Since the main power source of hybrid electric vehicle（HEV） is supplied by the power battery,the predicted performance of power battery,especially the state-of-charge（SOC） estimation has attracted great attention ...Since the main power source of hybrid electric vehicle（HEV） is supplied by the power battery,the predicted performance of power battery,especially the state-of-charge（SOC） estimation has attracted great attention in the area of HEV.However,the value of SOC estimation could not be greatly precise so that the running performance of HEV is greatly affected.A variable structure extended kalman filter（VSEKF）-based estimation method,which could be used to analyze the SOC of lithium-ion battery in the fixed driving condition,is presented.First,the general lower-order battery equivalent circuit model（GLM）,which includes column accumulation model,open circuit voltage model and the SOC output model,is established,and the off-line and online model parameters are calculated with hybrid pulse power characteristics（HPPC） test data.Next,a VSEKF estimation method of SOC,which integrates the ampere-hour（Ah） integration method and the extended Kalman filter（EKF） method,is executed with different adaptive weighting coefficients,which are determined according to the different values of open-circuit voltage obtained in the corresponding charging or discharging processes.According to the experimental analysis,the faster convergence speed and more accurate simulating results could be obtained using the VSEKF method in the running performance of HEV.The error rate of SOC estimation with the VSEKF method is focused in the range of 5% to 10% comparing with the range of 20% to 30% using the EKF method and the Ah integration method.In Summary,the accuracy of the SOC estimation in the lithium-ion battery cell and the pack of lithium-ion battery system,which is obtained utilizing the VSEKF method has been significantly improved comparing with the Ah integration method and the EKF method.The VSEKF method utilizing in the SOC estimation in the lithium-ion pack of HEV can be widely used in practical driving conditions.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
基金Supported by National Key Technology R&D Program of Ministry of Science and Technology of China(Grant No.2013BAG14B01)
文摘Since the main power source of hybrid electric vehicle（HEV） is supplied by the power battery,the predicted performance of power battery,especially the state-of-charge（SOC） estimation has attracted great attention in the area of HEV.However,the value of SOC estimation could not be greatly precise so that the running performance of HEV is greatly affected.A variable structure extended kalman filter（VSEKF）-based estimation method,which could be used to analyze the SOC of lithium-ion battery in the fixed driving condition,is presented.First,the general lower-order battery equivalent circuit model（GLM）,which includes column accumulation model,open circuit voltage model and the SOC output model,is established,and the off-line and online model parameters are calculated with hybrid pulse power characteristics（HPPC） test data.Next,a VSEKF estimation method of SOC,which integrates the ampere-hour（Ah） integration method and the extended Kalman filter（EKF） method,is executed with different adaptive weighting coefficients,which are determined according to the different values of open-circuit voltage obtained in the corresponding charging or discharging processes.According to the experimental analysis,the faster convergence speed and more accurate simulating results could be obtained using the VSEKF method in the running performance of HEV.The error rate of SOC estimation with the VSEKF method is focused in the range of 5% to 10% comparing with the range of 20% to 30% using the EKF method and the Ah integration method.In Summary,the accuracy of the SOC estimation in the lithium-ion battery cell and the pack of lithium-ion battery system,which is obtained utilizing the VSEKF method has been significantly improved comparing with the Ah integration method and the EKF method.The VSEKF method utilizing in the SOC estimation in the lithium-ion pack of HEV can be widely used in practical driving conditions.
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.
