dynamic mode is established on the software platform of MATLAB/Simulink. The optimal transmission ratios are identified by employing the multi-objective optimization genetic algorithm NSGA-II in the software ISIGHT, and the optimal transmission ratios are 7.34i1 13.9 and 5.44i2 8.49 .The optimum , .when it be taken as , , and the ratio of the main deceleration ratio is transmission ratio of the transmission system ig1 = 3.09 ig2 =1.83 i1 =11.75 i2 =6.98 iF =3.8 ig1 = 3.09 ig2 =1.83","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78382018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-31DOI: 10.12783/dteees/iceee2019/31734
Hao Zhang, Yifeng Zhao, Zeyu Chen, Bo Liu
Energy management strategy (EMS) is an effective measure to improve the energy economy, however, where fluctuant electricity and fuel prices were not taken into account. Since price fluctuations have an impact on rule-based energy management strategies, constant price energy management strategies are not optimal. This paper proposes a novel energy management strategy considering price fluctuation of electricity and fuel for plug-in electric vehicles (PHEVs). Firstly, the electricity price data of partial cities during peak and trough periods are counted. Secondly, a SA-PSO algorithm is used in the rule-based energy management strategy to optimize the key thresholds. The influence of electricity price fluctuation on energy management strategy has been analyzed.
{"title":"Research on the Impact of Electricity Price Fluctuation on Energy Management Strategy","authors":"Hao Zhang, Yifeng Zhao, Zeyu Chen, Bo Liu","doi":"10.12783/dteees/iceee2019/31734","DOIUrl":"https://doi.org/10.12783/dteees/iceee2019/31734","url":null,"abstract":"Energy management strategy (EMS) is an effective measure to improve the energy economy, however, where fluctuant electricity and fuel prices were not taken into account. Since price fluctuations have an impact on rule-based energy management strategies, constant price energy management strategies are not optimal. This paper proposes a novel energy management strategy considering price fluctuation of electricity and fuel for plug-in electric vehicles (PHEVs). Firstly, the electricity price data of partial cities during peak and trough periods are counted. Secondly, a SA-PSO algorithm is used in the rule-based energy management strategy to optimize the key thresholds. The influence of electricity price fluctuation on energy management strategy has been analyzed.","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85933707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-31DOI: 10.12783/dteees/iceee2019/31808
Xiaopeng Tang, K. Yao, Boyang Liu, F. Gao
This paper presents a connection failure detection for a Lithium-ion battery pack when no external vibrations exist. First, the gradient correction method is employed to identify the overall ohmic resistance, which is the summation of the internal and external (contact) resistance. Second, the battery state of health (SOH) is estimated with incremental capacity analysis (ICA) - based method. Third, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method is applied to diagnose the connection failure by matching the calculated resistance with the estimated SOH. Finally, a linear projection is applied to reduce the method sensitivity to the testing conditions such as different state of charge (SOC). Experiments show that the proposed method can identify the location of the connection failure well in real time.
{"title":"Connection Failure Detection for Lithium-ion Batteries Based on DBSCAN-Projection Method","authors":"Xiaopeng Tang, K. Yao, Boyang Liu, F. Gao","doi":"10.12783/dteees/iceee2019/31808","DOIUrl":"https://doi.org/10.12783/dteees/iceee2019/31808","url":null,"abstract":"This paper presents a connection failure detection for a Lithium-ion battery pack when no external vibrations exist. First, the gradient correction method is employed to identify the overall ohmic resistance, which is the summation of the internal and external (contact) resistance. Second, the battery state of health (SOH) is estimated with incremental capacity analysis (ICA) - based method. Third, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method is applied to diagnose the connection failure by matching the calculated resistance with the estimated SOH. Finally, a linear projection is applied to reduce the method sensitivity to the testing conditions such as different state of charge (SOC). Experiments show that the proposed method can identify the location of the connection failure well in real time.","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84022076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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