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

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 \boldmath 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.

Automated summary

This paper investigates the predictive Equivalent Consumption Minimization Strategy for hybrid vehicle energy management. It formulates the energy management as a receding optimization problem to determine torque split between the internal combustion engine and electric machine. By exploiting the slow dynamic distribution and rational tuning of algorithm parameters, the strategy allows for controlling state of charge and achieving low fuel consumption.