Abstract
In order to complete the reasonable parameter matching of the pure electric vehicle (PEV) with a hybrid energy storage system (HESS) consisting of a battery pack and an ultra-capacitor pack, the impact of the selection of the economic index and the control strategy on the parameters matching cannot be ignored. This paper applies a more comprehensive total cost of ownership (TCO) of HESS as the optimal target and proposes an optimal methodology integrating parameters and control strategy for the PEV with HESS. Through the integrated optimal methodology, the application value of HESS is analyzed under various types of driving cycles and the results indicate that the HESS can significantly improve the economic performance of PEVs under both urban and suburban driving cycles. Due to the poor adaptability of traditional control strategies to different driving cycles, a novel extreme learning machine (ELM) based controller is established. Firstly, a dynamic programming (DP) based controller is applied for the offline optimization of the HESS power allocation under several typical driving cycles. Then, an analytical method combining correlation analysis and mean impact value (MIV) is employed to deal with offline sample data from DP and obtain the characteristic variables of the ELM model. Ultimately, the instantaneous power allocation strategy of HESS is acquired by utilizing ELM to learn offline data of HESS. Comparative simulations between the ELM-based controller and the rule-based controller are conducted, and the simulation results show that compared to the rule-based controller (RBC), the ELM-based controller reduces the electricity consumption by 3.78% and battery life loss by 6.51%.
Highlights
A comparative work about the three kinds of controller was carried out by [19] and the results showed that the rule-based controller and fuzzy logic controller could achieve similar economic enhancement of hybrid energy store system (HESS) with the global optimal control strategy under certain driving cycles
To improve the adaptability of the control strategy to driving cycles, the dynamic programming (DP)-based strategy is applied to several typical driving cycles and the offline data of optimal instantaneous power allocation for HESS is obtained, and the characteristic variables of the extreme learning machine (ELM) model are acquired through the method combining the correlation analysis and the mean impact value (MIV)
In order to specify the application value of HESS under different types of driving cycles, a minimum driving range of 200 km and mileage of 180,000 km are taken as the constraints, and the integrated optimal methodology is applied to the pure electric vehicle (PEV) with HESS and the PEV with the battery energy store system (BESS) under various driving cycles
Summary
Pure electric vehicles (PEVs) have broad development prospects due to their zero emission and pollution property [1]. Some problems such as high battery costs, short lifespan, low energy density, and power density limit the further progress of PEVs. The hybrid energy store system (HESS). Consisting of a battery pack and an ultra-capacitor pack can almost solve the problems above by combining the advantage of the battery and ultra-capacitor [2], which has caused the HESS to become a hot issue in the research and application area of PEVs. the two energy sources of HESS contribute to the great flexibility of the parameter matching and control strategy. Research related to the parameter matching and control strategy is reviewed
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