Paper published in Mechatronics. 38 (2016) 93–102. Authored by Xiangrui Zeng & Junmin Wang.

Many hybrid electric vehicle (HEV) energy management strategies are developed and evaluated under fixed driving cycles. However in the real-world driving, vehicles are very unlikely to keep running under a fixed known cycle. Instead, a lot of vehicles run on fixed routes. Unfortunately, human driving data collected on a driving simulator shows that it is very difficult to select or create a determined typical driving cycle to rep- resent the fixed-route driving due to the uncertainties in traffic light stops and driver behaviors. This paper presents a two-level stochastic approach to optimize the energy management strategy for fixed-route HEVs. The historical data on the fixed route are utilized and a road-segment-based stochastic HEV energy consump- tion model is built. The higher-level energy optimization problem is solved by stochastic dynamic program- ming (SDP). The SDP computation uses the vehicle model and historical driving data on the fixed route and it can be conducted offline. The result of SDP is a 2-dimension lookup table of parameters for lower-level control strategy therefore this approach can be easily real-time implemented in practice. The developed stochastic approach is compared with three strategies using the data collected on the driving simulator: the optimal energy consumption by assuming all trip information is known in advance and solved via dynamic programming (DP), a determined energy management approach using typical trip data of the fixed-route driving, and a simple strategy which does not require any route data. Simulation results show that the proposed stochastic energy management strategy consumes 1.8% more energy than the optimal result after 24 trips on the fixed route and considerably outperforms the other two real-time HEV energy management strategies.

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