Optimization of Tremblay's battery model parameters for plug-in hybrid electric vehicle applications

Abstract

Accurate modeling of batteries for plug-in hybrid vehicle applications is of fundamental importance to optimize the operation strategy, extend battery life and improve vehicle performance. Tremblay's battery model has been specifically designed and validated for electric vehicle applications. Tremblay's parameter identification method is based on evaluating the three remarkable points manually picked from a manufacturer's discharge curve. This method is error prone and the resultant discharge curve may deviate significantly from the experimental curve as reported in previous studies. This paper proposes to use a novel quantum-behaved particle swarm optimization (QPSO) parameter estimation technique to estimate the model parameters. The performance of QPSO is compared to that of genetic algorithm (GA) and particle swarm optimization (PSO) approaches. The QPSO technique needs less tuning effort than other techniques since it only uses one tuning parameter. Reducing the number of iterations should be a welcome development in most applications areas. Results show that the QPSO parameter estimation technique converges to acceptable solutions with fewer iterations than that obtained by the GA and the PSO approaches. IndexTerms-Tremblay's battery model, parameter identification, genetic algorithm, particle swarm optimization, quantum-behaved particle swarm optimization.I.

Impacts of supercapacitors on battery lifetime in hybrid energy storage system in building integrated photovoltaic DC micro-grid

“…According to the operating range of the battery (10% < SoC < 90%), we don't need to consider end of charge and end of discharge phenomena. In these conditions, the accuracy of a linear model is sufficient [27], [28]. Moreover, according to Tremblay's assumptions in [25], the battery temperature is considered constant, and self-discharged and Peukert effect aren't taken into account.…”

Forecasting Charging Point Occupancy Using Supervised Learning Algorithms

“…Tremblay et al obtained battery model parameters by evaluating just three points from the manufacturer's discharge curve in steady state. The study by Zhang et al was conducted on optimization of Tremblay's battery model parameters for plug-in hybrid EV applications [29]. Since the original method by Tremblay et al is error prone, Zhang et al proposed to use a novel quantum-behaved particle swarm optimization (QPSO) parameter-estimation technique to estimate the model parameters [28,29].…”

“…The study by Zhang et al was conducted on optimization of Tremblay's battery model parameters for plug-in hybrid EV applications [29]. Since the original method by Tremblay et al is error prone, Zhang et al proposed to use a novel quantum-behaved particle swarm optimization (QPSO) parameter-estimation technique to estimate the model parameters [28,29]. Another model employed by [30][31][32] is the Volterra model, which can be used to approximate complex nonlinear dynamics inherent to the battery.…”

Accurate battery model parameter identification using heuristic optimization

“…To accurately estimate the model parameters, one effective way is by performing parameter extraction from manufacturer discharge curves using optimization approach. A Quantum-behaved Particle Swarm Optimization (QPSO) and Particle Swarm Optimization (PSO) are used to obtaining the Li-ion battery parameters from the manufacturer discharge curve for the electric vehicle application have been presented in [17] and [18], respectively. Whereas, in [19], a parallel Jaya algorithm is applied to estimate the Li-ion battery parameters and the simulation results showed good performance of the developed algorithm.…”

“…Whereas, in [19], a parallel Jaya algorithm is applied to estimate the Li-ion battery parameters and the simulation results showed good performance of the developed algorithm. The specific optimization algorithms used in [17][18][19] however gives challenges to the user with limited access to the optimization codes. An easy to use guideline for such parametric optimization strategy should be developed considering widely available optimization approach.…”