Energy Management Strategies for Hybrid-Pneumatic Engine Studied on an Markov Chain Type Generated Driving Cycle

This paper presents energy management strategies for a new hybrid pneumatic engine concept, which is specific by its configuration: It is not a vehicle but only an engine itself which is hybridized. This arrangement could provide as much as 30% of fuel saving depending on the driving cycle. Therefore different energy management strategies are proposed and compared in this paper. The first of them is called Causal Strategy and implements a rule-based control technique. A second strategy called Constant Penalty Coefficient is based on minimization of equivalent consumption, where the use of each energy source is formulated in a comparative unit. The balance between consumption of different energy source (chemical or pneumatic) is reached by introduction of an equivalence factor. The third strategy is called Variable Penalty Coefficient, where the equivalence factor is consider as variable within the amount of pneumatic energy stored in the air-tank. Another way to adapt the penalty coefficient is to recognize a reference pattern during the driving cycle. The equivalence factor value can then be changed according to an optimized one found for each of the reference cycle. This strategy is called Driving Pattern Recognition (DPR). The benefits of the DPR strategy is demonstrated on the candidate cycles, which are constructed at the basis of the Markov chain theory from the reference cycle. To provide better analysis, all results obtained by the different methods are compared with a Dynamic Programming approach considered as the optimal solution. The objective of this paper is to validate the DPR strategy on the Markov chain type constructed cycles for the concept of the hybrid-pneumatic engine.