Abstract
This paper introduces the development and calibration process of a vehicle drivetrain observer used in hybrid and electric vehicles for active damping control (ADC) and for the improvement of the electric machine’s rotor angle signal quality. This approach starts with creating an overall vehicle model that includes the electric machine, the transmission, side shafts, the tires and the vehicle body. For control engineering purposes, that multi-order-model is then reduced into a two-mass-oscillator which can be easily described in state-space form. Using this reduced drivetrain model and applying a Luenberger observer approach, not only the signal quality of both the instrumented rotor angle and the speed of the electric machine can be improved considerably but also the oscillation dynamics of this vehicle drivetrain can be estimated. If not compensated during vehicle operation, drivetrain oscillations might lead to increased drivetrain wear, NVH issues and limited ride comfort; therefore, the oscillation speed is very important in computing an active damping torque that is to compensate drivetrain oscillations. Calibration of the vehicle drivetrain observer is done using specific vehicle test data that are fed into a standalone calibration tool identifying the parameters of the vehicle drivetrain as well as the Luenberger feedback vector. Based on these data, a proper active damping control application is set-up and verified in various vehicle tests and to lead to the calibration finally to the application in several hybrid and electric vehicle series projects (e.g. Peugeot 3008 HYbrid4).
Highlights
Hybrid (HEV) and electric vehicles (EV) do show a quite different vehicle drivetrain setup compared to conventional, combustion-enginedriven-only vehicles
This paper introduces the development and calibration process of a vehicle drivetrain observer used in hybrid and electric vehicles for active damping control (ADC) and for the improvement of the electric machine’s rotor angle signal quality
A Luenberger observer [3, 4] combines a model of a real-world process with one or several internal measurement signals derived from the real-world process ( y Rotor ) leading to model state estimates which cannot be measured (Figure 7)
Summary
Hybrid (HEV) and electric vehicles (EV) do show a quite different vehicle drivetrain setup compared to conventional, combustion-enginedriven-only vehicles. When launching a HEV or an EV from standstill using the electric machine, there is no need for a friction clutch since an electric machine is able to provide torque from zero speed already In this case torque excitations on a vehicle drivetrain that does not include some sort of launch element result in e-machine speed oscillations that limit ride comfort and tend to significantly increase drivetrain wear. Calibration of the drivetrain observer model is done using vehicle test data (e.g., electric machine torque and speed) and feeding it into a standalone parameter identification tool that computes drivetrain and observer feedback parameters Applying these parameters into the electric machine’s torque and current control loop and properly calibrating the active damping control, drivetrain oscillations can be reduced considerably while improving ride comfort
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