In the paper, a rule-based (RB) control strategy is proposed to optimize on-board energy management on a Hybrid Solar Vehicle (HSV) with series structure. Previous studies have shown the promising benefits of such vehicles in urban driving in terms of fuel economy and carbon dioxide reduction, and that economic feasibility could be achieved in a near future.
The control architecture consists of two main loops: one external, which determines final battery state of charge (SOC) as function of expected solar contribution during next parking phase, and the second internal, whose aim is to define optimal ICE- EG power trajectory and SOC oscillation around the final value, as addressed by the first loop.
In order to maximize the fuel savings achievable by a series architecture, an intermittent ICE scheduling is adopted for HSV. Therefore, the second loop yields the average power at which the ICE is operated as function of the average values of traction power demand and solar power. Expected solar contribution can be estimated starting from widely available solar databases and by processing past solar energy data measured on the vehicle. Neural Networks predictors, previously stored data and/or GPS derived information are suitable to estimate average power requested for vehicle traction.
Extensive simulation analyses were carried out to test the performance of the RB algorithm, also comparing it to Genetic Algorithms-based optimization strategies previously developed by the authors. The results confirm the high potentialities offered by the proposed RB control strategy to perform real-time energy management on hybrid solar vehicles.
The proposed rule-based optimization is currently under-implementation in an NI® cRIO control unit, thus allowing to perform experimental tests on a real HSV prototype developed at University of Salerno.
