Nils Masuch

Wind power-aware vehicle-to-grid algorithms for sustainable EV energy management systems

Renewable energy carriers such as wind or solar radiation turn out to be serious alternatives to fossil and nuclear energy production. However, due to its fluctuating characteristics its application within power grids leads to new challenges for system operators. That includes the intermediate storage of the energy which necessitates the installation of new systems or approaches. One of them is the usage of electric vehicle batteries which can be aggregated to virtual power plants. In this paper we propose an energy management algorithm which schedules the optimal charging and discharging times of an electric vehicle battery according to the expected fraction of regenerative energy within the power grid. At the same time the constraints of other stakeholders (driver, charging station infrastructure provider) are taken into account, enabling the algorithm to support the user in his charging decisions upon his daily mobility requirements. In the course of the paper we provide a detailed description of the algorithm, simulation results based on this approach and discuss its application in a field test we have performed, recently.

A distributed multi-operator W2V2G management approach

The most critical factors for the success of electric mobility are costs, the environmental friendliness and mobility. Within the sub-project “Vehicle 2 Grid” of the joint project “MINI E powered by Vattenfall”, we developed a decentralised Wind-to-Vehicle-to-Grid systems (W2V2G) to improve the CO2 balance of the car and the mobility of the driver, as well as an AAA infrastructure for the non-discriminatory access to the charging infrastructure by incorporating standardised technologies from the Internet and mobile telecommunication networks.

A Multi-agent Approach to Professional Software Engineering

The community of agent researchers and engineers has produced a number of interesting and mature results. However, agent technology is still not widely adopted by industrial software developers or software companies--possibly because existing frameworks are infused with academic premises that rarely apply to industrial settings. In this paper, we analyse the requirements of current industry-driven software projects and show how we are able to cope with these requirements in the Java Intelligent Agent Componentware agent framework, JIACi¾źV. We argue that the lack of industry-grade requirements and features in other agent frameworks is one of the reasons for the slow acceptance of agent technology in the software industry. The JIACi¾źV framework tries to bridge that gap--not as a final solution, but as a stepping stone towards industrial acceptance.

SeMa 2 :A Hybrid Semantic Service Matching Approach

The SeMa2 software module is a hybrid semantic service matchmaker based on OWL-S service descriptions and SWRL rules that participated in the S3 Contests 2008–2010. It provides syntactical as well as semantical matching techniques. Besides classifying input and output parameters it offers an approach to precondition and effect rule matching and reasoning. In this chapter we describe the architecture and the workflow of our approach and the results that have been evaluated during the last S3 Contest and with experimental test runs. Finally, we discuss the benefits and drawbacks of our implementation and propose future steps on our work.

An Open Extensible Platform for Intermodal Mobility Assistance

Abstract Mobility can be considered as an important factor when it comes to assessing ones’ quality of life. Today, there are many different means of transportation available, each one with its particular advantages and drawbacks. Recently, a whole set of new transportation concepts made some noise in the world. As an example, consider electric vehicles or Car- and Ride Sharing providers. We do not wish to favour the one or the other concept, in fact, each mean of transportation has its own distinctive charm, depending on the situation. We see the future in an intelligent combination of many different means of transportation, where the selection is aligned with the traffic participants’ preferences and requirement, as well as with external factors. Nevertheless, any attempt implement an automated assistance is aggravated by the distributed nature of the data which is required for such calculation. In this paper we tackle this problem and propose an open, extensible platform on which (mobility) providers can offer their service in a standarised fashion. Based on this platform we present our mobility assistance system which supports users with urban trip-planning and outnumbers comparable approaches inasmuch as intermodal options are considered on yet another level.

Towards a Holistic Approach for Problems in the Energy and Mobility Domain

Abstract With the current rise of electric vehicles, it is possible to use those vehicles for storing surplus energy from renewable energy sources; however, this can be in conflict with providing and ensuring the mobility of the vehicles user. At DAI-Labor, we have a large number of both, past and upcoming projects concerned with those two aspects of managing electric vehicles: energy and mobility. To unify and facilitate developments in those projects, we developed common domain models describing the different aspects of the e-mobility domain. Those domain models are used in many of our projects for optimising charging schedules and for ensuring the users mobility.

A User-Centric Approach for Efficient Daily Mobility Planning in E-Vehicle Infrastructure Networks

The next generation of e-vehicles will be assessed on their ability to master the challenges posed by urbanization, resource restrictions and increasingly flexible and diverse user demands. Presumably, the vehicle-user-infrastructure network will become too dynamic and complex for an individual user to fathom without some sort of automated assistance. This paper proposes a user-centric, constraint-based, in-vehicle travel planning system, which schedules a daily travel plan of a user by exploiting knowledge about current and future states of the vehicle-user-infrastructure network. The system is implemented in an agent-based framework and is evaluated by a traffic simulator that additionally incorporates parking and charging lots together with an availability monitoring and booking service. The benefit of the planning system is assessed in a traffic simulation, where vehicles with and without a planning system compete for available resources. It can be shown that proactive conflict management and resource scheduling can reduce parking search time and overall travel time. Furthermore it can be shown that the adherence to schedule can be significantly improved.

A common approach to intelligent energy and mobility services in a smart city environment

Due to the fact that electric vehicles have not broadly entered the vehicle market there are many attempts to convince producers to integrate technologies that utilise embedded batteries for purposes different from driving. The vehicle-to-grid technology, for instance, literally turns electric vehicles into a mobile battery, enabling new areas of applications (e.g., to provide regulatory energy, to do grid-load balancing, or to buffer surpluses of energy) and business perspectives. Utilising a vehicle’s battery, however is not without a price—in this case: the driver’s mobility. Given this dependency, it is interesting that most available works consider the application of electric vehicles for energy and grid-related problems in isolation, that is, detached from mobility-related issues. The distributed artificial intelligence laboratory, or DAI-Lab, is a third-party funded research lab at Technische Universität Berlin and integrates the chair for agent technologies in business applications and telecommunication. The DAI-Lab has engaged in a large number of both, past and upcoming projects concerned with two aspects of managing electric vehicles, namely: energy and mobility. This article aims to summarise experiences that were collected during the last years and to present developed solutions which consider energy and mobility-related problems jointly.

Agent based assistance for electric vehicles an evaluation

Even before car manufacturers start offering series-produced electric vehicles in a large scale, expectations in the electric powertrain are considerably high. Prospective business perspectives are additionally driven by the so called Vehicle-to-Grid technology, which allows electric vehicles to not only procure electric energy, but also to feed energy back into the grid network. However, by using Vehicle-to-Grid, energy literally degenerates into an article of merchandise and becomes of interest to several stakeholders. We have developed a multi-agent system, which embraces this exact view and maximises the interest of several stakeholders in using Vehicle-to-Grid capable electric vehicles. The purpose of this paper is to describe the evaluation of our assistance system and to present collected evaluation results.

Reconsider your strategy an agent-based conceptualisation of compensatory driver behaviour

Currently, there are many models available which can be used to describe a drivers behaviour for a traffic simulation. Despite the number of available formalisms it is our opinion that existing approaches neglect the interplay between the simulation topology and strategic decisions of simulated drivers. Existing models either disregard strategy updates or focus on short-term strategies only. In this paper we tackle this problem and propose a model which incorporates (long-term) strategic reactions of simulated drivers to influences of the surrounding infrastructure. In doing so, we formalise a certain type of human behaviour which is commonly known as strategic-level compensation.