Energy management strategy of series hybrid electric bus using nonlinear programming and motor torque distribution

Abstract

One of the most widespread issues with the hybrid electric vehicle is the energy management strategy from the engine to the motor for fuel economy improvement. There are lots of approaches to achieve it, but many of them are hard to apply and/or the mathematical background is so complicated. Furthermore, the sophisticated and complicated strategy usually required an ideal model and environment that the practical application was not available. Therefore, simple rule based strategies are often in use for the real-time applications. Such a tendency was evident in SHEV (Series Hybrid Electric Vehicle) because the structure of SHEV is so simple that those rule based strategies such as thermostat and power follower strategies are considered to be enough for covering the energy flow management from the engine to the battery [1-3]. This research proposes an advanced strategy which overcomes those weaknesses. The strategy proposed in this research fully optimizes the SHEV energy applicable to SHEV that the efficiency of the total system increase by finding the most efficient operating points in each component in the vehicle. The proposed strategy, which uses a semi MPC framework, has its foundation on NLP (Nonlinear Programming) for finding minimum fuel consumption and the speed prediction with intra-city bus was evaluated with the real bus data for the future path plan. Furthermore, the manipulation of command signal (signal synchronization, signal bundling, signal removing & filling up, and zero speed synchronization) and the traction torque distribution in TMU (Traction Motor Unit) were also considered, where the torque distribution made it possible avoid large currents from the battery to the motor during the periods of high loading required in the traction. The performance of the proposed strategy was compared to that of other strategies such as DP (Dynamic Programming), thermostatic strategy, and power follower strategy where DP gives the reference global optimal operating points and thermostat & power follower strategies are the well-known real-time energy distribution strategy. So, the contribution of the proposed techniques could be evaluated. As a result, the proposed strategy in this paper shows much better fuel economy with the practical, fast, and exact methodology uniquely adapted to series type hybrid electric intra-city bus. All simulation was achieved based on AMEsim and Simulink co-simulation for the non-analytic forward bus model and NLP solver of NPSOL was used for the simulation.