Evaluation of OCPP and IEC 61850 for smart charging electric vehicles

Schmutzler, Jens; Andersen, Claus A.; Wietfeld, Christian

doi:10.1109/evs.2013.6914751

Cited by 30 publications

(14 citation statements)

References 5 publications

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“…7, the most relevant standards and protocols for the e-mobility sector, in specific for battery-electric mobility, are categorized. The protocols are extracted from ElaadNL (2017); Rodríguez et al (2015); Schmutzler et al (2013). This allocation helps in identifying gaps in standardization.…”

Section: Emsa Information Layermentioning

confidence: 99%

E-Mobility Systems Architecture: a model-based framework for managing complexity and interoperability

et al. 2019

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The future of e-mobility will consist of a large number of connected electric vehicles, smart charging stations and information systems at the intersection of electricity and mobility sector. When engineering and integrating the multitude of systems into even more complex systems-of-systems for e-mobility, interoperability and complexity handling are vital. Model-based system architectures support the engineering process of information systems with the concepts of abstraction, reduction and separation of concerns. In this paper, we contribute to the research body, by extracting requirements for managing complexity and interoperability of these systems. Further, a comparative analysis of the state-of-the-art in existing architecture models and frameworks for e-mobility is conducted. Based on the identified gaps in existing research, we propose the E-Mobility Systems Architecture (EMSA) Model, a three-dimensional systems architecture model for the e-mobility sector. Its structure originates from the well-established Smart Grid Architecture Model. We further allocate all relevant entities from the e-mobility sector to the EMSA dimensions, including a harmonized role model, functional reference architecture, component and systems allocation, as well as a mapping of data standards and communication protocols. The model then is validated qualitatively and quantitatively against the requirements with a case study approach. Our evaluation shows that the EMSA Model fulfills all requirements regarding the management of complexity and ensuring interoperability. From the case study, we further identify gaps in current data model standardization for e-mobility.

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Section: Emsa Information Layermentioning

confidence: 99%

E-Mobility Systems Architecture: a model-based framework for managing complexity and interoperability

et al. 2019

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“…An example of the IT architecture for the implementation of the mechanism is shown below in Figure No. 17 [28], [29].…”

Section: Application Of the Solutionmentioning

confidence: 99%

The V2G Process With the Predictive Model

Mroczek

Kolodynska

2020

IEEE Access

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The paper proposes using a predictive model to optimize the use of electricity in the V2G (vehicle to grid) service. The novelty of the mechanism as a kind of model predictive control (MPC) is that it seeks an effective way of managing electric energy in an Electric Vehicle (EV). Additionally, it proposes a new method of predicting the electricity consumption which allows the battery of an electric vehicle to reconcile two sides: both the system's and the user's demand will be met at the same time. The model allows for very precise determination of the vehicle's demand for the energy related to the progressive movement, taking into account the parameters characteristic of a given vehicle model, its suspension structure and aerodynamics. In addition, the machine learning algorithm was proposed for the prediction model as a hybrid (offline and online) of supervised learning. As the first part of the research, by using Matlab/Simulink/dSpace software, a prediction of EV energy consumption was created on a selected route at different times of the day (offline data matrix). At the same time, the simulated route was travelled by a BMW i3 EV (online data matrix). Based on the developed machine learning algorithm the results of the electric energy consumption were compared. The research confirms that if the correct mechanism for prediction of energy consumption by the EV is used, it is possible to define the amount of energy needed for a V2G service. The measurement error was obtained at 0.5%. The added value is setting up the EV energy security of customers after the V2G service and a correct WIN-WIN relation between the Low Voltage grid and EV customers' needs.

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“…As a step towards promoting environmentally clean transport, the Indian Government has propounded the Bharat EV Charger specifications for standardisation of protocols, charging infrastructure, and regulation of EVCFs in charging networks [38]. This draft standard, prepared by Automotive Research Association of India, includes the specifications for AC and DC charging, charger classifications, charging power levels, communication requirements between PEV and EVSE via Controller Area Network (CAN) protocol, between PEV-Central Server via Open Charge Point Protocol (OCPP) [39] and between PEV-Central Management System via Internet, billing methods, connector specifications, cable specifications, and safety requirements related to PEV charging. The charging power levels are exhibited in Table 2.…”

Section: Indian Standardsmentioning

confidence: 99%

“…The sensor data was then published to a web server via MQTT protocol. The defined protocol for this communication in Bharat EV charger standards is OCPP [39]. The charging parameters were monitored from the station, and by a PEV owner, on his smart phone.…”

Section: Implementation Of Evcf With Multimode Chargingmentioning

confidence: 99%

Power factor corrected level‐1 DC public green‐charging infrastructure to promote e‐mobility in India

Raveendran

Nair

2020

IET Power Electronics

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With the growing popularity of electrified transportation across the world, there have been extensive reports of research involving plug-in electric vehicles (PEVs), electric vehicle charging facilities (EVCFs) and their integration to the main electric grid, besides sustainable energy resources for powering these vehicles. This study proffers a power factor corrected level-1 DC public green-charging infrastructure accompanied by an integrated control, for electric light motor vehicles, compliant with recently formulated Indian charging standards. Power factor correction of plug-in electric vehicle charging current, solar photo-voltaic grid integration and charging of energy storage system is achieved using control of bidirectional AC/DC converters. A firmware and user interface for multimode off-board DC charging, with green, fast and semi-fast charging modes are investigated. Intelligent control for fast charging is also presented to reduce its pernicious impact on the grid. The simulation and hardware results verify the prime performance objectives of improving the local power quality and renewable energy utilisation with multimode charging, in the Indian context. The effectiveness of the proposed infrastructure is further demonstrated by a simulated use case, where 30 PEVs are randomly charged, in various modes, at this EVCF during busy commute hours. 2 State-of-the-art This section presents the issues, latest research, and standards, both global and in the Indian context in the area of PEV charging infrastructure and its grid integration. 2.1 Power quality issues due to charging equipment In linear circuits, as voltage and currents are sinusoidal, the power quality issue arises from the displacement power factor. In nonlinear circuits, with non-sinusoidal currents, power quality issues arise from both displacement and distortion power factor [11]. Loads that draw non-sinusoidal current from the sinusoidal voltage source cause harmonics. Battery chargers, typically, are equipped with rectifiers at the input stage that cause current harmonics in the distribution system [12, 13]. Generally, lower priced models of chargers do not handle this requirement effectively. Saber et al. [14] propose a PFC scheme based on unidirectional, current-source active rectifier for charging PEVs. An on-board PFC scheme was also discussed in [15], in which the converters in the PEV are controlled to refine the power quality. However, this increases the complexity and weight of the on-board charger.

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Evaluation of OCPP and IEC 61850 for smart charging electric vehicles

Cited by 30 publications

References 5 publications

E-Mobility Systems Architecture: a model-based framework for managing complexity and interoperability

E-Mobility Systems Architecture: a model-based framework for managing complexity and interoperability

The V2G Process With the Predictive Model

Power factor corrected level‐1 DC public green‐charging infrastructure to promote e‐mobility in India

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