Stochastic Predictive Boundary Management for a Hybrid Powertrain

Hybrid electric vehicles (HEVs) are capable of improving fuel economy with reduced emissions over traditional vehicles powered only by the internal combustion (IC) engine. HEV durability is often significantly limited by the useful life of the battery, and battery life can be significantly reduced if the battery is often operated outside its allowed discharging/charging limits. This could occur particularly at extremely low temperature, leading to permanent damage and reduced useful life of the battery. To reduce battery overdischarging duration for extended battery life, this paper proposes a stochastic predictive boundary management (SPBM) strategy to proactively make the engine power available to the powertrain based on the predicted torque requirement and its prediction error variance. The effectiveness of the proposed SPBM is validated in simulations, and its performance is compared with the baseline power follower control strategy (PFCS) and predictive boundary management (PBM) under five typical driving cycles with selected initial battery temperature. The simulation results show that the proposed control strategy dramatically reduces the battery overdischarging energy under all five typical driving cycles over both PFCS and PBM, where the average reduction is 82% under aggressive US06 and ARB02 driving cycles, 67% under NYCC and FTP driving cycles, and 37% under the IM240 highway driving cycle. Note that the adaptive prediction and its error variance can be calculated in real time with fairly low computational load.


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  • Accession Number: 01611647
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jun 21 2016 4:18PM