Energy Management Strategies for a Pneumatic-Hybrid Engine Based on Sliding Window Pattern Recognition

This paper presents energy management strategies for a new hybrid pneumatic engine concept which is specific by its configuration in that it is not the vehicle but only the engine itself which is hybridized. Different energy management strategies are proposed in this paper. The first is called Causal Strategy (CS) and implements a rule-based control technique. The second strategy, called Constant Penalty Coefficient (CPC), is based on the minimization of equivalent consumption, where the use of each energy source is formulated in a comparative unit. The balance between the consumption of different energy sources (chemical or pneumatic) is achieved by the introduction of an equivalence factor. The third strategy is called Variable Penalty Coefficient (VPC). In fact, it is beneficial to consider the equivalence coefficient as variable within the amount of pneumatic energy stored in the air-tank i.e. state of charge, because the choice of propulsion mode should be different if the tank is full or empty. In this case, the penalty coefficient appears as a non linear function of the air-tank state of charge. Another way to adapt the penalty coefficient is to recognize a reference pattern during the driving cycle. The coefficient value can then be changed according to an optimized value found for each of the reference cycles. This strategy is called Driving Pattern Recognition (DPR). It involves a technique of sliding window pattern recognition. The concept is to convert the whole driving cycle into smaller pieces to which the equivalence factor can be appropriately adapted. This strategy is based on the assumption that the current driving situation does not change rapidly and thus the pattern is likely to continue into the near future. The identification window size is a parameter which has to be adjusted to attain the maximum of identification success over the reference cycle. We propose to define reference patterns as statistical models. The pattern recognition method is based on a correlation function. To improve analysis, all the results obtained with different energy management strategies are compared with a Dynamic Programming approach (DP) considered as the optimal solution. Results show that about 40% of fuel saving can be achieved by DP. The VPC and DPR strategies give better results than the CPC strategy, not so far from the results obtained with DP