Analysis of a rule-based control strategy for on-board energy management of series hybrid vehicles

In the paper, the performances of a rule-based (RB) control strategy for series hybrid vehicles are assessed via comparison with a batch Genetic Algorithm-based (GA) optimization. The suitability of GA optimization as reference benchmark for series architecture is demonstrated through comparison with Dynamic Programming technique. Specifically in this paper, a hybrid solar vehicle (HSV) was considered, thus requiring to define the heuristic rules as function of both average traction power and current solar irradiation. The comparison with the reference GA benchmark confirms the suitability of the proposed RB strategy for HSV on-board energy management. Extensive simulations were performed to test the influence of driving cycle features, power-prediction time-horizon and solar irradiation on HSV fuel economy. Such simulation analysis, beyond providing useful indications about correct implementation of the RB strategy on both hybrid and solar hybrid cars, also demonstrates the potentialities offered by HSV powertrains in both urban and highway driving conditions.