Optimal Control Strategy to Maximize the Performance of Hybrid Energy Storage System for Electric Vehicle Considering Topography Information

Abstract

This research designed an energy management system involving a battery-supercapacitor Hybrid Energy Storage System (HESS) for electric vehicles (EV). The objective is to improve the performance of the HESS by combining battery and supercapacitor features, accounting for topographical information to guarantee continuous hybridization during the drive cycle. Contour Positioning System (CPS) was used to determine the slope of the rode travelled by the vehicle. Two adaptive algorithms were designed for a rule-based controller to control the energy shared between the battery and the supercapacitor; an optimal adaptive controller and fuzzy adaptive controller. The HESS model, electric vehicle and controllers were tested using MATLAB/Simulink with three real drive cycles, namely, uphill, downhill and city tour, in three different speeds 50Km/h, 60Km/h and 70 Km/h. The results proved the controllers managed to extend battery life-cycle by reducing the stress on the battery for the drive cycles. The results were compared in terms of energy consumption for the optimal adaptive rule-based controller and fuzzy adaptive rule-based controller. The optimal adaptive rule-based controller guaranteed the HESS was able to operate continuously and extend the number of drive cycles in a wide range of speeds and road slopes. INDEX TERMS Electric vehicles; Energy storage; Energy management; Navigation.

Challenges of Electric Vehicles and Their Prospects in Malaysia: A Comprehensive Review

“…The performance of HESS can be improved by combining battery and supercapacitor features. An optimal adaptive, fuzzy adaptive controller is used to control the energy shared between the battery and supercapacitor [123]. In [124], an extended Kalman filter (EKF) and traditional coulomb counting (CC) structure are used for estimating the state of charge (SOC) of a hybrid power management (HPM) system composed of fuel cell HEV and supercapacitors.…”

Battery Energy Management Techniques for an Electric Vehicle Traction System

“…The FLC in [13] shifts the abrupt energy demand to an SC to reduce the discharge stress on the battery during the acceleration phase. On the other hand, [14] proposes an adaptive fuzzy controller which shares the energy consumption between a battery and an SC by means of a DC-DC converter. A contour positioning system was used to obtain the road slope information which was used to estimate the energy required to reach the destination.…”

“…Coulomb counting (CC) is considered one of the conventional techniques and is commonly used in EV literature due to its computational simplicity [46]. A few notable works that compare different energy management strategies and utilize CC to obtain the SOC are [12], [14], [25], [32], [47]- [49]. The mathematical formulation of the CC method is shown in (31).…”

Particle Swarm Optimization of a Hybrid Energy Storage System

“…Thus, also the acceleration can be determined in each moment to derive the mechanical power required by the given speed profile. As we don't aim to detail the mechanical model of the vehicle, let's consider that the required mechanical power at a given moment is a function of the instantaneous speed 𝑣 𝑣 and acceleration 𝑎 𝑣 , and the required electrical power 𝑝 𝑒𝑙_𝑟𝑒𝑞 results from this by considering a global efficiency 𝜂 𝑣 [10], according to (1):…”