@article {NaitMalek2022,
	title = {A Hybrid Approach for State-of-Charge Forecasting in Battery-Powered Electric Vehicles},
	journal = {Sustainability (Switzerland)},
	volume = {14},
	number = {16},
	year = {2022},
	note = {cited By 0},
	abstract = {Nowadays, electric vehicles (EV) are increasingly penetrating the transportation roads in most countries worldwide. Many efforts are oriented toward the deployment of the EVs infrastructures, including those dedicated to intelligent transportation and electro-mobility as well. For instance, many Moroccan organizations are collaborating to deploy charging stations in mostly all Moroccan cities. Furthermore, in Morocco, EVs are tax-free, and their users can charge for free their vehicles in any station. However, customers are still worried by the driving range of EVs. For instance, a new driving style is needed to increase the driving range of their EV, which is not easy in most cases. Therefore, the need for a companion system that helps in adopting a suitable driving style arise. The driving range depends mainly on the battery{\textquoteright}s capacity. Hence, knowing in advance the battery{\textquoteright}s state-of-charge (SoC) could help in computing the remaining driving range. In this paper, a battery SoC forecasting method is introduced and tested in a real case scenario on Rabat-Sal{\'e}-K{\'e}nitra urban roads using a Twizy EV. Results show that this method is able to forecast the SoC up to 180 s ahead with minimal errors and low computational overhead, making it more suitable for deployment in in-vehicle embedded systems. {\textcopyright} 2022 by the authors.},
	keywords = {electric vehicle, forecasting method, Morocco, transportation},
	doi = {10.3390/su14169993},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137735443\&doi=10.3390\%2fsu14169993\&partnerID=40\&md5=95a2dbf1fda5d9cc89e011adc1137835},
	author = {NaitMalek, Y. and Najib, M. and Lahlou, A. and Bakhouya, M. and Gaber, J. and Essaaidi, M.}
}
@article {NaitMalek2021,
	title = {Embedded Real-time Battery State-of-Charge Forecasting in Micro-Grid Systems},
	journal = {Ecological Complexity},
	volume = {45},
	year = {2021},
	note = {cited By 2},
	abstract = {Micro-grid systems (MGS) are increasingly investigated for green and energy efficient buildings in order to reduce energy consumption while maintaining occupants{\textquoteright} comfort. It includes renewable energy sources for power production, storage devices for storing power excess, and control strategies for orchestrating all components and improving the system{\textquoteright}s efficiency. In fact, MGS can be seen as complex systems composed of different heterogeneous entities that interact dynamically and in collective manner in order to balance between energy efficiency and occupants{\textquoteright} comfort. However, the uncertainty and intermittency of energy production and consumption requires the development of real-time forecasting methods and predictive control strategies. The State-of-Charge (SoC) of batteries is one of the main parameters used in MGS predictive control algorithms. It indicates how much energy is stored and how long MGS can be relying on deployed storage devices. Several methods have been developed for SoC estimation, but little work, however, has been dedicated for SoC forecasting in MGS. In this paper, we focus on advancing MGS predictive control through near real-time embedded forecasting of batteries SoC. In fact, we have deployed, on two platforms, two forecasting methods, Long Short-Term Memory (LSTM) and Auto Regressive Integrated Moving Average (ARIMA). Their accuracy and performance have been evaluated in both classical batch mode and streaming mode. Extensive experiments have been conducted for different forecasting horizons and results are presented using two main metrics, the accuracy and the computational time. Obtained results show that LSTM outperforms ARIMA for real-time forecasting, it has the better tradeoff in terms of forecasting accuracy and performance. {\textcopyright} 2020 Elsevier B.V.},
	doi = {10.1016/j.ecocom.2020.100903},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098467418\&doi=10.1016\%2fj.ecocom.2020.100903\&partnerID=40\&md5=e995cf66a3ed47b77bb78e95b8628d9d},
	author = {NaitMalek, Y. and Najib, M. and Bakhouya, M. and Essaaidi, M.}
}
@article {Malek202156,
	title = {Multivariate deep learning approach for electric vehicle speed forecasting},
	journal = {Big Data Mining and Analytics},
	volume = {4},
	number = {1},
	year = {2021},
	note = {cited By 46},
	pages = {56-64},
	abstract = {Speed forecasting has numerous applications in intelligent transport systems{\textquoteright} design and control, especially for safety and road efficiency applications. In the field of electromobility, it represents the most dynamic parameter for efficient online in-vehicle energy management. However, vehicles{\textquoteright} speed forecasting is a challenging task, because its estimation is closely related to various features, which can be classified into two categories, endogenous and exogenous features. Endogenous features represent electric vehicles{\textquoteright} characteristics, whereas exogenous ones represent its surrounding context, such as traffic, weather, and road conditions. In this paper, a speed forecasting method based on the Long Short-Term Memory (LSTM) is introduced. The LSTM model training is performed upon a dataset collected from a traffic simulator based on real-world data representing urban itineraries. The proposed models are generated for univariate and multivariate scenarios and are assessed in terms of accuracy for speed forecasting. Simulation results show that the multivariate model outperforms the univariate model for short- and long-term forecasting. {\textcopyright} 2018 Tsinghua University Press.},
	keywords = {Deep learning, Design and control, Dynamic parameters, Electric vehicles, energy efficiency, Forecasting, Forecasting methods, Intelligent systems, Intelligent transport systems, Learning approach, Long short-term memory, Long-term forecasting, Multivariate modeling, Roads and streets, Speed, Traffic control, Traffic simulators, Vehicle actuated signals},
	doi = {10.26599/BDMA.2020.9020027},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099566517\&doi=10.26599\%2fBDMA.2020.9020027\&partnerID=40\&md5=2faaf6f427bd230fa39a873be4e45566},
	author = {Malek, Y.N. and Najib, M. and Bakhouya, M. and Essaaidi, M.}
}
