Energy Based Hysteresis for Real-Time State Optimization in Hybrid Torque Controls

Through real-time online optimization, the full potential of the performance and energy efficiency of multi-gear, multi-mode, series–parallel hybrid powertrains can be realized. The framework allows for the powertrain to be in its most efficient configuration amidst the constantly changing hardware constraints and performance objectives. Typically, the different gears and hybrid/electric modes are defined as discrete states, and for a given vehicle speed and driver power demand, a formulation of optimization costs, usually in terms of power, are assigned to each discrete states and the state which has the lowest cost is naturally selected as the desired of optimum state. However, the optimization results would be sensitive to numerical exactitude and would typically lead to a very noisy raw optimum state. The generic approach to stabilization includes adding hysteresis costs to state-transitions and time-debouncing. These added costs could result in systems remaining in sub-optimal states during steady state operation when the hysteresis thresholds are not overcome. This paper proposes an improved hysteresis framework where time-dependent and transition cost considerations are integrated into the optimization. The results show that this method produces an improved stability while maintaining a level of energy efficiency compared to the existing hysteresis method.