Currently electric vehicle (EV) charging stations are equipped with chargers having fixed charging rates where the users do not have control over their charging schedule, except to decide whether they require a full recharge or partial recharge. In addition, no matter what kind of charging technique is adopted, EV charging takes much time compared to refueling conventional vehicles. This lengthy time at a charging station is the reason why people are hesitant to switch to EVs from internal combustion engine vehicles. The long waiting time may lead to congestion at the charging stations. This has motivated us to develop an efficient multi-level charging system (MlCS) that allows EV customers to specify their charging needs and provide them with charging at the best charging rate possible. The proposed MlCS put forward here features multiple charging levels on a single charger and each level corresponds to a distinct charging rate. Taking the interest of customers into account, the selection of charging rate for the customer is based on his priority for battery lifetime and recharging time. Further, the MlCS is integrated into the station planning problem to analyze the effect of MlCS on station capacity estimation and their impact on the power grid. Finally, the numerical results illustrate that MlCS is better for improving the customer quality of service. The contribution of the proposed model allows decision-makers and operators to develop charging stations with multi-level chargers and provide a better service for customers with different charging options.

Nature-inspired metaheuristic optimization algorithms, e.g., the butterfly optimization algorithm (BOA), have become increasingly popular. The BOA, which adapts the food foraging and social behaviors of butterflies, involves randomly defined, algorithmic-dependent parameters that affect the exploration and exploitation strategies, which negatively influences the overall performance of the algorithm. To address this issue and improve performance, this paper proposes a modified BOA, i.e., the quantum chaos BOA (QCBOA), that relies on chaos theory and quantum computing techniques. Chaos mapping of unpredictable and divergent behavior helps tune critical parameters, and the quantum wave concept helps the representative butterflies in the algorithm explore the search space more effectively. The proposed QCBOA also implements a ranking strategy to maintain balance between the exploration and exploitation phases, which is lacking in conventional BOAs. To evaluate reliability and efficiency, the proposed QCBOA is tested against a well-utilized set of 20 benchmark functions and travelling salesman problem which belongs to the class of combinatorial optimization problems. Besides, the proposed method is also adopted to photovoltaic system parameter extraction to demonstrate its application to real-word problems. An extensive comparative study was also conducted to compare the performance of QCBOA with that of the conventional BOAs, fine-tuned particle swarm optimization (PSO) algorithm, differential evolution (DE), and genetic algorithm (GA). The results demonstrate that, chaos functions with the quantum wave concept yield better performance for most tested cases and comparative results in the rest of the cases. The speed of convergence also increased compared to the conventional BOAs. The proposed QCBOA is expected to provide better results in other real-word optimization problems and benchmark functions.

The automobile industry is regarded as one of the primary producers of greenhouse gas emissions. Hence, one major approach for promoting global sustainability is vehicle electrification. Energy source hybridization is imperative in vehicle electrification because no alternative clean energy source can match the performance of vehicles with internal combustion engines. For a compact energy source system with minimal source degradation and low operational costs, it is crucial to determine the optimal sizing of multiple sources in a hybrid electric vehicle (HEV). The objective of this article is to conduct a thorough assessment of the optimal energy source sizing methodology for HEV alongside an analysis of energy management systems. The importance of dynamic energy source characteristics in the optimal source design is explained and the dynamic modelling of these sources is investigated in detail. Moreover, this paper discusses the dynamic source degradation models used in the literature. This analysis on HEV component sizing provides a comprehensive picture of the current state-of-the-art and highlights several shortcomings, difficulties, and knowledge gaps. The findings indicate that the establishment of an energy management system and energy source sizing are interdependent activities that can prolong the usable life of the energy sources and reduce energy consumption. The aging, temperature, depth of discharge, and charging state are found to be essential characteristics of vehicle energy sources, yet these dynamic aspects have largely been neglected in previous papers. Additionally, if the source deterioration and associated factors are considered throughout the design phase, the HEV system can be built with long-life characteristics. The assessment conclusions highlight the significance of continued analysis to overcome obstacles and improve the hybrid automobile sector.

Monitoring electricity energy usage can help to reduce power consumption considerably. Among load monitoring techniques, non-intrusive load monitoring (NILM) provides a cost-efficient solution to identify individual load consumption details from the aggregate voltage and current measurements. Existing load monitoring techniques often require large datasets or use complex algorithms to obtain acceptable performance. In this paper, a NILM technique using six non-redundant current waveform features with rule-based set theory (CRuST) is proposed. The architecture consists of an event detection stage followed by preprocessing and framing of the current signal, feature extraction, and finally, the load identification stage. During the event detection stage, a change in connected loads is ascertained using current waveform features. Once an event is detected, the aggregate current is processed and framed to obtain the event-causing load current. From the obtained load current, the six features are extracted. Furthermore, the load identification stage determines the event-causing load, utilizing the features extracted and the appliance model. The results of the CRuST NILM are evaluated using performance metrics for different scenarios, and it is observed to provide more than 96% accuracy for all test cases. The CRuST NILM is also observed to have superior performance compared to the feed-forward back-propagation network model and a few other existing NILM techniques.

The hybridization of multiple energy sources is crucial for electric vehicles to deliver the same performance as modern fossil fuel-based automobiles. This paper presents a torque and speed control strategy for an electric vehicle DC traction motor by regulating the power flow from two energy sources, namely battery and supercapacitor systems, to resist unpredictable disturbances. A control system with three control loops is applied to regulate the speed of the traction motor. The outer speed control loop is a nonlinear state feedback controller with a disturbance observer, which is capable of handling non-linear systems with unpredictable disturbances. The inner voltage and current control loop are precisely tuned PI controllers. Using an adaptive energy management method that varies depending on the state of charge of the source, the total reference current needed to support the load demand is split between two sources. A laboratory prototype system model is generated, and the performance is analyzed under motoring and regenerative braking conditions. For monitoring and controlling the system, the dSPACE DS1202 real-time simulator is employed. The performance of the proposed control system is evaluated and it is found that the settling time to settle within a tolerance band of 1% is 0.75 s.