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International audienceAn 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

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The HVAC system represents the main auxiliary load in electric vehicles (EVs) and requires efficient control approaches that balance energy saving and thermal comfort. In fact, passengers always demand more comfort, but on the other hand the HVAC system consumption strongly impacts the vehicle driving range, which constitutes the major concern in EVs. In this paper, dynamic programing is applied to develop an HVAC system supervisor that optimizes the thermal comfort on a given journey, for given climatic conditions and energy available. The electric vehicle model and the optimization approach are presented. Two test-cases, corresponding to hot climate, are simulated. In the first one, the energetic cost of improved comfort is quantified, while in the second one the trade-off between driving speed and thermal comfort is analyzed.

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