Adaptive Software Component for Predicting Energy Consumption of an Electric Vehicle on a Planned Route

This paper presents the development of an adaptive software component for forecasting the energy consumption of electric vehicles along planned routes. It includes a conceptual framework, architectural design, and modular software composition, introducing mathematical models for managing energy consumption with a model-based design approach for precise predictions and optimization. The importance of data sources such as route information, vehicle condition, and driver behavior is emphasized to create a comprehensive state vector for energy optimization. Following ISO 26262 and A-SPICE 3.1 standards, the implementation uses a model-based approach with Simulink and aligns with the V-Model for rigorous validation. The methodology details segmenting routes and optimizing energy consumption for each segment, considering driving style and environmental conditions. The gradient search method adjusts energy consumption to minimize usage while maximizing comfort and ensuring route completion. This research lays the groundwork for future advancements in predictive energy management systems for electric vehicles, with potential real-world applications. Future work will focus on refining predictive models, exploring machine learning for improved accuracy, and integrating real-time data from connected vehicle technologies for dynamic optimization.