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Hybrid electric vehicles (HEVs) have an over-actuated system by including twopower sources, a battery pack and an internal combustion engine. This featureof HEV is exploited in this paper to simultaneously achieve accurateidentification of battery parameters/states. By actively injecting currentsignals, state of charge, state of health, and other battery parameters can beestimated in a specific sequence to improve the identification performance whencompared to the case where all parameters and states are estimated concurrentlyusing the baseline current signals. A dynamic programming strategy is developedto provide the benchmark results about how to balance the conflictingobjectives corresponding to identification and system efficiency. The tradeoffbetween different objectives is presented to optimize the current profile sothat the richness of signal can be ensured and the fuel economy can beoptimized. In addition, simulation results show that the Root-Mean-Square errorof the estimation can be decreased by up to 100% at a cost of less than 2%increase in fuel consumption. With the proposed simultaneous identification andcontrol algorithm, the parameters/states of the battery can be monitored toensure safe and efficient application of the battery for HEVs.

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