GRINDING A DIAMOND – THE ITERATIVE DEVELOPMENT OF CITIZEN-INITIATED SERVICES

Many city administrations follow the smart city concept to grasp the potential of citizen participation. However, most participation concepts are not developed thoroughly, this leading to unexploited potential. Citizens are experts of their everyday life and are best aware of their personal needs. However, current forms of citizen participation stop at the idea phase of service engineering. Following design science research, we iteratively build and evaluate a so-called “digitalization street” which aims to systematically guide the citizens through the refinement and further development of their services. This digitalization street is implemented in a mid-size European city and integrates five modules which let citizens (1) describe their project proposal, (2) concretise according strengths, weaknesses, opportunities and threats, (3) identify the gain creators and pain relievers, (4) create their solution, (5) present their solution. Based on literature and a requirement elicitation workshop, a first instantiation of the artefact was developed. We contribute to the existing body of knowledge by presenting a framework for creating services based on a citizen-centric approach. We exhibit how the digitalization street can be implemented into existing processes in the city administration and help to increase the citizen participation from a project to an evaluated prototype

CITIZEN PARTICIPATION IN INCREASINGLY DIGITALIZED GOVERNMENTAL ENVIRONMENTS – A SYSTEMATIC LITERATURE REVIEW

Citizen participation in increasingly digitalized governmental environments can introduce fruitful capabilities to encourage citizens to engage in municipal affairs and through this take actively part in fostering smart cities’ effectiveness. However, the practical exploitation of recent knowledge is still not sufficiently operationalized, whilst research in this field yields various approaches focusing on diverse emphases. Therefore, the necessity of systematically collecting and afterwards analysing the existing literature towards this topic is obvious. This paper depicts a proceeding to systematically review the available literature towards the relevant research units on citizen participation. Overall, 48 topic-based papers were identified out of leading journals and conference papers about information systems. The main findings of the relevant papers were assessed to a proposed analytical framework consisting of increasing participation stages and two distinct focus groups namely government and citizens. Accordingly, the covered recent focus areas of research are identified to reveal where state-of-the-art research falls short. Consequently, the imperative of emphasising investigation regarding concepts for ICT-enabled services focusing the empowerment of citizens arises as being our contribution for guiding future research, whilst governments can practically benefit from the composed framework by using it for classifying, planning and implementing proposed participation activities

Predictive battery thermal management using quantile convolutional neural networks

An improvement in energy efficiency of Battery Thermal Management Systems (BTMS) can increase range and reduce well-to-wheel emissions of Battery Electric Vehicles (BEV). In this work, the potential of a predictive BTMS using Quantile Convolutional Neural Networks (QCNN) was examined. The QCNN provided quantile predictions of battery temperature based on input data from both previous and following drive segments. The predictive control was designed to choose battery cooling thresholds based on a weighted sum of battery cooling, ageing and derating costs derived by the quantile predictions. The predictive BTMS was analyzed concerning its adaptability to different routes ahead, tunability of cost weights as well as robustness to uncertainty of inputs. A setup with unchanged ageing costs reduced average cooling costs by 9% compared to a fixed threshold strategy in a set of 18 scenarios. Simplifications and limitations were discussed to provide a base for further improvements, for example concerning the limited freedom of cooling threshold choice. In conclusion, the developed framework was able to use QCNN predictions to increase the BTMS energy efficiency while taking ageing and derating effects into account

A Method of Developing Quantile Convolutional Neural Networks for Electric Vehicle Battery Temperature Prediction Trained on Cross-Domain Data

The energy consumption caused by battery thermal management of electric vehicles can be reduced using predictive control. A predictive controller needs a prediction model of the battery temperature, for example for different battery cooling and heating thresholds. In the proposed method, cross-domain data from simulation, vehicle fleet and weather stations were analyzed and processed as training data for a Convolutional Neural Network (CNN). The CNN took data from previous road segments and predictions for following road segments as input and predicted the change in battery temperature as quantile sequences over a prediction horizon. Properties of the collected cross-domain data sets were analyzed and considered during preprocessing, before 150 models were trained, of which the best performing model was further analyzed. Point-forecast metrics and quantile-related metrics were used for model comparison and evaluation. For example, the median prediction achieved a mean absolute error (MAE) of 0.27 ◦C and the true values were below the median prediction in 47% of the test data. Possible improvements of the method such as increasing data size, using more complex architectures as well as optimizing the horizon sizes were discussed. In conclusion, the method was able to well predict battery temperatures for different battery cooling thresholds