Stochastic Charge Planning with Dynamic Programming

Sammanfattning

The development of charge planning algorithms which extend further than thoseconsidering uncorrelated disturbance models and produce robust policies is an importantsubject. The freight sector is moving towards battery electric trucks whereuncertainties can have a major impact on missions due to state of charge constraints.Therefore, this thesis investigates dynamic programming algorithms for use in chargeplanning. Disturbances are modeled as Gaussian processes, which for certain structuresadmits an equivalent transformation to an LTI SDE system. Using this transformation,the distribution along state trajectories are estimated using an unscentedKalman filter. The UKF showed good performance for the modeled disturbances,with a largest mean bias of 1.106% in a worst-case scenario. The proposed approximatedynamic based charge planning algorithm became robust under stochasticexternal uncertainties from wind and traffic by implementing chance constraints.The proposed planning algorithms achieved better performance than both a simplerdeterministic dynamic programming algorithm and a simple heuristic planner.Computational complexity remains a key concern for real time implementations andis a crucial challenge when designing stochastic charge planning algorithms.