Real-Time Optimal Energy Management of Heavy Duty Hybrid Electric Vehicles

The performance of energy flow management strategies is essential for the success of hybrid electric vehicles (HEVs), which are considered amongst the most promising solutions for improving fuel economy as well as reducing exhaust emissions. The heavy duty HEVs engaged in cycles characterized by start-stop configuration has attracted widely interests, especially in off-road applications. In this paper, a fuzzy equivalent consumption minimization strategy (F-ECMS) is proposed as an intelligent real-time energy management solution for heavy duty HEVs. The online optimization problem is formulated as minimizing a cost function, in terms of weighted fuel power and electrical power. A fuzzy rule-based approach is applied on the weight tuning within the cost function, with respect to the variations of the battery state-of-charge (SOC) and elapsed time. Comparing with traditional real-time supervisory control strategies, the proposed F-ECMS is more robust to the test environments with rapid dynamics. The proposed method is validated via simulation under two transient test cycles, with the fuel economy benefits of 4.43% and 6.44%, respectively. The F-ECMS shows better performance than the telemetry ECMS (T-ECMS), in terms of the sustainability of battery SOC.