Electric vehicle battery management algorithm development using a HIL simulator incorporating three-phase machines and power electronics

This paper describes a hardware-in-the-loop (HIL) test rig for the test and development of electric vehicle battery management and state-estimation algorithms in the presence of realistic real-world duty cycles. The rig includes two back-to-back connected brushless DC motors, the respective power electronic controllers, a target battery pack, a dSPACE real-time simulator, a thermal chamber and a PC for human-machine interface. The traction motor is commanded to track a reference velocity based on a drive cycle and the target battery pack provides the required power. Except the battery pack and the electric machine which are real, other parts of a vehicle powertrain system are modelled and used in the real-time simulator. A generic framework has been developed for real-time battery measurement, model identification and state estimation. Measurements of current and battery terminal voltage are used by an identification unit to extract parameters of an equivalent circuit network (ECN) model in real-time. Outputs of the identification unit are then used by an estimation unit which uses an artificial intelligent model trained to find the relationship between the battery parameters and state-of-charge (SOC). The results demonstrate that even with a high noise level in measured data, the proposed identification and estimation algorithms are able to work well in real-time