The transition to electric vehicles presents unique challenges and opportunities in the realm of transportation planning. This thesis addresses the critical need for advanced planning methods to ensure seamless long distance travel with electric vehicles, focusing on the integration of risk-aware hierarchical task network planning. The proposed framework leverages comprehensive domain knowledge, including battery management, charging infrastructure, energy consumption, environmental conditions, and driver preferences, to develop robust and efficient travel plans. By incorporating probabilistic models, the framework accounts for the inherent uncertainties and risks associated with electric vehicle travel. The methodology involves knowledge acquisition, requirements analysis, domain modelling, implementation, and evaluation. The evaluation phase assesses the performance of the implemented model under various scenarios and risk attitudes, demonstrating its effectiveness in enhancing the overall travel experience for electric vehicle drivers. This work contributes to the field by providing a scalable and adaptable planning solution that addresses the complexities of electric vehicle navigation, promoting sustainable transportation and reducing range anxiety.