Simultaneous Identification and Control Using Active Signal Injection for Series Hybrid Electric Vehicles Based on Dynamic Programming

Hybrid electric vehicles (HEVs) are overactuated systems in that they include two power sources: a battery pack and an internal combustion engine. This feature of HEVs is exploited in this article to achieve accurate identification of battery parameters/states. By actively injecting currents, the state of charge, state of health, and other battery parameters can be estimated in a specific sequence to improve identification performance when compared to the case where all parameters and states are estimated concurrently using baseline currents. A dynamic programming strategy is developed to provide the benchmark results regarding how to balance the conflicting objectives corresponding to the identification and system efficiency. The tradeoff between different objectives is presented to optimize the current profile so that the richness of the signal can be ensured and the good fuel economy can be achieved. In addition, simulation results show that the root-mean-square error of the estimation can be decreased by up to 100% at a cost of less than a 2% increase in fuel consumption. With the proposed simultaneous identification and control algorithm, the parameters/states of the battery can be monitored to ensure safe and efficient operation of the battery for HEVs.