Embedded Real-Time Speed Forecasting for Electric Vehicles: A Case Study on RSK Urban Roads

Abstract

During the past ten years, worldwide efforts have been pursuing an ambitious policy of sustainable development, particularly in the energy sector. This ambition was revealed by noticeable progress in the deployment and development of infrastructures for the production of renewable electrical energy. These infrastructures combined with the deployment of wired and wireless communications could support research actions in the field of connected electro-mobility. Also, this progress was manifested by the development of electric vehicles (EV), penetrating our transportation roads more and more. They are considered among the potential solutions, which are envisaged to further reduce road transport's greenhouse gas emissions, relying on low-carbon energy production. However, the uncertainty caused by both external road disturbances and drivers' behavior could influence the prediction of upcoming power demands. These latter are mainly affected by the unpredictability of the electric vehicles' speed on transportation roads. In this work, we introduce an energy management platform, which interfaces with in-vehicle components, using a developed embedded system, and external services, using IoT and big data technologies, for efficient battery power use. The platform was deployed in real-setting scenarios and tested for EV speed prediction. In fact, we have used driving data, which have been collected on Rabat-Salé-Kénitra (RSK) urban roads by our Twizy EV. A multivariate Long Short Term Memory (LSTM) algorithm was developed and deployed for speed forecasting. The effectiveness of LSTM was evaluated against well-known algorithms: Auto Regressive Integrated Moving Average (ARIMA), Convolutional Neural Network (CNN) and Convolutional LSTM (ConvLSTM). Experiments have been conducted using two approaches; the whole trajectory dataset and segmented trajectory datasets to train the models. The experimentation results show that LSTM outperforms the other used algorithms in terms of forecasting the speed, especially when using the trajectory segmentation approach.INDEX TERMS Electro-mobility, Intelligent Transportation Systems, Speed forecasting, Time-series forecasting, Deep learning I. INTRODUCTIONN OWADAYS, electric vehicles (EVs) are penetrating more and more transportation roads. There are three main categories of EVs: Hybrid Electric Vehicles (HEV), Plugin Hybrid Electric Vehicles (PHEV), and Batterypowered Electric Vehicles (BEV). BEVs are fully-electric vehicles with rechargeable batteries. HEV and PHEV are, however, powered by both gasoline and electricity. Unlike PHEV, which can be recharged through both regenerative braking and external electricity sources, HEV is charged from the electricity generated by the car's braking system. However, both of them are still using gasoline, which is one of the sources of greenhouse gas.BEVs have been getting more attention in the last years, as they are environment-friendly while having an acceptable driving range, particularly in urban areas. BEV driving range

A Hybrid Approach for State-of-Charge Forecasting in Battery-Powered Electric Vehicles

“…However, the proposed method relies on the forecasted speed, which is already deployed into the embedded system, to generate the future SoC. The deployed speed forecasting system is already tested in the previous work [43], and the simplicity and the computational power of the EV model helps in integrating it alongside the speed forecasting system. As a future work, the proposed hybrid approach is undergoing integration into the simulator, V2X Simulation Runtime Infrastructure (VSIMRTI), for virtual testing and scalability studies.…”

“…The proposed method combines a model-based approach with machine learning algorithms. Therefore, the proposed method follows two steps: (i) a machine learning algorithm is used to forecast the vehicle speed [43], and (ii) the forecasted speed is fed to a model, which outputs the forecasted SoC. The proposed method is tested using real dataset and compared against other methods from literature.…”

“…The data-based method is used to forecast the vehicle's speed, while the model-based method uses the forecasted speed to output the corresponding battery SoC. Regarding the first component of the proposed approach, we leveraged on the work presented in [43]. In the previous work, an embedded system was developed and used a data-driven method for speed forecasting.…”