• Energy Research

Electric vehicles are becoming dominant in the global automobile market due to their better environmental friendliness compared to internal combustion vehicles. An adequate network of public charging stations is required to fulfil the fast charging demands of EV users. Knowing the shape and amplitude of their power curves is essential for power purchase planning and grid capacity sizing. Based on a large-scale empirical and representative dataset, this paper creates standard load profiles for various power levels, station sizes, and operating environments. It is found that the average power per charge point increases with rated station power, particularly for a rated power above 100 kW, and decreases with the number of charge points per station for AC chargers. For AC chargers, it is revealed how the shape of the power curve largely depends on the environment of a station, with urban settings experiencing the highest average power of 0.71 kW on average leading to an annual energy sale of 6.2 MWh. These findings show that the rated grid capacity can be well below the sum of the rated power of each charge point.

Abstract The state of health for lithium battery is necessary to ensure the reliability and safety for battery energy storage system. Accurate prediction battery state of health plays an extremely important role in guaranteeing safety and minimizing maintenance costs. However, the complex physicochemical characteristics of battery degradation cannot be obtained directly. Here a novel Gaussian process regression model based on the partial incremental capacity curve is proposed. First, an advanced Gaussian filter method is applied to obtain the smoothing incremental capacity curves. The health indexes are then extracted from the partial incremental capacity curves as the input features of the proposed model. Additionally, the mean and the covariance function of the proposed method are applied to predict battery state of health and the model uncertainty, respectively. Four aging datasets from NASA data repository are employed for demonstrating the predictive capability and efficacy of the degradation model using the proposed method. Besides, different initial health conditions of the tested batteries are used to verify the robustness and reliability of the proposed method. Results show that the proposed method can provide accurate and robust state of health estimation.