scholarly journals Electric Vehicles Plug-In Duration Forecasting Using Machine Learning for Battery Optimization

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4208
Author(s):  
Yukai Chen ◽  
Khaled Sidahmed Sidahmed Alamin ◽  
Daniele Jahier Pagliari ◽  
Sara Vinco ◽  
Enrico Macii ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electric Vehicles ◽  
Rechargeable Batteries ◽  
Just In Time ◽  
Time Interval ◽  
Factors Affecting ◽  
Charging Station ◽  
Forecasting Error ◽  
The Moment ◽  
Battery Optimization

The aging of rechargeable batteries, with its associated replacement costs, is one of the main issues limiting the diffusion of electric vehicles (EVs) as the future transportation infrastructure. An effective way to mitigate battery aging is to act on its charge cycles, more controllable than discharge ones, implementing so-called battery-aware charging protocols. Since one of the main factors affecting battery aging is its average state of charge (SOC), these protocols try to minimize the standby time, i.e., the time interval between the end of the actual charge and the moment when the EV is unplugged from the charging station. Doing so while still ensuring that the EV is fully charged when needed (in order to achieve a satisfying user experience) requires a “just-in-time” charging protocol, which completes exactly at the plug-out time. This type of protocol can only be achieved if an estimate of the expected plug-in duration is available. While many previous works have stressed the importance of having this estimate, they have either used straightforward forecasting methods, or assumed that the plug-in duration was directly indicated by the user, which could lead to sub-optimal results. In this paper, we evaluate the effectiveness of a more advanced forecasting based on machine learning (ML). With experiments on a public dataset containing data from domestic EV charge points, we show that a simple tree-based ML model, trained on each charge station based on its users’ behaviour, can reduce the forecasting error by up to 4× compared to the simple predictors used in previous works. This, in turn, leads to an improvement of up to 50% in a combined aging-quality of service metric.

Layout evaluation for electric vehicle charging pile based on charging frequency

2021 ◽  
pp. 1-13
Author(s):  
Xiangke Cui ◽  
Zhenji Zhang ◽  
Fang Liu ◽  
Jingya Liu
Keyword(s):  
Electric Vehicles ◽  
Factors Affecting ◽  
Charging Station ◽  
Passenger Demand ◽  
Basic Model ◽  
Electric Vehicle Charging ◽  
Total Vehicle ◽  
Improved Model ◽  
Charging Stations ◽  
Station Location

This study aims to solve the problem of locating charging stations for public electric vehicles. We take into consideration the factors affecting charging station locations including mileage, electric vehicles distribution, and passenger distribution. A Non-deterministic Polynomial model aiming to minimize the total vehicle service distance is developed. We use an agent-based model to simulate the optimized charging station location based on Anylogic. Through a case study of Beijing, we test the model in five situations. The results of one situation show that the existing layout of the charging stations is unreasonable when charging frequency is sharply variant (basic model); this paper optimizes the existing location by improving the constraint for the smallest number of charging stations (improved model); compared with the basic model, the improved model has a shorter response time to passenger demand, shorter service time for passengers but more mileage for electric vehicles.

Performance analysis on liquid-cooled battery thermal management for electric vehicles based on machine learning

2021 ◽  
Vol 494 ◽  
pp. 229727
Author(s):  
Xingwang Tang ◽  
Qin Guo ◽  
Ming Li ◽  
Changhua Wei ◽  
Zhiyao Pan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Performance Analysis ◽  
Thermal Management ◽  
Electric Vehicles ◽  
Battery Thermal Management

Machine learning roles in advancing the power network stability due to deployments of renewable energies and electric vehicles

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mohamad Nassereddine
Keyword(s):  
Machine Learning ◽  
Renewable Energy ◽  
Electric Vehicles ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Power Network ◽  
Network Stability ◽  
Fast Charging ◽  
Electrical Loads ◽  
Charging Stations

AbstractRenewable energy sources are widely installed across countries. In recent years, the capacity of the installed renewable network supports large percentage of the required electrical loads. The relying on renewable energy sources to support the required electrical loads could have a catastrophic impact on the network stability under sudden change in weather conditions. Also, the recent deployment of fast charging stations for electric vehicles adds additional load burden on the electrical work. The fast charging stations require large amount of power for short period. This major increase in power load with the presence of renewable energy generation, increases the risk of power failure/outage due to overload scenarios. To mitigate the issue, the paper introduces the machine learning roles to ensure network stability and reliability always maintained. The paper contains valuable information on the data collection devises within the power network, how these data can be used to ensure system stability. The paper introduces the architect for the machine learning algorithm to monitor and manage the installed renewable energy sources and fast charging stations for optimum power grid network stability. Case study is included.

scholarly journals The Architecture Design of Electrical Vehicle Infrastructure Using Viable System Model Approach

Systems ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 19
Author(s):  
Mahdi Boucetta ◽  
Niamat Ullah Ibne Hossain ◽  
Raed Jaradat ◽  
Charles Keating ◽  
Siham Tazzit ◽  
...  
Keyword(s):  
Urban Space ◽  
Electric Vehicles ◽  
Reference Model ◽  
System Model ◽  
Ecosystem Structure ◽  
Viable System Model ◽  
Charging Station ◽  
Viable System ◽  
Station Location ◽  
Ev Charging

Exponential technological-based growth in industrialization and urbanization, and the ease of mobility that modern motorization offers have significantly transformed social structures and living standards. As a result, electric vehicles (EVs) have gained widespread popularity as a mode of sustainable transport. The increasing demand for of electric vehicles (EVs) has reduced the some of the environmental issues and urban space requirements for parking and road usage. The current body of EV literature is replete with different optimization and empirical approaches pertaining to the design and analysis of the EV ecosystem; however, probing the EV ecosystem from a management perspective has not been analyzed. To address this gap, this paper develops a systems-based framework to offer rigorous design and analysis of the EV ecosystem, with a focus on charging station location problems. The study framework includes: (1) examination of the EV charging station location problem through the lens of a systems perspective; (2) a systems view of EV ecosystem structure; and (3) development of a reference model for EV charging stations by adopting the viable system model. The paper concludes with the methodological implications and utility of the reference model to offer managerial insights for practitioners and stakeholders.

scholarly journals Financing Growth through Remittances and Foreign Direct Investment: Evidences from Balkan Countries

2021 ◽  
Vol 14 (3) ◽  
pp. 117
Author(s):  
Esmeralda Jushi ◽  
Eglantina Hysa ◽  
Arjona Cela ◽  
Mirela Panait ◽  
Marian Catalin Voica
Keyword(s):  
Developing Economies ◽  
Central Banks ◽  
Sustainable Growth ◽  
Time Interval ◽  
Foreign Direct Investments ◽  
Factors Affecting ◽  
Balkan Countries ◽  
To Come ◽  
Sustainable Economic Growth ◽  
The Impact

The ultimate goal of central banks, worldwide, is to promote the foundations for sustainable economic growth. In the case of developing economies, in particular, such objective requires time, huge efforts, attention, and plenty of resources in order to be accomplished to the fullest degree. This paper thoroughly investigates key factors affecting Balkan countries’ economic development (as measured by gross domestic product (GDP) growth), focusing especially on the impact of remittances. The analysis was done over an 18-year time interval (2000–2017) and builds on 144 observations. The data figures were retrieved from the World Bank database while two dummies were created to test the impact of the last financial crisis (2008–2012). Econometric tools were employed to carry out a broad analysis on the interdependencies that exist and, in particular, to determine the role of remittance income on growth. The vector auto regressive model was estimated using EViews software, and was used to come up with relevant insights. Empirical findings suggest the following: population growth, remittances, and labor force participation are insignificant factors for sustainable growth. On the other hand, previous levels of GDP, trade, and foreign direct investments (FDIs) appear to be relevant for the predictor. This research provides up-to-date conclusions, which can be considered during the decision-making process of central banks, as well as by government policymakers.

scholarly journals Energy Management System Optimization of Drug Store Electric Vehicles Charging Station Operation

2021 ◽  
Vol 13 (11) ◽  
pp. 6163
Author(s):  
Yongyi Huang ◽  
Atsushi Yona ◽  
Hiroshi Takahashi ◽  
Ashraf Mohamed Hemeida ◽  
Paras Mandal ◽  
...  
Keyword(s):  
Power Generation ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
System Optimization ◽  
Mixed Integer ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Photovoltaic Power ◽  
Vehicle Charging ◽  
Charging Stations

Electric vehicle charging station have become an urgent need in many communities around the world, due to the increase of using electric vehicles over conventional vehicles. In addition, establishment of charging stations, and the grid impact of household photovoltaic power generation would reduce the feed-in tariff. These two factors are considered to propose setting up charging stations at convenience stores, which would enable the electric energy to be shared between locations. Charging stations could collect excess photovoltaic energy from homes and market it to electric vehicles. This article examines vehicle travel time, basic household energy demand, and the electricity consumption status of Okinawa city as a whole to model the operation of an electric vehicle charging station for a year. The entire program is optimized using MATLAB mixed integer linear programming (MILP) toolbox. The findings demonstrate that a profit could be achieved under the principle of ensuring the charging station’s stable service. Household photovoltaic power generation and electric vehicles are highly dependent on energy sharing between regions. The convenience store charging station service strategy suggested gives a solution to the future issues.

scholarly journals Separation and Efficient Recovery of Lithium from Spent Lithium-Ion Batteries

Metals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1091
Author(s):  
Eva Gerold ◽  
Stefan Luidold ◽  
Helmut Antrekowitsch
Keyword(s):  
Lithium Ion Batteries ◽  
Electric Vehicles ◽  
Sodium Phosphate ◽  
Room Temperature ◽  
State Of The Art ◽  
Potassium Phosphate ◽  
Lithium Ion ◽  
Rechargeable Batteries ◽  
Raw Material ◽  
Efficient Recovery

The consumption of lithium has increased dramatically in recent years. This can be primarily attributed to its use in lithium-ion batteries for the operation of hybrid and electric vehicles. Due to its specific properties, lithium will also continue to be an indispensable key component for rechargeable batteries in the next decades. An average lithium-ion battery contains 5–7% of lithium. These values indicate that used rechargeable batteries are a high-quality raw material for lithium recovery. Currently, the feasibility and reasonability of the hydrometallurgical recycling of lithium from spent lithium-ion batteries is still a field of research. This work is intended to compare the classic method of the precipitation of lithium from synthetic and real pregnant leaching liquors gained from spent lithium-ion batteries with sodium carbonate (state of the art) with alternative precipitation agents such as sodium phosphate and potassium phosphate. Furthermore, the correlation of the obtained product to the used type of phosphate is comprised. In addition, the influence of the process temperature (room temperature to boiling point), as well as the stoichiometric factor of the precipitant, is investigated in order to finally enable a statement about an efficient process, its parameter and the main dependencies.

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