This content is not included in your SAE MOBILUS subscription, or you are not logged in.
An Unsupervised Machine-Learning Technique for the Definition of a Rule-Based Control Strategy in a Complex HEV
Published April 05, 2016 by SAE International in United States
Citation: Finesso, R., Spessa, E., and Venditti, M., "An Unsupervised Machine-Learning Technique for the Definition of a Rule-Based Control Strategy in a Complex HEV," SAE Int. J. Alt. Power. 5(2):308-327, 2016, https://doi.org/10.4271/2016-01-1243.
An unsupervised machine-learning technique, aimed at the identification of the optimal rule-based control strategy, has been developed for parallel hybrid electric vehicles that feature a torque-coupling (TC) device, a speed-coupling (SC) device or a dual-mode system, which is able to realize both actions. The approach is based on the preliminary identification of the optimal control strategy, which is carried out by means of a benchmark optimizer, based on the deterministic dynamic programming technique, for different driving scenarios. The optimization is carried out by selecting the optimal values of the control variables (i.e., transmission gear and power flow) in order to minimize fuel consumption, while taking into account several constraints in terms of NOx emissions, battery state of charge and battery life consumption. The results of the benchmark optimizer are then processed with the aim of extracting a set of optimal rule-based control strategies, which can be implemented onboard in real-time. The input variables of the rule-based strategy are the vehicle power demand, the vehicle speed and the state of charge of the battery. The method for the rule extraction can be summarized as follows. A clustering algorithm discretizes the input domain (in terms of vehicle power demand, vehicle speed and state of charge of the battery) into a mesh of clusters. The generic rule associated to a specific cluster (i.e., the combination of gear and power flow that has to be actuated) is identified by searching for the control strategy most frequently adopted by the benchmark optimizer within the considered cluster. The optimal mesh of clusters is generated using a genetic algorithm technique. Optimal sets of rules are identified for different driving scenarios. These strategies can then be implemented on-board, provided the mission features are known at the beginning of the trip. The main advantage of the proposed technique is that the definition of the rule-based strategy is derived from a machine learning method and is not based on heuristic techniques.