Multi-mode predictive energy management for fuel cell hybrid electric vehicles using Markov driving pattern recognizer

Considering the changeable driving conditions in reality, energy management strategies for fuel cell hybrid electric vehicles should be able to effectively distribute power demands under multiple driving patterns. In this paper, the development of an adaptive energy management strategy is presented, including a driving pattern recognizer and a multi-mode model predictive controller. In the supervisory level, the Markov pattern recognizer can classify the real-time driving segment into one of three predefined patterns. Based on the periodically updated pattern identification results, one set of pre-optimized control parameters is selected to formulate the multi-objective cost function. Afterwards, the desirable control policies can be obtained by solving a constrained optimization problem within each prediction horizon. Validation results demonstrate the effectiveness of the Markov pattern recognizer, where at least 94.94% identification accuracy can be reached. Additionally, compared to a single-mode benchmark strategy, the proposed multi-mode strategy can reduce the average fuel cell power transients by over 87.00% under multi-pattern test cycles with a decrement of (at least) 2.07% hydrogen consumption, indicating the improved fuel cell system durability and the enhanced fuel economy.