Digitization has made data a central element in all areas of life and business. More and more data is being generated, stored and processed. In the process, they appear in the most diverse forms and characteristics. Data analysis is the analysis of data in order to extract valuable information from it. At FZI/ITIV, we are researching methods to detect and describe hidden relationships in the data.
Range prediction for electric vehicles
Electric vehicles are seen as an environmentally friendly alternative in the fight against greenhouse gases, pollutant emissions and dependence on fossil fuels. Despite the ecological advantages that these vehicles bring, electric vehicles are still not very popular with customers. The reason for this is the so-called range anxiety, which describes the driver's concern that the battery of the electric vehicle might not be sufficient to reach the destination. The exact determination of the range of an electric vehicle is a key success factor for high customer acceptance and the widespread introduction of electric vehicles. We at FZI/ITIV are working on methods to accurately predict the range of an electric vehicle.
Pattern recognition in time series data
Historical data is increasingly being used in companies to support decision-making. Due to digitalization and the associated ease of collecting data, the trend will increase in the future. For predictive maintenance, we at ITIV/FZI are looking at different methods to classify time series and identify patterns in them. This classification can be used for decision making and helps the engineer to make informed decisions.