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Model Predictive Control for Series-Parallel Plug-In Hybrid Electrical Vehicle

The automotive industry is required to deal with increasingly stringent legislation for greenhouse gases. Hybrid Electric Vehicles, HEV, are gaining acceptance as the future path of lower emissions and fuel consumption. The increased complexity of multiple prime movers demand more advanced control systems, where future driving conditions also becomes interesting. For a plug-in Hybrid Electric Vehicle, PIHEV, it is important to utilize the comparatively inexpensive electric energy before the driving cycle is complete, this for minimize the cost of the driving cycle, since the battery in a PIHEV can be charged from the grid. A strategy with length information of the driving cycle from a global positioning system, GPS, could reduce the cost of driving. This by starting to blend the electric energy with fuel earlier, a strategy called blended driving accomplish this by distribute the electric energy, that is charged externally, with fuel over the driving cycle, and also ensure that the battery’s minimum level reaches before the driving cycle is finished. A strategy called Charge Depleting Charge Sustaining, CDCS, does not need length information. This strategy first depletes the battery to a minimum State of Charge, SOC, and after this engages the engine to maintain the SOC at this level. In this thesis, a variable SOC reference is developed, which is dependent on knowledge about the cycle’s length and the current length the vehicle has driven in the cycle. With assistance of a variable SOC reference, is a blended strategy realized. This is used to minimize the cost of a driving cycle. A comparison between the blended strategy and the CDCS strategy was done, where the CDCS strategy uses a fixed SOC reference. During simulation is the usage of fuel minimized; and the blended strategy decreases the cost of the driving missions compared to the CDCS strategy. To solve the energy management problem is a model predictive control used. The designed control system follows the driving cycles, is charge sustaining and solves the energy management problem during simulation. The system also handles moderate model errors.

Jimmy Engman


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Senast uppdaterad: 2019-03-29