Fuzzy-PI based hybrid energy storage system topology for electric vehicles

Nowadays, electric vehicles have inspired many researchers and manufacturers to use them as an alternative to fuel vehicles with zero carbon emissions, making them safer for the environment. However, there are significant technical barriers to widespread adoption of battery electric vehicles (BEVs), such as shorter driving ranges, longer charging times, and limited battery capacity and volume. Previous research has suggested using hybrid energy storage systems (HESS) such as supercapacitors, flywheels, and solar power as auxiliary power sources rather than batteries alone to extend battery life and driving range. As a HESS in electric vehicles, the supercapacitor and battery are used to complement each other in this thesis. Due to their high power density and lack of chemical reaction, supercapacitors can be used in BEVs to mitigate instantaneous power requirements. The modelling of battery, supercapacitor, and battery-supercapacitor models has been studied and developed using Matlab simulation with experimental validation data. The proposed energy management system (EMS) with the control strategy of the fuzzy-PI validated the proposed topology between battery-supercapacitor. This EMS with the proposed fuzzy rules enables the battery to supply average power and also enables the supercapacitor to supply high peak power to achieve the battery’s current peak reduction in a short period of time. The different speed profile patterns in EVs differ from smooth driving to aggressive driving, so three different driving cycles are used in this thesis. These driving cycles are the urban dynamic driving cycle (UDDS), the New European Driving Cycle (NEDC) and the Supplemental Federal Test Procedure (US06). EV was tested in four different case scenarios with initial battery state of charge (SoC) conditions of 100, 80, 60, and 40 percent of battery capacity for each full driving cycle. The results of the proposed topology and control strategy using HESS in comparison to BEV have been highlighted in terms of SoC, voltage, current, power, and battery energy consumption. This research improves the modelling process of a battery by estimating the remaining capacity inside the battery cell by using terminal voltage. The model has been validated against experimental data with a maximum relative error of 0.015V compared to 0.045V in previous work. In supercapacitor modeling, a novel method for parameter identification is proposed for comparison to the sophisticated methods in the literature. The terminal voltage was validated experimentally with a maximum relative error of 0.045 V, compared to a standard deviation of 0.19 V for a similar experimental test profile used in the literature. The proposed topology is validated against the full active topology in the literature. The results showed an improvement in the proposed topology of 55% in SoC compared to 30% in full active topology in the literature. The EMS (Fuzzy-PI) results showed that using HESS instead of BEV resulted in 82.6 percent increase in energy consumption. Also, the battery's current peak decreased by 81.8 percent using HESS compared to BEV.After benchmarking to eight prior studies using three different cycles (UDDS, NEDC, and US06), the greatest increase in energy consumption was 38.8%, compared to 17% in the literature. The proposed HESS reduces battery peak current by 45 A, compared to 59 A in previous work. The HESS has been proven to be useful in the near future for electric vehicles.

Text FK 2022 96 IR.pdf Download (2MB)

Actions (login required)

View Item