Comparative Study of Real-Time HEV Energy Management Strategies

An adequate energy management strategy is the key to optimizing hybrid electric vehicle fuel efficiency. Various real-time controls have been recently developed. As each study is performed in a specific context, a comparative analysis is critically needed to point out their pros and cons. This paper proposes a comparison between three promising real-time strategies: adaptive equivalent consumption minimization strategy (A-ECMS), optimal control law (OCL), and stochastic dynamic programming (SDP). Two offline algorithms are used as benchmark: Pontryagin's minimum principle and dynamic programming. Implementation and parameters setting issues are discussed for each strategy. The real-time strategies robustness is then evaluated over several types of driving cycles and a statistical analysis is conducted using random cycles generated by Markov process. Simulation results show that OCL needs improvement. A-ECMS reaches the best fuel saving performance when used with parameter sets adjusted to the driving environment, while SDP better respects the charge sustaining constraint.