Browsing by Author "Samaranayake, Lilantha"

Now showing 1 - 5 of 5
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    Cost functions for degradation control of electric motors in electric vehicles
    (Institute of Electrical and Electronics Engineers, 2015-11-16) Samaranayake, Lilantha; Longo, Stefano
    This paper introduces a novel set of electric motor degradation cost functions based on energy usage, energy loss and work output, against their continuous operation rated values recommended by the manufacturer. Unlike conventional electric motor degradation indicators such as the bearing life and insulation life based service factors, these cost functions account for the quantified time in the degradation process. The cost functions are evaluated throughout the operational life of the motor using real-time measurements. Hence, they give a very accurate indication, which may be adapted for online controller tuning. This solid establishment of a degradation cost function also enables the system designer to give the user a choice between performance and degradation minimization. The proposed cost function scheme has experimentally been verified using a hardware-in-the-loop electric powertrain test-rig where standard drive cycles are used to conduct the experiments. The experimental results reveal that the degradation cost functions Cumulative Input Energy Ratio (CIER), Cumulative Loss Ratio (CLR) and Cumulative Work Ratio (CWR) accurately represent the electric motor degradation both qualitatively and quantitatively.
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    Degradation control for electric vehicle machines using nonlinear model predictive control
    (IEEE, 2017-01-17) Samaranayake, Lilantha; Longo, Stefano
    Electric machines (motors and generators) are over actuated systems. In this paper, we show how to exploit this actuation redundancy in order to mitigate machine degradation while simultaneously ensuring that the desired closed loop performance is maintained. We formulate a multiobjective optimization problem with a cost function having terms representing closed loop performance and component degradation for an inverter-fed permanent magnet synchronous motor. Such machines are important as they are widely used as the prime mover of commercial electric vehicles. The resulting optimal control problem is implemented online via a nonlinear model predictive control (NMPC) scheme. The control framework is validated for standard vehicle drive cycles. Results show that the NMPC scheme allows for better closed loop performance and lower degradation than standard industrial controllers, such as the field-oriented control method. Hence, this paper demonstrates how the remaining useful life of a machine can be increased by appropriate controller design without compromising performance.
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    Electric vehicle battery management algorithm development using a HIL simulator incorporating three-phase machines and power electronics
    (2016-09-09) Fotouhi, Abbas; Propp, Karsten; Samaranayake, Lilantha
    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.
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    Fuzzy logic control for energy saving in autonomous electric vehicles
    (IEEE, 2015-03-06) Al-Jazaeri, Ahmed O.; Samaranayake, Lilantha; Longo, Stefano; Auger, Daniel J.
    Limited battery capacity and excessive battery dimensions have been two major limiting factors in the rapid advancement of electric vehicles. An alternative to increasing battery capacities is to use better: intelligent control techniques which save energy on-board while preserving the performance that will extend the range with the same or even smaller battery capacity and dimensions. In this paper, we present a Type-2 Fuzzy Logic Controller (Type-2 FLC) as the speed controller, acting as the Driver Model Controller (DMC) in Autonomous Electric Vehicles (AEV). The DMC is implemented using realtime control hardware and tested on a scaled down version of a back to back connected brushless DC motor setup where the actual vehicle dynamics are modelled with a Hardware-In-the-Loop (HIL) system. Using the minimization of the Integral Absolute Error (IAE) has been the control design criteria and the performance is compared against Type-1 Fuzzy Logic and Proportional Integral Derivative DMCs. Particle swarm optimization is used in the control design. Comparisons on energy consumption and maximum power demand have been carried out using HIL system for NEDC and ARTEMIS drive cycles. Experimental results show that Type-2 FLC saves energy by a substantial amount while simultaneously achieving the best IAE of the control strategies tested.
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    A hardware-in-the-loop test rig for development of electric vehicle battery identification and state estimation algorithms
    (Inderscience, 2018-03-01) Fotouhi, Abbas; Propp, Karsten; Samaranayake, Lilantha; Auger, Daniel J.; Longo, Stefano
    This paper describes a hardware-in-the-loop (HIL) test rig for the test and development of electric vehicle battery parameterisation and state-estimation algorithms in the presence of realistic real-world duty cycles. The rig includes two electric machines, a battery pack, a real-time simulator, a thermal chamber and a PC for human-machine interface. 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 are used to extract parameters of an equivalent circuit network model. Outputs of the identification unit are then used by an estimation unit trained to find the relationship between the battery parameters and state-of-charge. 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.