Electric vehicles (EVs) have positive impacts on reducing oil dependence and exhaust emissions. However, the range problem of EVs is a factor that raises concerns for individual users and bus operators. For this reason, studies on increasing the range of the electric buses in public transportation is extremely important to ensure optimum operation. In this study, a novel energy consumption model, MTECM (Malatya Trolleybus Energy Consumption Model), is developed using the multi-parameter linear regression method. The real-time big data was collected on the field of Trolleybus vehicles, which have been operated for 8 years in Malatya / Turkey. Firstly, by calculating the correlation of the parameters affecting this model, the parameters that are suitable for the purpose of our study are determined and regression analysis is performed on the original Trolleybus dataset. A total of 75.497.472 data are analyzed for this model. The RMSE (Root Mean Square Error) of MTECM is calculated as 0.29996. The trained model is applied to the 10 busiest routes in Malatya in terms of passenger density. The RMSE value on these routes is calculated between 0.30299 and 0.31421. Based on the results, with lower error rates, the proposed novel model is more efficient than other studies in the literature. In addition, energy consumption can be calculated for any route planned to establish an electric bus operation with MTECM. Therefore, according to the consumption obtained, the correct determination and selection of parameters that significantly affect the investment cost such as route, vehicle length, engine power, and battery capacity can be made.

Battery selection remains an up-to-date engineering problem for hybrid and electric vehicle manufacturers. Type of battery and its capacity will depend on the trip and vehicle parameters. An electric vehicle produced with the ideal bat-tery type will undoubtedly be preferred by customers. Data collected from black boxes of trolleybuses operated by Malatya Metropolitan Municipality were used in this study. The real road and driver characteristics were included in the study with the experimentally obtained data. These data are the accelerator pedal data obtained from vehicles driven by different drivers in regular and congested traf-fic hours. In this study, four different battery chemistries were run separately on a hybrid vehicle model and analyzed. Chosen battery chemistries are the most commonly used by manufacturers. These are Lead Acid, Nickel Cadmium, Nickel Metal Hydride and Lithium Iron Phosphate batteries. The results of the study are presented in detail comparatively. Among the battery chemistries, Lithium iron phosphate is observed to be the most ideal battery type for hybrid electric vehi-cles.

Storage of electrical energy is one of the most important technical problems in terms of today’s technology. The increasing number of high-capacity high-power applications, especially electric vehicles and grid scale energy storage, points to the fact that we will be faced with a large number of batteries that will need to be recycled and separated in the near future. Additionally multi-chemistry battery management systems that enables the collective use of superior features of different batteries with different chemical composition. Here, battery chemical composition determination emerges as a technical problem. In this study, an alternative method to the currently used methods for categorizing batteries according to their chemistry is discussed. As the foundation, batteries with four different chemical composition including Lithium Nickel Cobalt Aluminium Oxide, Lithium Iron Phosphate, Nickel Metal Hydride, and Lithium Titanate Oxide aged with a battery testing hardware. Fifth, is Lithium Sulphur battery which is simulated. Brand new and aged batteries are used in experimental setup that is consist of a programmable electronic DC load and a software developed to run the algorithm on it. According to the algorithm, batteries are connected to two different loads one by one and voltage-current data are stored. Collected data are pre-processed by framing them and framed data are processed with a separation function. Eventually, the determination problem is converted to a classification problem. In order to solve this, artificial neural network and classification tree algorithms are applied. Because the artificial neural network algorithm is applied in previous studies and the high computational cost of it is presented; classification tree algorithm is concluded to be more applicable especially on low-power microcontroller applications. Consequently, 100% accuracy for battery chemical composition determination is achieved and results are presented comparatively.

Archimedes Optimization Algorithm (AOA) is a recent optimization algorithm inspired by Archimedes’ Principle. In this study, a Modified Archimedes Optimization Algorithm (MDAOA) is proposed. The goal of the modification is to avoid early convergence and improve balance between exploration and exploitation. Modification is implemented by a two phase mechanism: optimizing the candidate positions of objects using the dimension learning-based (DL) strategy and recalculating predetermined five parameters used in the original AOA. DL strategy along with problem specific parameters lead to improvements in the balance between exploration and exploitation. The performance of the proposed MDAOA algorithm is tested on 13 standard benchmark functions, 29 CEC 2017 benchmark functions, optimal placement of electric vehicle charging stations (EVCSs) on the IEEE-33 distribution system, and five real-life engineering problems. In addition, results of the proposed modified algorithm are compared with modern and competitive algorithms such as Honey Badger Algorithm, Sine Cosine Algorithm, Butterfly Optimization Algorithm, Particle Swarm Optimization Butterfly Optimization Algorithm, Golden Jackal Optimization, Whale Optimization Algorithm, Ant Lion Optimizer, Salp Swarm Algorithm, and Atomic Orbital Search. Experimental results suggest that MDAOA outperforms other algorithms in the majority of the cases with consistently low standard deviation values. MDAOA returned best results in all of 13 standard benchmarks, 26 of 29 CEC 2017 benchmarks (89.65%), optimal placement of EVCSs problem and all of five real-life engineering problems. Overall success rate is 45 out of 48 problems (93.75%). Results are statistically analyzed by Friedman test with Wilcoxon rank-sum as post hoc test for pairwise comparisons.

Electrification in urban transportation is becoming more popular, beside it is also becoming a necessity due to climate changes and sustainability issues. Trolleybuses are presenting an alternative for this purpose. Although their technology is a mature technology that has been used for decades, there are still some technical problems that need to be overcome. In this study, a technical method is presented for the conversion of trolleybus auxiliary power units. The electrification conversion demanded by the metropolitan public transportation company operating 22 trolleybuses in the province of Malatya is the replacement of diesel generators, used as auxiliary power units, with battery units capable of meeting the local operational requirements. For this purpose, a method is proposed and followed. At first step of the implementation, real-time data has gathered from a trolleybus and this one round tour data is used to run on a scaled experiment. The setup has prepared as hardware and software to simulate the consumption on a scaled battery pack. Experimental results were interpreted with capacity and voltage restrictions resulting the determination of battery chemistry and casing to be used. Then optimal battery placement was defined as a container loading problem and application was made with the first fit decreasing algorithm considering mass and volume restrictions. It was found that only two packing types out of six combinations is enough to form a battery pack within the mass and volumetric limitations. It is evaluated that the method adopted in this study can be used in conversion applications to be made in the future.

The reality of global warming brings along an increase in environmental aware-ness. In recent years, this awareness has shifted public focus on electric vehicles. For a large group of people greenhouse gas emission is attributed to internal combustion engines. However, some challenges have arisen for electric vehicles. Limited range due to immature battery technologies and insufficient fast charging technologies that do not meet end user expectations are some major obstacles to overcome. Eventual-ly this situation negatively affects the sales and the wide use of electric vehicles. That is the reason why studies on wired and wireless charging systems play an important role in improving the sales performance of electric vehicles. In this study a thorough review of worldwide electric vehicle charging systems is conducted and discussed in the framework of the electric vehicles, charging stations, installations, and implemen-tation of standards in Turkey. The distribution of charging stations in Turkey are analyzed with respect to location, region, type, infrastructure requirements and future projections. The historical development of charging technologies, modes and charge levels have been studied in detail. As highlight of this study, wireless charging tech-nologies were also discussed and the historical development process was analyzed along with related standards. The current state of electric vehicle sector and charging stations in Turkey are discussed and provided with up to date information.