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Summary: Electric vehicle (EV) is considered as a critical requirement to the future development of transportation. However, the battery performance in terms of power density and energy density limits the use of EVs. An energy management system (EMS) of plug‐in hybrid electric vehicle (PHEV) is very critical to achieve successful transition from the conventional vehicle to the pure electric vehicle (PEV). This paper proposes a hybrid EMS for the series‐parallel PHEV utilising a rule‐based control strategy and genetic algorithm (GA)‐based optimisation technique to overcome the battery limitations. A mathematical model was developed and verified by conducting simulation studies using the vehicle model from ADVISOR database and the GA Optimization Toolbox (GAOT) in the Matlab Simulink environment. The simulation results show that the GA optimization successfully achieved the sub‐targets set in the fitness function. To show the effectiveness of the proposed technique, the results were compared with the simulation results of a single function of the designed rule‐based control strategy—the proposed EMS achieved a significant improvement in the hydrocarbon (HC) emission and NOx emission. [ABSTRACT FROM AUTHOR]

Summary: Energy consumption is increasing in parallel with the population and technological advancements. Albeit the energy efficiency efforts, industrial consumption, and transportation have quite important roles in this increase. The electric vehicles (EVs) are expected to constitute a positive solution to mitigate energy use in transportation. Increasing penetration of EVs into public transport provides new opportunities both for environmental sustainability and transportation infrastructure. To project the potential operator revenue, we set up a hybrid model (solar and storage), which optimizes scheduling for the EV charging unit and evaluates environmental and economic benefits. Different operational scenarios of PV‐assisted charging stations will be simulated and solved with Rule‐based decision making. Since regulations have an important role in building the optimization model, scenarios are created considering different European regulations. Uncertainties depending on the traffic jam (arrival‐departure times), technological differences of the charging stations are encountered in the model. The results are compared among three different scenarios. Net present value of the system changes for different scenarios and electricity sale price is one of the main factors. This value tends to increase after an annual electricity sale price increase rate of 11%. Novelty statement In this study, the effect of different economic parameters on an electric vehicle charging station were analyzed. A system with electric vehicles, solar panel support, and external battery was mathematically modeled and a case study was done. [ABSTRACT FROM AUTHOR]