This thesis presents a predictive energy management controller for long-haul hybrid trucks. In a receding horizon control framework, the vehicle speed reference, battery energy reference, and engine on/off decision are optimized over a prediction horizon. A mixed-integer quadratic program (MIQP) is formulated by performing modelling approximations and by including the binary engine on/off decision in the optimal control problem. The branch-and-bound algorithm is applied to solve this problem. Simulation results show fuel consumption reductions between 10-15%, depending on driving cycle, compared to a conventional truck. The hybrid truck without the predictive control saves significantly less. Fuel consumption is reduced by 3-8% in this case. A sensitivity analysis studies the effects on branch-and-bound iterations and fuel consumption when varying parameters related to the binary engine on/off decision. In addition, it is shown that the control strategy can maintain a safe time gap to a leading vehicle. Also, the introduction of the battery temperature state makes it possible to approximately model the dynamic battery power limitations over the prediction horizon. The main contributions of the thesis are the MIQP control problem formulation, the strategy to solve this with the branch-and-bound method, and the sensitivity analysis.


    Access

    Download


    Export, share and cite



    Title :

    Predictive Energy Management of Long-Haul Hybrid Trucks : Using Quadratic Programming and Branch-and-Bound


    Contributors:

    Publication date :

    2021-01-01


    Type of media :

    Theses


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    510 / 629




    Energy-Efficient Timely Transportation of Long-Haul Heavy-Duty Trucks

    Deng, Lei / Hajiesmaili, Mohammad H. / Chen, Minghua et al. | IEEE | 2018


    Model Predictive Eco-Driving Control for Heavy-Duty Trucks Using Branch and Bound Optimization

    Wingelaar, Bart / da Silva, Gustavo R. Goncalves / Lazar, Mircea | IEEE | 2023



    New Mighty Mine Haul Trucks

    Online Contents | 2010