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

TL;DR

This paper introduces a method for hybrid electric vehicles that uses active signal injection and dynamic programming to simultaneously identify battery parameters and optimize control for improved accuracy and efficiency.

Contribution

It proposes a novel simultaneous identification and control approach using active signal injection and dynamic programming for HEV batteries.

Findings

Estimation error reduced by up to 100%.

Fuel consumption increased by less than 2%.

Enhanced battery parameter monitoring for safety and efficiency.

Abstract

Hybrid electric vehicles (HEVs) have an over-actuated system by including two power sources, a battery pack and an internal combustion engine. This feature of HEV is exploited in this paper to simultaneously achieve accurate identification of battery parameters/states. By actively injecting current signals, state of charge, state of health, and other battery parameters can be estimated in a specific sequence to improve the identification performance when compared to the case where all parameters and states are estimated concurrently using the baseline current signals. A dynamic programming strategy is developed to provide the benchmark results about how to balance the conflicting objectives corresponding to identification and system efficiency. The tradeoff between different objectives is presented to optimize the current profile so that the richness of signal can be ensured and the fuel economy can be optimized. 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 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 application of the battery for HEVs.