EFFEREST targets a decisive leap forward in the novel use of data to achieve energy-efficient EV designs, matching enhanced user acceptance with efficient vehicle operation. By leveraging insights from real-world fleet behavior, the project seeks to deliver substantial improvements. Users will benefit from personalized data and the ability to choose different vehicle performance modes, allowing them to experience tailored eco-functionality that meets their everyday needs. This will encourage long-term energy-saving behavior. To achieve its goals, EFFEREST brings together 11 partners from both industry and academia, representing the entire EV value chain. In Figure 2 a schematic overview of the EFFEREST project is given including all participating partners. The three key challenges and solutions in EFFEREST, namely a holistic user-centric approach, incorporating artificial intelligence (AI)-enhanced system design and operation, and demonstrating the project outcomes, are outlined in greater detail below.
The Holistic User-Centric Approach: EFFEREST will develop user-centric design solutions and control functionalities for making EVs more affordable, energy-efficient, comfortable, safe, and tailored to the individual user needs. Novel indicators will be identified that more accurately and holistically represent the potential impact that such technical improvements have on user experience. This will help to derive development targets which balance user benefits and engineering effort. A holistic user-centric energy management system control (HUC) will be developed, integrating innovative, tailored vehicle control functions for thermal management and powertrain optimization. These functions will be designed to reduce energy consumption and/or extend vehicle’s driving range while not compromising comfort. Rather than improving each function in isolation, they will be interconnected within the HUC framework for enhanced overall performance.
AI-Enhanced System Design and Operation: A streamlined co-design framework for design and operation will be implemented based on adaptive DTs, which will be derived from high-fidelity physics-based simulation models and specific measurements with the help of AI and machine learning (ML) techniques. The system of interacting DTs allows for model-based optimization for component rightsizing and the development of an integrated predictive model-based control for the combined system of powertrain, thermal management, and cabin comfort system.
Demonstration of the Results: EFFEREST will demonstrate its achievements in a series vehicle, which is specifically modified during the project and tested on test benches as well as under realistic driving conditions. In addition, virtual demonstrators will be used in a complementing way to assess EFFEREST developments in an extensive set of scenarios, representing the realistic vehicle usage in the EU. To derive those relevant testing scenarios real-fleet data from different sources is analysed and characteristic vehicle use cases are derived during the project, which allows for the estimation of the effective impact of EFFEREST on the overall reduction of the vehicle’s CO2 footprint.
