Modeling and simulation analysis of electric forklift energy prediction management
In recent years, the effective utilization of energy for electric forklift has been the focus of research. However, the load of forklift change markedly and frequently, leading to a great challenge in the performance of the composite energy system. To solve this problem, this paper presents the powe...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-09-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722006242 |
| _version_ | 1811225042549211136 |
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| author | Lili Cheng Dingxuan Zhao Tianyu Li Yao Wang |
| author_facet | Lili Cheng Dingxuan Zhao Tianyu Li Yao Wang |
| author_sort | Lili Cheng |
| collection | DOAJ |
| description | In recent years, the effective utilization of energy for electric forklift has been the focus of research. However, the load of forklift change markedly and frequently, leading to a great challenge in the performance of the composite energy system. To solve this problem, this paper presents the power system structure of electric forklift and the battery–supercapacitor hybrid energy management method of electric forklift truck. The working conditions and energy flow of electric forklift are analyzed and the power demand forecasting model of electric forklift power system is indispensable. Two predictive control based on Markov chain and neural network are developed, considering the performance of predictive control. The predictive model is applied to the stochastic model predictive (SMPC) energy management strategy. Simulations are carried out on the representative driving cycles of an electric forklift. As an auxiliary power supply on the energy management of composite power, the influence of the development depth of supercapacitor is discussed. Simulation results show that short-term Markov chain model is closer to the actual working condition. Fuzzy neural network model can give full play to the charging and discharging characteristics of supercapacitors. The state of charge of supercapacitor is stable between 0.5 and 1, which effectively stabilizes the rate of the charge and discharge of the battery. |
| first_indexed | 2024-04-12T09:00:28Z |
| format | Article |
| id | doaj.art-3827665444894cd597b71d3d48db6b0f |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-12T09:00:28Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-3827665444894cd597b71d3d48db6b0f2022-12-22T03:39:16ZengElsevierEnergy Reports2352-48472022-09-018353365Modeling and simulation analysis of electric forklift energy prediction managementLili Cheng0Dingxuan Zhao1Tianyu Li2Yao Wang3School of Mechanical and Aerospace Engineering , Jilin University, Changchun 130012, ChinaSchool of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China; Corresponding author.School of Mechanical and Aerospace Engineering , Jilin University, Changchun 130012, ChinaSchool of Mechanical Engineering, Beihua University, Jilin 132013, ChinaIn recent years, the effective utilization of energy for electric forklift has been the focus of research. However, the load of forklift change markedly and frequently, leading to a great challenge in the performance of the composite energy system. To solve this problem, this paper presents the power system structure of electric forklift and the battery–supercapacitor hybrid energy management method of electric forklift truck. The working conditions and energy flow of electric forklift are analyzed and the power demand forecasting model of electric forklift power system is indispensable. Two predictive control based on Markov chain and neural network are developed, considering the performance of predictive control. The predictive model is applied to the stochastic model predictive (SMPC) energy management strategy. Simulations are carried out on the representative driving cycles of an electric forklift. As an auxiliary power supply on the energy management of composite power, the influence of the development depth of supercapacitor is discussed. Simulation results show that short-term Markov chain model is closer to the actual working condition. Fuzzy neural network model can give full play to the charging and discharging characteristics of supercapacitors. The state of charge of supercapacitor is stable between 0.5 and 1, which effectively stabilizes the rate of the charge and discharge of the battery.http://www.sciencedirect.com/science/article/pii/S2352484722006242Electric forkliftHybrid energy storageSMPCEnergy management |
| spellingShingle | Lili Cheng Dingxuan Zhao Tianyu Li Yao Wang Modeling and simulation analysis of electric forklift energy prediction management Energy Reports Electric forklift Hybrid energy storage SMPC Energy management |
| title | Modeling and simulation analysis of electric forklift energy prediction management |
| title_full | Modeling and simulation analysis of electric forklift energy prediction management |
| title_fullStr | Modeling and simulation analysis of electric forklift energy prediction management |
| title_full_unstemmed | Modeling and simulation analysis of electric forklift energy prediction management |
| title_short | Modeling and simulation analysis of electric forklift energy prediction management |
| title_sort | modeling and simulation analysis of electric forklift energy prediction management |
| topic | Electric forklift Hybrid energy storage SMPC Energy management |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722006242 |
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