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This paper examines and optimizes parameters that affect the sizing and control of a hybrid embedded power supply composed of Li-ion batteries and supercapacitors in electric vehicle applications. High demands including power and energy density, low charge/discharge power stress on the battery (long lifetime), lightweight design and relatively modest cost at the same time cannot be provided solely by batteries or supercapacitors. For this reason, we propose the use of a Li-ion battery/supercapacitor hybrid embedded power supply for an urban electric vehicle. The sizing process of this system including the optimization of the power sharing is done thanks to a developed hybrid Particle Swarm-Nelder-Mead (PSO-NM) algorithm involving multi-objective optimization. This approach also allows us to optimize the proposed energy management strategies based on frequency rule-based control and different ways of supercapacitors energy regulation. Obtained results show that the hybrid embedded power supply with the proposed control strategies is able to offer the best performances for the chosen electric vehicle in terms of weight, initial cost and battery lifetime.Index Terms-electric vehicle, hybrid embedded power supply, Li-ion battery, supercapacitor, energy management strategy, hybrid (PSO-NM) optimization algorithm. I. NOMENCLATURE SymbolsAerodynamic drag force (N) Tire rolling resistance force (N) Gravitational resistance force by slope in the road (N) Acceleration force (N) Air density (kg/m3) S Vehicle frontal surface ( 2 ) Penetration air coefficient 0 , 1 Rolling resistance coefficients ℎ Vehicle forward speed (m/s) M Vehicle mass (Kg) Electric vehicle power (W) Final value of the energy consumption in one mission (J) _ Series battery cells in one branch _ Branches number in parallel of battery pack _ Nominal energy of battery cell ( J) _ Battery weight ratio in energy consumption ( J/Kg) W el_ B Weight of battery cell (Kg) 0 _ Internal resistance of battery cell (Ω) U _ Nominal voltage of battery cell (V) _ Maximum of traction power (W) _ Maximum of braking power (W) _ Maximum discharge power of battery cell (W) _ Maximum charge power of battery cell (W) ∂ P W cons Battery weight ratio in discharge power (W/Kg) ∂ P W rec Battery weight ratio in charge power (W/Kg) _ Series supercapacitor cells in one branch _ Branches number in parallel of supercapacitor pack ∆ Energy requirements of supercapacitor pack to ensure transient peak power (J) − Maximum of supercapacitor energy in one cycle (J) − Minimum of supercapacitor energy in one cycle (J) _ Nominal capacity of supercapacitor cell (F) DC bus voltage ( ) Supercapacitor weight ratio in discharge power (W/Kg) Supercapacitor weight ratio in charge power (W/Kg) _ Weight of supercapacitor cell (Kg) Cutoff frequency (Hz) Maximum limit of battery discharge power (W) Maximum limit of battery charge power (W) 0− Battery power before regulation of supercapacitor energy − Recharge power needed to ensure supercapacitor energy regulation − Optimal value of supercapacitor state o...

In this paper, a dynamic model of Li-ion batteries incorporating electrothermal and ageing aspects is proposed for electric vehicle applications. The main goal of the proposed model is to be both simple and sufficiently representative of the physical phenomena occurring in a battery cell. These two features allow for using this model as an evaluation tool of electric vehicle performances under different operational and environmental conditions. The developed model is based on an equivalent circuit diagram coupled with a thermal circuit and a semi-empirical ageing equation. Identification of parameters in the dynamic model is conducted by measurement tests in timedomain, which uses a hybrid Particle Swarm-Nelder-Mead optimization algorithm to achieve excellent prediction over the whole applicable current and state of charge ranges. The validation results show that the proposed model is able to simulate the dynamic interaction between the battery ageing and the thermal as well as electric behavior with sufficient accuracy in the range tested.

Abstract-This paper deals with experimental validation of a reconfiguration strategy for sensor fault-tolerant control (FTC) in induction-motor-based electric vehicles (EVs). The proposed active FTC system is illustrated using two control techniques: indirect field-oriented control (IFOC) in the case of healthy sensors and speed control with slip regulation (SCSR) in the case of failed current sensors. The main objective behind the reconfiguration strategy is to achieve a short and smooth transition when switching from a controller using a healthy sensor to another sensorless controller in the case of a sensor failure. The proposed FTC approach performances are experimentally evaluated on a 7.5-kW induction motor drive.

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