An Electric Vehicle Simulator for Realistic Battery Signals Generation from Data-sheet and Real-world Data

Electric vehicles (EVs) have been globally recognized as a reliable alternative to fossil fuel vehicles. The core component of an electric vehicle is its rechargeable battery pack. However, there still needs to be large-scale publicly available EV data to investigate and distribute effective solutions to monitor the conditions of the EV’s battery pack. Hence, we propose an EV simulator that generates EV battery pack internal signals starting from the input driving cycle. The simulated data resemble the behavior of a multi-cell EV battery pack undergoing the user’s utilization of the EV. The simulated data include vehicle speed, voltage, current, State of Charge (SOC), and internal temperature of the battery pack. The virtual-EV model simulator, including the battery pack subsystem, has been tuned using real-world EV data-sheet information. The battery pack embeds thermal and aging models for further realism, influencing the output signals given the environmental temperature and the battery’s State of Health (SOH). The data generated by the virtual EV simulator have been validated with real EV data signals sampled by an equivalent real-world EV. The data comparison yields a minimum R2 value of 0.94 and a Root Mean Squared Error not higher than 2.74V for the battery pack’s voltage and SOC, respectively

Impact of bidirectional EV charging stations on a distribution network: a Power Hardware-In-the-Loop implementation

The need for decarbonizing the entire energy system calls for new operational approaches in different sectors, currently (almost) fully dominated by fossil fuels, such as the transports. In particular, the decarbonization of the light-duty passenger transport, based on the implementation of Battery Electric Vehicles, may have a twofold benefit, because of (i) the reduction of local and global direct emissions, and (ii) the role that the Battery Electric Vehicles can have in supporting the operation of the power system in case of large share of non-dispatchable renewable energy sources. This paper aims to investigate, through a Power Hardware-In-the-Loop laboratory setup, the impacts of the Vehicle-to-Grid and Grid-to-Vehicle paradigms on a Low Voltage grid portion serving as grid infrastructure an energy community. The results show that the Low Voltage grid losses, if not taken into account, can cause a wrong evaluation of the expected impact on the grid of the Battery Electric Vehicles. Furthermore, the harmonics of current injected into the grid by several chargers could compromise the perceived power quality. Both the analyzed aspects must be hence carefully considered for properly evaluating pros and cons that the installation of several chargers may have on the grid side. The main contributions refer to the calculation of losses and to the evaluation of the power quality aspects through a Power Hardware-In-the-Loop configuration, enabling to take into account the harmonics interaction between charging stations and power grid

Modelling battery packs of real-world electric vehicles from data sheet information

Lithium-ion batteries have emerged as the leading enabling technology in developing Electric Vehicles (EVs), But, large-scale publicly available EV data are extremely difficult to find. So it becomes difficult to research and disseminate new methods for monitoring the battery pack of an EV. In this work, we propose a Simulink-based approach to define a virtual-EV model that simulates EV battery pack signals starting from input driving sessions. The battery pack module within the virtual-EV has been fine-tuned using data gathered from real-world EV data sheets. Moreover, the battery pack module includes thermal and aging models, impacting on the output signals, considering the temperature of the surrounding environment and the initial State of Health (SOH) of the battery pack. The virtual-EV generates time series of vehicle's speed, and battery pack's current, State of Charge (SOC), voltage, and average internal temperature according to the input driving cycle. We defined two Simulink EV models emulating two distinct real-world-EVs. Then, we assessed the performances of the simulators comparing the simulated data and real EV data signals collected by the same real-world-EV models, and we obtain, for both simulated EV models, R2 values higher than 0.70 and an RMSE of at most 7V and 8% for the voltage and SOC of the battery pack, respectively