Abstract
The energy storage system (ESS) is the main issue in traction applications, such as battery electric vehicles (BEVs). To alleviate the shortage of power density in BEVs, a hybrid energy storage system (HESS) can be used as an alternative ESS. HESS has the dynamic features of the battery and a supercapacitor (SC), and it requires an intelligent energy management system (EMS) to operate it effectively. In this study, a real-time EMS is proposed, which is comprised of a fuzzy logic controller-based low-pass filter and an adaptive proportional integrator-based charge controller. The proposed EMS intelligently distributes the required power from the battery and SC during acceleration. It allocates the braking energy to the SC on the basis of the state of charge. A simulation study was conducted for three standard drive cycles (New York City cycle, Artemis urban cycle, and New York composite cycle) using MATLAB Simulink. Comparative analysis of conventional and proposed EMSs was carried out. The results reveal that the proposed EMS reduced the stress, temperature, and power losses of the battery. The steady-state charging performance of the SC was 98%, 95%, and 96% for the mentioned drive cycles.
Highlights
The transportation sector plays a key role in energy consumption and greenhouse gas emissions, triggering global warming with limited fossil fuel resources and price fluctuations, which leads to the need for investigating an alternate energy source [1,2]
This paper proposes a real-time energy management system (EMS) consisting of an adaptive charging controller for a SC and an adaptive low-pass filter (A-LPF) for a battery–supercapacitor semi-active hybrid energy storage system (HESS)
An energy management system for a semi-active hybrid electric vehicle using an adaptive low-pass filter and an adaptive charging controller was implemented in this study
Summary
The transportation sector plays a key role in energy consumption and greenhouse gas emissions, triggering global warming with limited fossil fuel resources and price fluctuations, which leads to the need for investigating an alternate energy source [1,2]. The rule-based EMS implemented in References [16,30], which was based on expert experience, used a rule-based controller considered a load power and the SOC of an HESS to improve the range and the performance of the electric vehicle (EV). The rule-based method does not require any prior information about the drive cycle, but it does not consider the frequency components in load demand, which is very harmful to battery life These rules were analyzed based on the initial state of the HESS and cannot precisely reflect the conditions of the system components after a long period of operation. This paper proposes a real-time EMS consisting of an adaptive charging controller for a SC and an adaptive low-pass filter (A-LPF) for a battery–supercapacitor semi-active HESS.
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