Optimal control methods in charge- and trip-planning for electric vehicles

Licentiate thesis, 2026

The transport sector is a major contributor to global greenhouse gas emissions, prompting increasingly stringent regulations and accelerating the transition toward electric mobility. Although electric vehicles (EVs) offer significant potential for emission reduction, their large-scale adoption is still hindered by range anxiety, i.e. the fear of the battery running out before a charging station is reached. Intelligent charge- and trip-planning (ICTP) is a way to address range anxiety by optimizing the charging station selection, the vehicle’s energy consumption, the battery thermal management, and the charging process. However, the resulting problems are typically large-scale, nonlinear, and mixed-integer, which makes them computationally challenging to solve. This thesis develops optimal control methods to solve the ICTP problem in a computationally efficient way, to allow real-time onboard implementation. First, the computational tractability of the ICTP problem is improved through tailored warm-start strategies and the relaxation of binary decision variables, enabling the use of faster continuous solvers and achieving substantial reductions in computation time. Second, a semi-analytical optimal control solver based on Pontryagin’s Maximum Principle is developed for EV charging optimization. The solver yields explicit control laws and its low computation time allows for real-time embedded implementation. Finally, a nonlinear optimal control framework for mission planning of long-range solar-powered EVs is proposed, enabling the joint optimization of trip time and energy management under spatio-temporal constraints. The method was tested on a solar-powered vehicle racing across the Australian Outback.