For a series plug-in hybrid electric vehicle,higher working efficiency can be achieved by the drive system with two small motors in parallel than that with one big motor alone.However,the overly complex structure will...For a series plug-in hybrid electric vehicle,higher working efficiency can be achieved by the drive system with two small motors in parallel than that with one big motor alone.However,the overly complex structure will inevitably lead to a substantial increase in the development cost.To improve the system price-performance ratio,a new kind of series-parallel hybrid system evolved from the series plug-in hybrid system is designed.According to the technical parameters of the selected components,the system model is established,and the vehicle dynamic property and pure electric drive economy are evaluated.Based on the dynamic programming,the energy management strategy for the drive system under the city driving cycle is developed,and the superiority validation of the system is completed.For the studied vehicle driven by the designed series-parallel plug-in hybrid system,compared with the one driven by the described series plug-in hybrid system,the dynamic property is significantly improved because of the multi-power coupling,and the fuel consumption is reduced by 11.4%with 10 city driving cycles.In a word,with the flexible configuration of the designed hybrid system and the optimized control strategy of the energy management,the vehicle performance can be obviously improved.展开更多
Due to the nonlinearity and nonstationary of hydropower market data, a novel hybrid learning paradigm is proposed to predict hydropower consumption, by incorporating firefly algorithm （FA） into least square support ...Due to the nonlinearity and nonstationary of hydropower market data, a novel hybrid learning paradigm is proposed to predict hydropower consumption, by incorporating firefly algorithm （FA） into least square support vector regression （LSSVR）, i.e., FA-based LSSVR model. In the novel model, the powerful and effective artificial intelligence （AI） technique, i.e., LSSVR, is employed to forecast hydropower consumption. Furthermore, a promising AI optimization tool, i.e., FA, is espe- cially introduced to address the crucial but difficult task of parameters determination in LSSVR （e.g., hyper and kernel function parameters）. With the Chinese hydropower consumption as sample data, the empirical study has statistically confirmed the superiority of the novel FA-based LSSVR model to other benchmark models （including existing popular traditional econometric models, AI models and similar hybrid LSSVRs with other popular parameter searching tools）~ in terms of level and direc- tional accuracy. The empirical results also imply that the hybrid FA-based LSSVR learning paradigm with powerful forecasting tool and parameters optimization method can be employed as an effective forecasting tool for not only hydropower consumption but also other complex data.展开更多
Recently, the single-shaft series-parallel powertrain of Plug-in Hybrid Electric Bus （PHEB） has become one of the most popu- lar powertrains due to its alterable operating modes, excellent fuel economy and strong ad...Recently, the single-shaft series-parallel powertrain of Plug-in Hybrid Electric Bus （PHEB） has become one of the most popu- lar powertrains due to its alterable operating modes, excellent fuel economy and strong adaptability for driving cycles. Never- theless, for configuring the PHEB with single-shaft series-parallel powertrain in the development stage, it still faces greater challenge than other configurations when choosing and matching the main component parameters. Motivated by this issue, a comprehensive multi-objectives optimization strategy based on Genetic Algorithm （GA） is developed for the PHEB with the typical powertrain. First, considering repeatability and regularity of bus route, the methods of off-line data processing and mathematical statistics are adopted, to obtain a representative driving cycle, which could well reflect the general characteristic of the real-world bus route. Then, the economical optimization objective is defined, which is consist of manufacturing costs of the key components and energy consumption, and combined with the dynamical optimization objective, a multi-objective op- timization function is put forward. Meanwhile, GA algorithm is used to optimize the parameters, for the optimal components combination of the novel series-parallel powertrain. Finally, a comparison with the prototype is carried out to verify the per- formance of the optimized powertrain along driving cycles. Simulation results indicate that the parameters of powertrain com- ponents obtained by the proposed comprehensive multi-objectives optimization strategy might get better fuel economy, meanwhile ensure the dynamic performance of PHEB. In contrast to the original, the costs declined by 18%. Hence, the strat- egy would provide a theoretical guidance on parameter selection for PHEB manufacturers.展开更多
This paper aims to present the configuration design approach and the energy management strategy （EMS） of a series-parallel hybrid electric transit bus （SPHEB） jointly developed by Shanghai Automotive Industry Co. ...This paper aims to present the configuration design approach and the energy management strategy （EMS） of a series-parallel hybrid electric transit bus （SPHEB） jointly developed by Shanghai Automotive Industry Co. Ltd. （SAIC） and Shanghai Jiao Tong University （SJTU）, China. A major feature of this SPHEB is that a novel manual transmission is designed to switch the powertrain configuration between series and parallel types. To reduce the fuel consumption as well as sustain the battery state of charge, an EMS including seven energy flow modes is designed and applied to this SPHEB. Governed by this EMS, the engine is maintained to operate in high efficiency regions. The experimental test carded on the transit bus city driving cycle is described and analyzed. The experimental results demonstrate the technical feasibility and fuel economy of this approach.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.51405259)China Postdoctoral Science Foundation funded project(Grant Nos.2014T70072&2013M530608)Colleges and Universities in Hebei Province Science and Technology Research Project(Grant No.QN2015056)
文摘For a series plug-in hybrid electric vehicle,higher working efficiency can be achieved by the drive system with two small motors in parallel than that with one big motor alone.However,the overly complex structure will inevitably lead to a substantial increase in the development cost.To improve the system price-performance ratio,a new kind of series-parallel hybrid system evolved from the series plug-in hybrid system is designed.According to the technical parameters of the selected components,the system model is established,and the vehicle dynamic property and pure electric drive economy are evaluated.Based on the dynamic programming,the energy management strategy for the drive system under the city driving cycle is developed,and the superiority validation of the system is completed.For the studied vehicle driven by the designed series-parallel plug-in hybrid system,compared with the one driven by the described series plug-in hybrid system,the dynamic property is significantly improved because of the multi-power coupling,and the fuel consumption is reduced by 11.4%with 10 city driving cycles.In a word,with the flexible configuration of the designed hybrid system and the optimized control strategy of the energy management,the vehicle performance can be obviously improved.
基金supported by the National Science Fund for Distinguished Young Scholars under Grant No.71025005the National Natural Science Foundation of China under Grant Nos.91224001 and 71301006+1 种基金National Program for Support of Top-Notch Young Professionalsthe Fundamental Research Funds for the Central Universities in BUCT
文摘Due to the nonlinearity and nonstationary of hydropower market data, a novel hybrid learning paradigm is proposed to predict hydropower consumption, by incorporating firefly algorithm （FA） into least square support vector regression （LSSVR）, i.e., FA-based LSSVR model. In the novel model, the powerful and effective artificial intelligence （AI） technique, i.e., LSSVR, is employed to forecast hydropower consumption. Furthermore, a promising AI optimization tool, i.e., FA, is espe- cially introduced to address the crucial but difficult task of parameters determination in LSSVR （e.g., hyper and kernel function parameters）. With the Chinese hydropower consumption as sample data, the empirical study has statistically confirmed the superiority of the novel FA-based LSSVR model to other benchmark models （including existing popular traditional econometric models, AI models and similar hybrid LSSVRs with other popular parameter searching tools）~ in terms of level and direc- tional accuracy. The empirical results also imply that the hybrid FA-based LSSVR learning paradigm with powerful forecasting tool and parameters optimization method can be employed as an effective forecasting tool for not only hydropower consumption but also other complex data.
基金supported by the National Key Science and Technology Projects(Grant No.2014ZX04002041)
文摘Recently, the single-shaft series-parallel powertrain of Plug-in Hybrid Electric Bus （PHEB） has become one of the most popu- lar powertrains due to its alterable operating modes, excellent fuel economy and strong adaptability for driving cycles. Never- theless, for configuring the PHEB with single-shaft series-parallel powertrain in the development stage, it still faces greater challenge than other configurations when choosing and matching the main component parameters. Motivated by this issue, a comprehensive multi-objectives optimization strategy based on Genetic Algorithm （GA） is developed for the PHEB with the typical powertrain. First, considering repeatability and regularity of bus route, the methods of off-line data processing and mathematical statistics are adopted, to obtain a representative driving cycle, which could well reflect the general characteristic of the real-world bus route. Then, the economical optimization objective is defined, which is consist of manufacturing costs of the key components and energy consumption, and combined with the dynamical optimization objective, a multi-objective op- timization function is put forward. Meanwhile, GA algorithm is used to optimize the parameters, for the optimal components combination of the novel series-parallel powertrain. Finally, a comparison with the prototype is carried out to verify the per- formance of the optimized powertrain along driving cycles. Simulation results indicate that the parameters of powertrain com- ponents obtained by the proposed comprehensive multi-objectives optimization strategy might get better fuel economy, meanwhile ensure the dynamic performance of PHEB. In contrast to the original, the costs declined by 18%. Hence, the strat- egy would provide a theoretical guidance on parameter selection for PHEB manufacturers.
基金Project (No. 2006AA11A127) supported by the Hi-Tech (863) Research and Development Program of China
文摘This paper aims to present the configuration design approach and the energy management strategy （EMS） of a series-parallel hybrid electric transit bus （SPHEB） jointly developed by Shanghai Automotive Industry Co. Ltd. （SAIC） and Shanghai Jiao Tong University （SJTU）, China. A major feature of this SPHEB is that a novel manual transmission is designed to switch the powertrain configuration between series and parallel types. To reduce the fuel consumption as well as sustain the battery state of charge, an EMS including seven energy flow modes is designed and applied to this SPHEB. Governed by this EMS, the engine is maintained to operate in high efficiency regions. The experimental test carded on the transit bus city driving cycle is described and analyzed. The experimental results demonstrate the technical feasibility and fuel economy of this approach.
