Power Management for Connected EVs Using a Fuzzy Logic Controller and Artificial Neural Network

Abstract

In recent years, the electric vehicles (EVs) power management strategy has been developed in order to reduce battery discharging power and fluctuation when an EV requires high and rapid discharging power due to frequent stop-and-go driving operations. A combination of lithium-ion batteries and a supercapacitor (SC) as the EV’s energy sources is known as a hybrid energy storage system (HESS) and is a promising solution for fast discharging conditions. Effective power management to extensively utilize HESS can be developed if future power demand is accessible. A vehicular network as a typical form of the currently developed internet of things (IoT) has made future information obtainable by collecting information on surrounding data. This paper proposes a power management strategy for the HESS with the support of IoT. Since the obtained information from vehicular network could not directly be used to improve HESS, a two levels control structure has been developed to perform future data prediction and power distribution. A fuzzy logic controller (FLC) is utilized in the level one control structure to manage a HESS power split based on future information. Since FLC requires future information as a reference input, the future information is obtained by using an artificial neural network (ANN) in a level two control structure. The ANN prediction is direct, which could approximate the future power demand prediction with the assumption that the vehicular network scenario that is used to obtain surrounding information is deployed. Simulation results demonstrate that the average discharging battery power and power variation are reduced by 46.1% and 52.3, respectively, when compared to the battery-only case.

Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review

“…The literature, as encapsulated in references [71,[126][127][128]136,[162][163][164][165], underscores the wide-ranging applications and empirical validations of this combined approach. These references likely detail specific instances where the fuzzy logic and neural network synergy has been applied successfully, showcasing its effectiveness in optimizing energy distribution, improving fuel efficiency, and enhancing overall vehicle performance.…”

Critical review on optimal regenerative braking control system architecture, calibration parameters and development challenges for EVs

“…Also, in some cases, a combination of fuzzy and PID controllers is employed in the regenerative braking strategy, which can split the mechanical and electrical braking force dynamically. 64 Also, it can increase the driving range of EVs while maintaining braking quality. As discussed in Reference 65, the reinforcement learning (RL) method is another modelling approach used to adjust and improve a fuzzy logic model for regenerative braking by tuning the model for a specific EV using actual data gathered from field tests.…”

“…When the driver presses the BP, the controller distributes the braking force between the wheels according to the input and driving conditions. Also, in some cases, a combination of fuzzy and PID controllers is employed in the regenerative braking strategy, which can split the mechanical and electrical braking force dynamically 64 . Also, it can increase the driving range of EVs while maintaining braking quality.…”

Real-Time Management for an EV Hybrid Storage System Based on Fuzzy Control

“…Afterwards, SC can charge the battery or utilize a low-pass filter for that case to avoid high frequencies. The rules that comprise the fuzzy model are the following [35,36]: 7.…”

“…The regulation of charging and discharging as well as energy demand coverage are priorities shown in Figure 8 below: Next, the power management flowchart shows the strategy followed by the FL controller to cover the power demand and handle the output of both sources. Membership functions are shown in previous works [32][33][34][35][36][37][38], based on the Mamdani model, and are projected in this paper as illustrated in Figure 7. The fuzzy model incorporates a small difference in rule definition (low to high) to specify the value of temperature and duty cycle in the power distribution scenarios implemented.…”