SummaryAiming at the deficiency of optimal control energy management strategy, a model of energy management controller for hybrid electric vehicle (HEV) is constructed based on Kernel Fuzzy C‐means Clustering (KFCM) and multi‐neural network. Using energy management control strategy based on PMP, the operational parameters of the four driving modes for HEV is extracted; the data cluster corresponding to the driving mode is generated by clustering through the KFCM method and is used as the training samples for the feedforward neural network. Taking the battery SOC, needed power and speed as the inputs of neural network, and taking engine power as the output of neural network, four sub‐neural network models are established. Taking the vehicle driving needed power at the current moment and the engine output power at the previous moment as characteristic parameters, the corresponding sub‐neural network model is selected for output prediction according to the proportional relationship between the driving demand torque and the engine output power. The simulation results show that, compared with the energy management strategy based on PMP, the calculation time is greatly shortened using the proposed control strategy, and the real‐time performance is better. The fuel economy is a little decreased under the condition of meeting the requirements, but better dynamic performance can be obtained.
For achieving higher energy transferring efficiency from the resources to the load, the Combined Cooling, Heating, and Power (CCHP) systems have been widely researched and applied as an efficient approach. The key idea of this study is designing a novel structure of a hybrid CCHP system and evaluating its performance. In this research, there is a hybrid energy storage unit enhancing the whole system’s operation flexibility while supplying cooling, heating, and power. An ORC system is integrated into the CCHP system which takes responsibility of absorbing the low-temperature heat source for electricity generation. There are a few research studies focusing on the CCHP systems’ performance with this structure. In order to evaluate the integrated system’s performance, investigation and optimisation work has been conducted with the approaches of experimental studies and modelling simulation. The integrated system’s configuration, the model building process of several key components, the optimisation method, and the case studies are discussed and analysed in this study. The design of the integrated system and the control strategy are displayed in detail. Several sets of dynamic energy demand profiles are selected to evaluate the performance of the integrated system. The simulation study of the system supplying selected scenarios of loads is conducted. A comprehensive evaluation report indicates that the system’s efficiency during each study process differs while supplying different loads. The results include the power supplied by each component, the energy consumed by each type of load, and the efficiency improvements. It is found that the integrated system fully satisfies the selected domestic loads and various selected scenarios of loads with high efficiency. Compared to conventional power plants or CHP systems, the system efficiency enhancement comes from higher amount of recovery waste heat. Especially, the ORC system can absorb the low-temperature heat source for electricity generation. Compared to the original following electrical load (FEL) control strategy, the optimisation process brings overall efficiency improvements. The system’s overall efficiency was increased by from 3%, 3.18%, 2.85%, 17.11%, 8.89%, and 21.7% in the second case studies. Through the whole study, the main challenge lies within the design and the energy management of the integrated system.
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