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An electric vehicle is one of the crucial technologies for transportation of passengers and goods to reduce the consumption of fossil fuel which is the main factor of air pollution. Currently, energy and power density of batteries are low when compared to fossil fuel, having an effect that electric vehicles have limited driving range. As a result, simulating velocity trajectory for optimal energy consumption for electric vehicle is necessary. This paper introduced the algorithm to determine optimal velocity trajectory or velocity profile to consume the least energy in the required cycle with the condition that the optimal velocity had to be similar to the profile of the reference velocity or the original velocity so that it was capable of driving in the traffic circumstance of the sample cycles. To find the optimal velocity with the least energy consumption, the simple vehicle model and mathematic approach to simulate optimal vehicle trajectory by particles swarm optimization (PSO) were used in this study. The route used in the study were divided into elementary driving cycles. The algorithm will simulate the acceleration and create the optimal velocity trajectory of each segment to find the trajectory that consumed the least energy regarding the condition of driving time determined by user. The result of the simulation found that the algorithm reduced the consumption of energy and maximum electric power with significance. That were 13.44% and 14.225% respectively when the electric vehicle was determined to arrive at the destination by 1 minute late.

In the electric vehicle industry, a good estimation of a traction battery pack or the state of charge (SOC) is crucial as it reflects how far a vehicle travel before recharging. As the battery degrades, its behavior and the associated parameters such as internal resistance, capacity and SOC-OCV (open circuit voltage) mapping changes. Thus, a battery model has to take into account the changes in the battery parameters for it to be accurate throughout the battery lifetime. For such a model to be computational intensive, it requires powerful processors. With limited calculation performance processors found in vehicles, the model fidelity is normally compromised. In this paper, two battery models are used to accurately estimate traction battery SOC; The Ohmic resistance model is used to sense changes in battery internal resistance, when the change is significant, the resistor-capacitor (RC) model is used to update the battery SOC-OCV curve which is used to estimate the battery initial SOC. Hence, the coulomb counting method is used to update the battery SOC. The real operational battery data from PEA Ze-Bus (Zero-Emission bus of the Provincial Electricity Authority of Thailand) are used in this study. The proposed algorithm used to test the state of charge of the battery has been verified and illustrates the error of SOC estimation at 3.31%, less than the unadaptable model.

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Voltage stability assessment is a crucial tool for headway planning in railway system operation. The use of appropriate minimum headway is an important factor that gives the metro system running fast and efficient while the system must have strong voltage stability. In view of the seriousness of this problem, the effective measure of voltage stability assessment should be taken for analyse the effect of train headway in DC railways. This paper describes the impact of minimum headway on voltage collapse of DC mass transit systems. This assessment is based on eigen-value sensitivity analysis of two voltage level systems. The smallest eigen-value is chosen to measure the stability margin of the DC railway mass transit system. As a result, the minimum headway of 3 minutes caused the voltage collapse of the 800 V test system while the 1.5 kV test system is still strong. The simulation result of all case shows that the 1500 V test system is stronger than 800 V.