# 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 battery and supercapacitor (SC) as the EV's energy sources, known as a hybrid energy storage system (HESS), 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 the vehicular network could not directly be used to improve HESS, a two-level control structure has been developed to perform future data prediction and power distribution. A fuzzy logic controller (FLC) is utilized in 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 level two control structure. The ANN provides a direct prediction that could approximate the future power demand prediction with the assumption that the vehicular network scenario is deployed to obtain surrounding information. Simulation results demonstrate that the average discharging battery power and power variation are reduced by 46.1% and 52.3%, respectively, compared to the battery-only case. artificial neural network; electric vehicle; fuzzy logic controller; hybrid energy storage system; power management strategy; power prediction +
Footnote †: journal: 26 November 2021
efficiency in power delivery [3]. Therefore, lithium-ion batteries are chosen for this work. Several developments to enhance the battery technology for EVs have been realized in recent years. One possible solution is to utilize a hybrid energy storage system (HESS) [4]. HESS is a combination between a lithium-ion battery and a supercapacitor (SC) for an EV's energy source in order to reduce battery stress level and fluctuation, which leads to longer battery life and better EV performance [5; 6]. The battery has a high energy density, and it is utilized to provide low to moderate power levels over a long-term duration. On the other hand, SC has high power density, but low energy capacity to support high instantaneous power levels and absorbs regenerative braking energy [7]. Therefore, the SC is typically used as an auxiliary power source to support the battery to supply fast burst power demand. A power management strategy is crucial to perform a power split between the battery and SC. The optimization of the HESS power split in power management strategy has gained more attention recently. The study in [8] adopts a rule-based strategy for a nonlinear control technique to perform the HESS power split. However, the rule-based strategy provides a strict condition that may not be optimal with regard to control issues. A fuzzy logic controller (FLC) is a widely adopted approach to perform a HESS power split and could provide more in control flexibility with low complexity. The study in [9; 10; 11] directly utilizes the FLC as a single controller without any concern about reference inputs for FLC. In a further study in [12], FLC is optimized using particle swarm optimization, which uses current measured power demand and no future power demand prediction. Thus, the potential of HESS is not fully utilized due to the absence of future information. The management strategy is also developed by using model predictive control (MPC) in [13; 14]. The MPC strategy model proposed in [13] requires heavy and complex computation due to large number of matrices that may lead to a late decision in power distribution.