Adaptive Exploration Harmony Search for Effective Parameter Estimation in an Electrochemical Lithium-Ion Battery Model

Abstract

Electrochemical models of lithium-ion batteries are derived according to the laws of physics; therefore, the parameters represent specific physical quantities such as lithium diffusivities, particle volume fractions, and ion intercalation rates. It is important to estimate these parameters to identify the internal states of a lithium-ion battery for efficient and safe management. Until now, parameter estimation algorithms for electrochemical lithium-ion battery models have been developed without considering the unequal identifiability among the target parameters. Thus, it is highly likely that existing algorithms exhibit inefficient exploration and lead to a slow convergence rate and even large parameter estimation error. For more accurate parameter estimation of an electrochemical lithium-ion battery model, we propose a new adaptive exploration harmony search (AEHS) scheme that provides a wide search space for a longer period of time when estimating parameters with low identifiability. The proposed algorithm is based on improved harmony search; its bandwidth parameters for determining the level of exploration are adjusted according to the individual and joint variabilities computed from the distributions of previously estimated parameters. Such adaptive bandwidth parameters can reduce inefficient exploration and enable fast convergence, allowing exploration that achieves global optimality. Simulation results show that the proposed parameter estimation algorithm produces the highest convergence rate and the smallest parameter estimation error compared with existing schemes. The performance of the proposed scheme is also validated using real data generated from experiments. INDEX TERMS Adaptive exploration harmony search, electrochemical model, lithium-ion battery, meta-heuristic algorithm, parameter estimation, parameter identifiability.

A Simplified Extension of Physics-Based Single Particle Model for Dynamic Discharge Current

“…Otherwise, measuring the surface thermal conductivity, inner thermal conductivity and specific heat need special equipment which is beyond the ability of commonly used experiment bench, so these parameters are also needed to be estimated in this paper. It is an effective manner that using multi-parameter optimization methods like particle swarm optimization [38], harmony search [9] and genetic algorithm (GA) [39] to estimate battery parameters. By balancing the precision and computational cost, GA has been adopted to estimate these parameters with dynamic…”

“…Electrochemical model accurately describes the discharge behavior of lithium-ion battery through analyzing physical phenomena such as the transport and diffusion of charges and lithium-ions [9]. The pseudo-two-dimensional (P2D) model describes the lithium salt transport phenomenon in the electrolyte and solid phases, and captures charge transfer reaction in the positive and negative porous electrodes [10].…”

Multi‐phase method of estimation and adaptation of parameters of electrical battery models

“…Although the authors have obtained good results with the method, they admit that it has a high computational cost, but they do not inform the processing time required by the method. Chun et al 49 use adaptive exploration harmony search to estimate parameters of an electrochemical Li-ion battery model, with a computational cost of 10.28 hours of processing time, a time similar to those obtained for the GA and PSO methods.…”

Real-Time Parameter Estimation of an Electrochemical Lithium-Ion Battery Model Using a Long Short-Term Memory Network

“…However, since the electrochemical lithium-ion battery model is highly nonlinear, the Jacobianbased parameter estimation algorithms are likely to converge to the local optima and hence show the poor parameter estimation accuracy [9], [10]. Empirical studies have also been conducted to estimate the parameters of the electrochemical lithium-ion battery model using meta-heuristic algorithms such as the genetic algorithm, particle swarm optimization, and harmony search [11]- [16]. Since the meta-heuristic algorithms try to obtain the globally optimal solution through random searches, they are less likely to trap into the local optima.…”

“…Since the meta-heuristic algorithms try to obtain the globally optimal solution through random searches, they are less likely to trap into the local optima. However, such algorithms require many iterations for convergence, and thus, considerable time is required to estimate the parameters of the electrochemical lithium-ion battery model [16], [17]. In order to observe the internal states of the actual battery and ensure its safe and healthy performance, a more sophisticated real-time parameter estimation algorithm is needed.…”