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Reducing the Computation Effort of a Hybrid Vehicle Predictive Energy Management Strategy

Abstract : The present paper is dedicated to the investigation of a predictive Equivalent Consumption Minimization Strategy. The objective is to determine the torque split between the internal combustion engine and the electric machine of a hybrid vehicle. The energy management is formulated as a receding optimization problem. To avoid a complex prediction of the vehicle speed and acceleration over time, the slow dynamic of their distribution is exploited. A rational tuning of the algorithm parameters is proposed as well as some improved implementations. The number of individual operations (additions, multiplications, interpolations, etc) required per seconds is discussed. Finally, the energy management algorithm energy consumption are assessed over different driving cycles, including one with a \boldsymbol 15406 km length obtained using GPS measurements. A comparison with an adaptive Equivalent Consumption Minimization Strategy is provided. The predictive Equivalent Consumption Minimization Strategy allows controlling the state of charge close to a (possibly time varying) set point while providing low fuel consumption.
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Submitted on : Wednesday, November 10, 2021 - 4:29:24 PM
Last modification on : Thursday, November 11, 2021 - 4:00:05 AM

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Sebastien Delprat, Mohamed Riad Boukhari. Reducing the Computation Effort of a Hybrid Vehicle Predictive Energy Management Strategy. IEEE Transactions on Vehicular Technology, Institute of Electrical and Electronics Engineers, 2021, 70 (7), pp.6500-6513. ⟨10.1109/TVT.2021.3082624⟩. ⟨hal-03424936⟩

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