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Cotton is an important economic crop and strategic resource. The 14th Five-Year Plan for agricultural mechanization development emphasizes the need to vigorously promote agricultural mechanization and intelligentization. As a key agricultural equipment for achieving whole process mechanization of cotton harvesting, cotton pickers have attracted increasing attention. In China, the traditional walking transmission system of cotton pickers usually adopts the form of hydraulic infinite variable speed combined with mechanical stepped transmission. However, during operation, these pickers face significant variations in working conditions and fluctuating external loads, resulting in an overall high load. The hydraulic walking transmission system finds it struggles to continuously adjust speed and torque to match the constantly changing actual load, which leads to frequent fluctuations in the engine operating points, mismatch of torque in transmission components, and low efficiency of the transmission system. At the same time, due to the large overall load during the operation of the cotton picker, the engine runs for a long time in the high-speed and high-load area, and it is unable to maintain the high-efficiency area, resulting in the problem of high fuel consumption. To address the high fuel consumption associated with traditional cotton picker engines operating in inefficient areas, as well as the low efficiency of the hydraulic drive transmission system, replacing the original 410 kW engine with a 560KW engine allows it to operate in the high-efficiency area. Simultaneously, employing hybrid technology to replace hydraulic transmission with electric drive can significantly improve the performance and efficiency of the cotton picker, yielding better operational outcomes and economic benefits. In order to solve the problems of low system efficiency and high energy consumption caused by the hydraulic transmission of the main working subsystem of the traditional diesel engine power six-row cotton picker, a series hybrid power system configuration is proposed for the first time. Based on this, a hybrid power system configuration for a six-row cotton picker is proposed for the first time. To accommodate the actual working conditions of cotton pickers, a two-speed gearbox is implemented, which includes a working gear and a transporting gear. The front and rear axles are driven by front and rear drive motors through gearboxes and front and rear axle reducers. This configuration allows for efficient operation and transmission of power, enhancing the overall performance of the cotton picker. A vehicle simulation model for a six-row series hybrid cotton picker has been developed using MATLAB/Simulink, and the effects of three strategies on the performance of the cotton picker are studied: power following strategy, equivalent consumption minimization strategy (ECMS), and torque distribution combined with equivalent consumption minimization strategy (TD-ECMS). Simulation results show that under a combined road transportation and field operation condition, the total simulation duration is 1 319 seconds, with 0 to 688 seconds allocated for road transportation and 689 to 1 319 seconds for field operation. The ECMS strategy significantly minimizes fluctuations in the engine's speed point. In comparison to the power-following strategy, which allows the engine to operate in a stable state only 34.34% of the time, the ECMS strategy enhances stability, increasing the stable operating time to 75.28%. This results engine speed stabilization time increased by 40.94 %. The TD-ECMS strategy achieves optimal control of the comprehensive efficiency of the dual motors, maintaining consistency with power-sharing control when the required power is high, and when the required power is low, the torque is completed by the front drive motor alone, the reduction of working points in the low-efficiency area of the rearmounted motor leads to a decrease in the overall proportion of operation within the low-efficiency area. The comprehensive efficiency of the dual motors under the TD-ECMS strategy increased from 93.92% to 94.83% compared to power-sharing control. Compared to the power-following strategy, the ECMS strategy has reduced fuel consumption by 4.87%, and the TDECMS has reduced fuel consumption by 5.62%, resulting in a significant decrease and an improvement in overall economic performance. This paper presents an efficient energy management strategy for a six-row series hybrid cotton picker, laying the foundation for engineering practice. [ABSTRACT FROM AUTHOR]

To reduce hydrogen consumption in fuel cell electric vehicles, mitigate power fluctuations, and maintain the battery system's state of charge, a novel transition process recognition method based on work condition recognition framework of energy management strategy is proposed. This method offers the advantages such as higher recognition rates and faster recognition speed comparing with the traditional condition recognition methods. A comparison is made with the commonly used LVQ recognition method, and the simulations are conducted to demonstrate the superiority of this condition recognition algorithm and the improved performance of the energy management strategy based on the present recognition method under mixed work conditions.

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Xi'an Jiaotong University is the property of Editorial Office of Journal of Xi'an Jiaotong University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The fuel economy of hybrid electric vehicles largely depends on the transmission system parameters and energy management strategy. However, due to the interaction between the transmission system parameters and energy management strategies on the optimization of fuel economy, and the difficulty in balancing the real-time and global optimality of energy management strategies, there is still a lack of an effective implementation method. This study takes the hybrid electric mining truck as a research object, establishes the mathematical model of the whole vehicle, takes reducing fuel consumption as the goal, establishes a collaborative optimization model that comprehensively considers the interaction between transmission system parameters and energy management strategies, and combines particle swarm optimization (PSO) algorithm and dynamic programming (DP) algorithm to build a two-layer interactive optimization algorithm to eliminate the interaction between transmission system parameters and energy management strategies. The optimal energy distribution can be achieved while optimizing the transmission parameters. On this basis, in order to solve the problem that the DP algorithm is difficult to realize online control, the optimal solution of DP energy management corresponding to the optimal transmission parameters is integrated, an effective method to extract the optimal control rules of DP is designed, and an energy management strategy based on the optimal control rules is established to realize online control of hybrid electric mining trucks. Thus, the integrated realization method of the coordinated optimization of transmission system parameters and energy management strategy, as well as the optimal energy management real-time control strategy, is established. The obtained optimal transmission system parameters and corresponding optimal energy management real-time control strategy are simulated and verified on the Stateflow-AMESim joint simulation platform. The results show that the fuel consumption results obtained by this method are better than those obtained by DP optimization alone, and they are closer to the results obtained by PSO-DP optimization, indicating that the optimization method is effective and feasible.

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The environment pollution and petroleum problems, which are increasingly becoming serious, have caused the vehicle industry to transition into a low-carbon and energy-saving industry. During processes, plug-in fuel-cell electric vehicles (PFCEVs) play an important role due to their advantages of rapid fueling, high energy density and efficiency, low operating temperature, and zero onboard emissions. PFCEVs use high-capacity rechargeable batteries to avoid working in low-efficiency areas. However, a robust energy management strategy that can achieve reliable energy distribution by regulating the output power of the fuel cell and battery within the hybrid powertrain merits further investigation. Considering the close relationship between the driving cycle, state of charge (SOC), equivalent factor, and hydrogen consumption, a trip distance adaptive equivalent consumption minimum strategy integrating driving cycle prediction is proposed. A backpropagation-based neural network is used to predict short-term vehicle velocity and analyze future changes in vehicle demand power. Planning a path to the destination with the help of the global positioning system, the intelligent transportation system can also obtain traffic flow information for the entire trip. The equivalent factor is dynamically corrected in real time using the driving distance and SOC to realize the adaptability of the energy management strategy. Finally, the velocity prediction sequence is combined with the objective function. The sequential quadratic programming algorithm is used to optimize the equivalent hydrogen consumption of the objective function and to obtain the distributed power of the fuel cell and battery. The vehicle simulation model is built and compared with a traditional energy management strategy based on MATLAB/Simulink software. The simulation results show that the driving cycle prediction algorithm based on the backpropagation-based neural network predicts future short-term conditions better, with a 12.5% higher accuracy than the Markov method. The proposed energy management strategy allows the fuel cell to operate in high-efficiency areas. The hydrogen consumption is 55.6% less than that of the CD/CS strategy under the UDDS cycle. The hardware in the loop experiment verifies a hydrogen consumption that is 26.8% less than that of the CD/CS strategy under the EUDC cycle. The numerical validation results demonstrate the superior performance of the proposed strategy in terms of hydrogen consumption over the CD/CS strategy. The effectiveness of the proposed strategy is validated by hardware during the loop experiment.

In order to reduce the equivalent hydrogen consumption of the hybrid system and delay the aging of the fuel cell, an Energy management strategy (EMS) was proposed based on multi-objective optimization and road condition classification. Firstly, the electrical model of the fuel cell and lithium battery hybrid system power was constructed, and the equivalent hydrogen consumption model and fuel cell aging model were introduced. Then, a rule-based multi-mode EMS was designed; on this basis, in order to further reduce the equivalent hydrogen consumption of the system and prolong its service life, the multi-objective beluga whale optimization algorithm (MOBWO) was proposed to optimize the control parameters. Furthermore, in order to make the designed EMS suitable for different road conditions, a real-time classification method of driving road conditions based on long short-term memory (LSTM) network was proposed, aiming to switch the control parameters of EMS according to the classification results to achieve the optimal effect. Finally, the proposed algorithm was analyzed on the simulation platform. The results showed that the hydrogen consumption of the hybrid system with the proposed method was reduced by 2.3% and the aging degree of the fuel cell was reduced by 1.02% compared with the rule-based method, The proposed EMS could effectively reduce the equivalent hydrogen consumption of the hybrid system and delay the aging of the fuel cell. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To enhance the fuel economy of parallel hybrid commercial vehicles equipped with automatic transmissions and optimize the efficiency of the drive system, a new method for dividing driving mode boundaries and gear boundaries was proposed. A control strategy for mode switching and gear shifting was developed based on optimal efficiency and simulated under the driving conditions of Chinese heavy commercial vehicles. The results demonstrate that the proposed strategy maintains the balance of the battery state of charge, and achieves a 11.2% reduction in fuel consumption per 100 kilometers compared to the electric assist control strategy, which improves the fuel economy. [ABSTRACT FROM AUTHOR]

Copyright of Nonferrous Metals (Mining Section) is the property of Beijing Research Institute of Mining & Metallurgy Technology Group and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As environmental problems become increasingly severe, achieving qualitative breakthroughs in the energy consumption and emissions of traditional internal combustion engine vehicles is difficult. In contrast, new energy vehicles are environmentally friendly and have low fuel consumption, which is important for the future development of vehicles. A plug-in hybrid electric vehicle (PHEV) is widely regarded as the most promising alternative solution for improving energy efficiency and reducing emissions. The optimization of the energy management strategy (EMS) mainly focuses on reducing fuel consumption and improving the economy. However, the durability of the power battery also needs attention, as the lack of life remains a major obstacle to the large-scale commercialization of PHEVs. Because PHEV batteries can obtain relatively cheap power through the grid, the traditional control strategy only considers the full use of the battery power but ignores its excessive use, which will accelerate the decline of the power battery capacity. Therefore, determining how to make full use of the battery power and control the decline of the battery capacity is a new research focus. Based on the semiempirical decay model of the battery, the energy management strategy of balancing the degradation of the battery capacity was established by introducing the battery utilization degree factor. The multiobjective optimization problem was transformed into a single-objective problem by selecting the appropriate weight factor through the Pareto noninferior target domain. A dynamic programming algorithm was used to obtain the global optimal solution of the weight coefficient. The optimal weight coefficient was selected by weighing the fuel consumption and battery capacity decline degree under different weights. In the case of equivalent fuel consumption, the decay rate of battery life can be effectively inhibited when the weight coefficient is 0.9. Finally, the validity of the proposed solution is verified by comparing the online equivalent consumption minimization strategy (ECMS) simulation with the dynamic programming solution under the same weight.

Traditional mine dump truck using diesel-electric drive technology have high energy consumption and serious pollution. To improve fuel economy of a hybrid power dump truck, firstly, it analyzes system composition, working mode and transportation condition characteristics of the hybrid power electric-wheel mining dump truck. Secondly, according to the mine driving conditions of the electric-wheel dump truck, an energy management strategy based on the driving pattern recognition is proposed according to the driver's intention and the vehicle state. Finally,to minimize the instantaneous equivalent fuel consumption of the whole hybrid power system, the equivalent consumption minimization strategy is used to optimize the instantaneous power distribution between the engine and battery. In order to verify the proposed energy management strategy, on site mining road test is conducted. Test results show that the fuel consumption of the hybrid power electric-wheel dump truck is 45.52 L/trip, which is 10.2% lower than that of the electric-wheel dump truck with full diesel engine mode.

Copyright of Journal of Henan University of Science & Technology, Natural Science is the property of Editorial Office of Journal of Henan University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Rolling Stock (1002-7602) is the property of Rolling Stock Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, a multi-objective optimization energy management strategy for a parallel hybrid electric vehicle is established to balance fuel economy and battery temperature rise effect. A vehicle dynamics model and a battery temperature rise model are also established. A multi-objective parameter optimization based on Pareto optimal solutions is built by selecting the control parameters of power components and several battery related parameters. Considering the temperature accumulation effect under high rate currents, the upper limit control strategy of a motor torque based on temperature rise feedback is developed, so that the current amplitude is actively controlled to realize battery temperature control. The analysis results indicate that frontier Pareto solutions obtained by multi-objective optimization have a good balance between both fuel economy and battery temperature rise response. The motor torque threshold control strategy effectively adjusts current amplitude under high speed conditions, thus achieving the limit of battery heat accumulation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Henan University of Science & Technology, Natural Science is the property of Editorial Office of Journal of Henan University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A hybrid power system has been widely used in Hybrid Electric Tractors (HETs) under farmland clustering and energy shortage in China. However, a great design difficulty can be found in the dynamic and variable load conditions of a working tractor on an unstructured road. Particularly, the powertrain flow can be used to realize the coupling and decoupling between the output power of the driven axle and Power Take-Off (PTO). It is a high demand to develop flexible powertrain for tractors, in order to improve the operation performance of agricultural machinery. Thus, the coupled-split powertrain system has been proposed with the principle of graph theory towards the single engine, dual motors, and clutch. All power ranges demand has been satisfied with the variable combination mode between the clutch and power units. In addition, the energy allocation can be optimized between the engine and motors in the background of non-linear loads. In this study, a Markov Decision Process (MDP) based Energy Management Strategy (EMS) was proposed to allocate the power between the engine and motors along with the dynamic and variable load. Firstly, the spectrum of the working load was collected in the period to distinguish the working scenarios. Specifically, the sample of the working environment included plains, hills, and basins. Secondly, the demand power in plowing was abstracted as the state transition of the MDP in the premise of tractor parameters collected under the plow condition, with which the comprehensive dynamics of tractor loads were mathematically formulated. Thirdly, energy consumption was defined as the cost function in the optimal control process, which was solved by the value iteration function. The working range of motor-2 was determined under the guidance of optimal control. In the actual plowing condition, the torque of motor-2 was optimized and determined along with the demand power and state of charge (SOC), which was converted to a look-up table and download in the Vehicle Control Unit (VCU). Finally, the effectiveness and feasibility of the system were validated with the hardware-in-loop test. Among them, the program was also conducted in the VCU on the actual test bench. Meanwhile, the model of the tractor was established for the co-simulation. The result indicated that the improved EMS reduced fuel consumption by 7.2%, compared with the traditional. The demand power forecast strategy further improved the energy efficiency characteristics of the drive motor in the plain plowing environment. In addition, the novel powertrain configurations of the tractor contained the direct and indirect coupled-split power system, which expanded the path of power flow between power units and wheels. Besides, the direct coupled-split configuration has the potential application to replace the technical difficulties of traditional power shifts and Continuously Variable Transmission (CVT). The new strategy can be expected to serve as high energy efficiency. The powertrain of coupled-split configuration can provide a strong reference to breaking through the difficult situation of power shift and CVT for high-power tractors. The finding can be expected to make great progress in the hybrid power system of the tractor in agricultural machinery. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to improve the fuel economy of hybrid electric vehicle (HEV) with single row planetary gear and reduce the fuel consumption of the HEV, an energy management strategy based on gated recurrent unit neural network (GRU-NN) speed predictive model and adaptive differential evolution (A-DE) algorithm was proposed. The future speed of HEV was predicted under the framework of model predictive control (MPC). The energy management strategy converted the global optimization solution problem in the entire working condition into a local optimization solution in the prediction time domain. Aiming at the lowest fuel consumption of the engine and the balance of battery state of charge (SOC) during driving, the optimal control sequence in the prediction domain was solved by A-DE. The simulation results show that the energy management strategy based on the GRU-NN and A-DE reduces fuel consumption by 4.55% compared with that of equivalent consumption minimization strategy (ECMS), and the fuel economy reaches 93.04% compared with that of dynamic programming (DP) under the driving cycle collected by vehicle. [ABSTRACT FROM AUTHOR]

The advantages of hybrid ships in energy conservation and emission reduction are increasingly prominent in the context of countries around the world paying increasing attention to such matters. Energy management is the key to achieving energy conservation and emission reduction on the premise of ensuring power performance. This paper summarizes the research progress and status of hybrid ship energy management. First, the energy management objectives are summarized in the three aspects of reliability objectives, economy objectives and environmental protection objectives. Second, the energy management strategies are compared and analyzed from the perspectives of rules and optimization, and the advantages, disadvantages and application conditions of the existing energy management strategies are summarized and analyzed deeply and comprehensively. Finally, according to the gaps in the existing research, the future research directions of hybrid ship energy management are predicted.

Hybrid technology has been regarded as an effective way to realize vehicle energy conservation and emission reduction. The P2 hybrid electric vehicles were taken as the research object. Considering that the state of charge (SOC) of battery is high, medium and low, the different energy management strategies were designed to maintain the balance of SOC while achieving the energy saving and emission reduction of hybrid electric vehicles. On the basis of rule, the multi-objective genetic algorithm in the Isight software was adopted by the strategy to optimize the threshold, so as to improve the reliability and effectiveness of the threshold in the energy management strategy. According to the simulation results, it indicates that the energy management strategy has a significant effect on the reduction of vehicle fuel consumption, which is also able to maintain the balance of battery SOC. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the driving conditions have a great influence on the fuel economy of the hybrid electric vehicle, a planetary hybrid truck is taken as the research object 1 the energy management strategy of the vehicle is established by combining driving condition recognition method, dynamic programming algorithm and neural network. The neural network is trained by using the optimal power distribution data of dynamic programming algorithm under four typical driving conditions, and the neural network models which are respectively suitable for each typical driving condition are obtained. During the control process, driving conditions are identified by the condition identification method. According to the cur-rent category of driving conditions, the corresponding neural network model is selected to optimize the distribution of demand power among power sources in real time. The simulation results show that compared with the energy management strategy before condition recognition, the vehicle fuel consumption decreases by 6. 25%,and the SOC of the battery is more stable, which is beneficial to the battery lifespan. [ABSTRACT FROM AUTHOR]

In order to further tap the fuel saving potential of hybrid heavy-duty trucks， energy management strategy based on dynamic programming is designed. Firstly， the quasi-static vehicle model of hybrid heavy-duty truck is established based on Matlab software platform， and the mathematical model of dynamic programming optimization control is built with the fuel economy taken as the objective function， the state of charge for the batteries taken as the state variable， and the torque distribution proportion coefficient of hybrid system taken as the control variable. Then， based on energy consumption test conditions of heavy-duty hybrid vehicles in China named C-WTVC， the simulation analysis of hybrid heavy-duty truck is carried out. Finally， based on the simulation results of dynamic programming， improved rules are extracted， and the C-WTVC simulation is carried out based on the joint simulation platform of AMESim and Simulink. The simulation results show that， compared with the rule-based energy management strategy， the energy management strategy based on dynamic programming can improve the fuel economy of hybrid heavy-duty trucks by 13.9%， and the control strategy based on improved rules can improve the fuel economy of hybrid heavy-duty trucks by 2.6%， which verifies the effectiveness of the improved rule-based strategy.

Objectives Aiming at the influence of load fluctuation on the fuel cell life and power quality of a fuel cell hybrid ship, an energy management strategy based on real-time wavelet transform is proposed.Methods By the frequency division processing of the power demand signal, each electrical power source undertakes the corresponding frequency part of the power demand according to its own electrical characteristics. Taking a fuel cell ferry as the target ship, a hybrid power system model simulation is built to verify the proposed algorithm.Results The experimental results show that this method canbe applied to fuel cells.Conclusions The proposed algorithm can prolong fuel cell/battery life and improve the power quality of a hybrid electric ship.

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Henan University of Science & Technology, Natural Science is the property of Editorial Office of Journal of Henan University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In Older to further tap the energy saving potential of power split hybrid electric vehicle, a speed prediction method combining driving intention, inter-vehicle motion characteristics and driving cycle history characteristic information was proposed. On this basis, aiming at the optimal fuel economy, the optimization model of vehicle energy management based on model predictive control was established, and the dynamic programming algorithm was used to solve the problem in the finite time domain. The real-time optimal torque distribution of each power source was realized. The simulation results of MATLAB/simulink platform show that the vehicle speed prediction accuracy is further improved in different prediction time domains under the urban dynamometer driving schedule. The fuel consuming of the vehicle energy management strategy is 28.53%, 23.40% and 26.42% lower than that of the optimized parameter strategy, respectively, which verifies the effectiveness of the vehicle speed prediction method and vehicle energy management. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Henan University of Science & Technology, Natural Science is the property of Editorial Office of Journal of Henan University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To further improve the fuel economy and online real-time optimization ability of energy management strategy for Hybrid Electric Vehicle（HEV）,the grey forecasting GM（1,1） model is established to forecast real-time vehicle speed,and then combined with Equivalent Consumption Minimization Strategy（ECMS） to be proposed HEV ECMS energy management strategy with grey forecasting method.Furthermore,the entire HEV simulation model is built,and the three different WVUCITY,WVUSUB and WVUINTER cycle is selected to simulate and analyze the effect of energy management.The simulation results show that,firstly,the precision of grey forecasting for speed is reliable.Secondly,the SOC will be more stable and battery has longer life by integrating grey forecasting and ECMS.Finally,the fuel economy is increased by 7.4%,7.5%,6.3%respectively through the proposed strategy,when compared with the non-forecasting method under the above three cycle.

The existing energy management strategy which is based on driving pattern recognition failed to fully consider the battery state of charge（ SOC） falling fast in some driving cycle into consideration in the progress of driving. The 23 typical driving cycles are chosen from ADVISOR software and five categories are divided by using clustering analysis method,the key parameters of each category are optimized by using particle swarm algorithm,with the goal of reducing fuel consumption,relevant optimized results are saved in database,an energy management strategy optimization method of HEV based on driving pattern recognition is proposed.Finally,the simulation analysis for the energy management is carried out under a comprehensive test cycle,simulation results show that vehicle fuel consumption is cut down 12. 77%,and the deviation of the battery SOC have greatly decrease compare with other energy management strategy which based on driving pattern recognition.

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For serial extended-range electric vehicle（E-REV）, the range extender miniaturization method was investigated according to the engine parameter matching. Four management strategies were proposed based on different rules by progressive optimization. An entire vehicle simulation platform was set up by Cruise/Simulink. Based on the platform, the explanations for fuel economy difference of strategies were analyzed according to engine operating point distribution, range extender efficiency and charge-discharge energy loss of battery. The off-line and HIL simulation results indicate that the main influence factors of fuel economy are range extender efficiency and charge-discharge energy loss of battery. The extended-range electric vehicle with miniature range extender cannot completely fit the common engine on-off and power following control strategy. The optimal curve control strategy has good fuel economy and control effect. [ABSTRACT FROM AUTHOR]

To solve the high energy consumption of standard active suspension system of vehicle, a 4 DOF energy-regenerative suspension system is established by taking the half-car as research object. A new energy-regenerative absorber, which has an actuator for active controlling and an energy-regenerator for energy harvesting, is designed to solve the problem of enabling to implement the active control and energy harvesting of energy-regenerative absorber at the same time. An energy management strategy based on the performing rate of active control is given according to the running conditions of vehicle. The simulated and experimental results show that, in comparison to the passive suspension, the comprehensive performance of the established energy-regenerative suspension with sliding mode control algorithm is improved by 22.700%, which indicates that the vibration control of the energy-regenerative suspension with sliding mode control is effective; 3 kinds of typical running condition, which are distinguished by road level and running speed, could be used as the criteria of the performing rate of active control. [ABSTRACT FROM AUTHOR]

A MPC5554-based hybrid control unit (HCU) is developed for a series-parallel hybrid electric bus ( SPHEB) in this paper. Firstly considering the operation features and powertrain configuration of SPHEB with minimizing fuel consumption as objective, a real-time optimal energy management strategy (EMS) based on iterative dynamic programming global optimization algorithm and Elman dynamic neural network is proposed. Then according to the vehicle requirements and EMS, the hardware system of HCU is designed with MPC5554 as CPU. Finally HCU hardware-in-the-loop simulation test is conducted with ETAS PT-LABCAR as platform. The results demonstrate that the developed HCU operates stable and reliable and can effectively realize real-time control for the SPHEB, significantly improving the fuel economy of the vehicle. [ABSTRACT FROM AUTHOR]

In view of the requirements of the special structural features of a series-parallel hybrid electric bus on its control strategy, an optimization simulation on its energy management strategy, which determines the power distribution between engine and battery, is conducted by using Pontryagin's minimum principle and based on the solution flow chart designed and the simulation model built, with battery power as control variable and minimizing fuel consumption as objective. The results demonstrate the marked effects of optimization on energy management strategy based on Pontryagin's minimum principle with the fuel consumption per 100km 18. 33% lower than that with rule-based control strategy. [ABSTRACT FROM AUTHOR]

Aiming at the effects of many factors such as uncertain driving environment and different driver's styles on the fuel economy of novel series-parallel hybrid electric bus (SPHEB) in real world driving conditions, a Markov model for driver's required power is built based on statistical data of several city driving cycles, and an energy management strategy based on stochastic dynamic programming (SDP) algorithm is proposed and the control rules for power distribution between engine and battery are obtained. Both the results of simulation on the forward model for novel SPHEB and hardware-in-the-loop test demonstrate that the proposed strategy enables the battery SOC to keep balance within its preset range and makes the engine operate in high efficiency region. In addition the fuel consumption per 100km of novel SPHEB reduces by 30.17% and 11. 29% respectively compared with original bus and that with rule-based strategy. [ABSTRACT FROM AUTHOR]

The vehicle control system (VCS) for a hybrid electric city bus is developed, including energy management strategy (EMS) and power coordination control (PCC). Based on the division and analysis of bus operation modes, the EMS determines the corresponding control strategy for each operation mode; while PCC uses the torque of the driving motor as compensation to realize AMT shifting without power interruption. In addition, the software of hybrid control unit (HCU) is developed using automatic code generation technology. The results of real vehicle test show that with the VCS developed, the fuel economy and power performance of the hybrid electric city bus are enhanced by around 26% and 17% respectively with emissions also improved. [ABSTRACT FROM AUTHOR]