scholarly journals Ethereum and IOTA based Battery Management System with Internet of Vehicles

2021 ◽  
Vol 3 (3) ◽  
pp. 229-240
Author(s):  
R. Kanthavel
Keyword(s):  
Electric Vehicles ◽  
The Internet ◽  
Battery Life ◽  
Battery Management System ◽  
Battery Management ◽  
Internet Of Vehicles ◽  
Viable Solution ◽  
Data Layer ◽  
Charging Stations ◽  
The Internet Of Things

The era of Electric Vehicles (EVs) has influenced the very make and manufacture of vehicles resulting in low pollution and advanced battery life. On the other hand, the internet of things has also expanded allowing a number of devices to stay connected using the internet. Massive drawbacks faced by EVs today are the limitation in battery swapping and charging stations and limitation in the range of batteries used. This proposed paper aims to efficiently manage the best battery system apart from building the essential infrastructure. In some cases battery swapping option is also provided through other EV drivers or at registered stations. Hence a complete database of the EV network is required so that it is possible to swap and charge batteries successfully. An EV management using two blockchains as a data layer and network of the application is implemented in this work. The first step involves the development of a blockchain framework using Ethereum and the next step entails a direct acyclic graph. When integrated, these two methodologies prove to be an efficient platform that offers a viable solution for battery management in Electric Vehicles.

scholarly journals Blockchain IoT for Smart Electric Vehicles Battery Management

2020 ◽  
Vol 12 (10) ◽  
pp. 3984 ◽  
Author(s):  
Bogdan Cristian Florea ◽  
Dragos Daniel Taralunga
Keyword(s):  
Electric Vehicles ◽  
Directed Acyclic Graph ◽  
Limited Range ◽  
Data Driven ◽  
Battery Life ◽  
Battery Management System ◽  
Battery Management ◽  
Viable Solution ◽  
Data Layer ◽  
The Internet Of Things

Electric Vehicles (EVs) have generated a lot of interest in recent years, due to the advances in battery life and low pollution. Similarly, the expansion of the Internet of Things (IoT) allowed more and more devices to be interconnected. One major problem EVs face today is the limited range of the battery and the limited number of charging or battery swapping stations. A solution is to not only build the necessary infrastructure, but also to be able to correctly estimate the remaining power using an efficient battery management system (BMS). For some EVs, battery swapping can also be an option, either at registered stations, or even directly from other EV drivers. Thus, a network of EV information is required, so that a successful battery charge or swap can be made available for drivers. In this paper two blockchain implementations for an EV BMS are presented, using blockchain as the network and data layer of the application. The first implementation uses Ethereum as the blockchain framework for developing smart contracts, while the second uses a directed acyclic graph (DAG), on top of the IOTA tangle. The two approaches are implemented and compared, demonstrating that both platforms can provide a viable solution for an efficient, semi-decentralized, data-driven BMS.

A Series Regeneration Converter Technique for Voltage Balancing of Energy Storage Devices

2017 ◽  
Vol 8 (2) ◽  
pp. 475 ◽  
Author(s):  
A K M Ahasan Habib ◽  
S. M. A. Motakabber ◽  
Muhammad I. Ibrahimy
Keyword(s):  
Electric Vehicles ◽  
Battery Life ◽  
Battery Management System ◽  
Battery Management ◽  
Resonant Converter ◽  
Energy Storage Devices ◽  
Voltage Level ◽  
Voltage Balancing ◽  
Series Resonant ◽  
The Individual

<p><em>A single series resonant converter has been designed to balance the voltage level of a storage battery for electric vehicles. The proposed design has been simulated and verified by using two 100F supercapacitors instate of the conventional rechargeable battery. A voltage monitoring circuit detects the voltage condition of the individual capacitor and sends the voltage status to the control circuit for action. A technique has been developed to control a set of switches to transfer the current between the capacitor to balance the voltage level. The MATLAB simulated result shows the balancing circuit decreases the voltage difference between the two supercapacitors from 200 mV to 0V in 140 seconds, which is less than the existing methods. This fast voltage balancing technique can be used in the battery management system or electric vehicles for long lasting the battery life.</em></p><p><em> </em></p><strong><em>Keywords</em></strong><em>: Voltage balancing; electric vehicles; supercapacitor; battery; series resonant converter</em>

scholarly journals Lithium-Ion Battery Management System: A Lifecycle Evaluation Model for the Use in the Development of Electric Vehicles

2018 ◽  
Vol 144 ◽  
pp. 04020 ◽  
Author(s):  
Ayush Sisodia ◽  
Jonathan Monteiro
Keyword(s):  
Life Cycle ◽  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Evaluation Model ◽  
Lithium Ion ◽  
Battery Life ◽  
Battery Management System ◽  
Battery Management ◽  
System A

The use of Lithium-ion batteries in the automobile sector has expanded drastically in the recent years. The foreseen increment of lithium to power electric and hybrid electric vehicles has provoked specialists to analyze the long term credibility of lithium as a transportation asset. To give a better picture of future accessibility, this paper exhibits a life cycle model for the key procedures and materials associated with the electric vehicle lithium-ion battery life cycle, on a worldwide scale. This model tracks the flow of lithium and energy sources from extraction, to generation, to on road utilization, and the role of reusing and scrapping. This life cycle evaluation model is the initial phase in building up an examination model for the lithium ion battery production that would enable the policymakers to survey the future importance of lithium battery recycling, and when in time setting up a reusing foundation be made necessary.

Maximizing Battery Life and Usable Capacity With Battery Management System in Electric Vehicles

2017 ◽  
Author(s):  
Zoltán Szeli ◽  
Gábor Szakállas ◽  
Ferenc Szauter
Keyword(s):  
Electric Vehicles ◽  
Management System ◽  
Active Cell ◽  
Battery Pack ◽  
Research Centre ◽  
Battery Life ◽  
Battery Management System ◽  
Battery Management ◽  
Cost Weight ◽  
Cell Balancing

In terms of the electric vehicles is an important issue of sizing a battery pack. The designer must take account of parameters such as cost, weight and durability. We can optimize these parameters with the help of a battery management system with integrated active cell balancing function. The article describes the development of a battery management system that developed by the Research Centre of Vehicle Industry at Széchenyi István University, Győr, Hungary.

scholarly journals Evaluation and Comparative Study of Cell Balancing Methods for Lithium-Ion Batteries Used in Electric Vehicles

2021 ◽  
Vol 10 (3) ◽  
pp. 471-479
Author(s):  
Thiruvonasundari Duraisamy ◽  
Kaliyaperumal Deepa
Keyword(s):  
Electric Vehicles ◽  
Lithium Ion ◽  
Least Square ◽  
Battery Life ◽  
Model Parameters ◽  
Battery Management System ◽  
Battery Management ◽  
Renewable Energy Resources ◽  
Voltage Deviation ◽  
Cell Balancing

Vehicle manufacturers positioned electric vehicles (EVs) and hybrid electric vehicles (HEVs) as reliable, safe and environmental friendly alternative to traditional fuel based vehicles. Charging EVs using renewable energy resources reduce greenhouse emissions. The Lithium-ion (Li-ion) batteries used in EVs are susceptible to failure due to voltage imbalance when connected to form a pack. Hence, it requires a proper balancing system categorised into passive and active systems based on the working principle. It is the prerogative of a battery management system (BMS) designer to choose an appropriate system depending on the application. This study compares and evaluates passive balancing system against widely used inductor based active balancing system in order to select an appropriate balancing scheme addressing battery efficiency and balancing speed for E-vehicle segment (E-bike, E-car and E-truck). The balancing systems are implemented using “top-balancing” algorithm which balance the cells voltages near the end of charge for better accuracy and effective balancing. The most important characteristics of the balancing systems such as degree of imbalance, power loss and temperature variation are determined by their influence on battery performance and cost. To enhance the battery life, Matlab-Simscape simulation-based analysis is performed in order to fine tune the cell balancing system for the optimal usage of the battery pack. For the simulation requirements, the battery model parameters are obtained using least-square fitting algorithm on the data obtained through electro chemical impedance spectroscopy (EIS) test. The achieved balancing time of the passive and active cell balancer for fourteen cells were 48 and 20 min for the voltage deviation of 30 mV. Also, the recorded balancing time was 215 and 42 min for the voltage deviation of 200 mV.

scholarly journals Evaluation and Comparative Study of Cell Balancing Methods for Lithium-Ion Batteries Used in Electric Vehicles

2021 ◽  
Vol 10 (3) ◽  
pp. 471-479
Author(s):  
Thiruvonasundari Duraisamy ◽  
Kaliyaperumal Deepa
Keyword(s):  
Electric Vehicles ◽  
Lithium Ion ◽  
Least Square ◽  
Battery Life ◽  
Model Parameters ◽  
Battery Management System ◽  
Battery Management ◽  
Renewable Energy Resources ◽  
Voltage Deviation ◽  
Cell Balancing

Vehicle manufacturers positioned electric vehicles (EVs) and hybrid electric vehicles (HEVs) as reliable, safe and environmental friendly alternative to traditional fuel based vehicles. Charging EVs using renewable energy resources reduce greenhouse emissions. The Lithium-ion (Li-ion) batteries used in EVs are susceptible to failure due to voltage imbalance when connected to form a pack. Hence, it requires a proper balancing system categorised into passive and active systems based on the working principle. It is the prerogative of a battery management system (BMS) designer to choose an appropriate system depending on the application. This study compares and evaluates passive balancing system against widely used inductor based active balancing system in order to select an appropriate balancing scheme addressing battery efficiency and balancing speed for E-vehicle segment (E-bike, E-car and E-truck). The balancing systems are implemented using “top-balancing” algorithm which balance the cells voltages near the end of charge for better accuracy and effective balancing. The most important characteristics of the balancing systems such as degree of imbalance, power loss and temperature variation are determined by their influence on battery performance and cost. To enhance the battery life, Matlab-Simscape simulation-based analysis is performed in order to fine tune the cell balancing system for the optimal usage of the battery pack. For the simulation requirements, the battery model parameters are obtained using least-square fitting algorithm on the data obtained through electro chemical impedance spectroscopy (EIS) test. The achieved balancing time of the passive and active cell balancer for fourteen cells were 48 and 20 min for the voltage deviation of 30 mV. Also, the recorded balancing time was 215 and 42 min for the voltage deviation of 200 mV.

scholarly journals Estimation of Lithium-Ion Batteries State-Condition in Electric Vehicle Applications: Issues and State of the Art

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1588
Author(s):  
Khaled Laadjal ◽  
Antonio J. Marques Cardoso
Keyword(s):  
Lithium Ion Batteries ◽  
Electric Vehicles ◽  
Lithium Ion ◽  
Internal Resistance ◽  
State Condition ◽  
Battery Life ◽  
Battery Management System ◽  
Battery Management ◽  
Energy Management Strategy ◽  
Factors Affecting

Lithium-ion batteries are the most used these days for charging electric vehicles (EV). It is important to study the aging of batteries because the deterioration of their characteristics largely determines the cost, efficiency, and environmental impact of electric vehicles, especially full-electric ones. The estimation of batteries’ state-condition is also very important for improving energy efficiency, lengthening the life cycle, minimizing costs and ensuring safe implementation of batteries in electric vehicles. However, batteries with large temporal variables and non-linear characteristics are often affected by random factors affecting the equivalent internal resistance (EIR), battery state of charge (SoC), and state of health (SoH) in EV applications. The estimation of batteries’ parameters is a complex process, due to its dependence on various factors such as batteries age and ambient temperature, among others. A good estimate of SoC and internal resistance leads to long battery life and disaster prevention in the event of a battery failure. The classification of estimation methodologies for internal parameters and the charging status of batteries will be very helpful in choosing the appropriate method for the development of a reliable and secure battery management system (BMS) and an energy management strategy for electric vehicles.

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