Deterioration diagnosis of lithium-ion battery based-on parameter estimation of electrochemical model using particle swarm optimization

2018 ◽  
Author(s):  
Takuro Shindo ◽  
Teruyoshi Sadahiro ◽  
Shoshiro Hatakeyama ◽  
Masami Iwase
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Lithium Ion Battery ◽  
Particle Swarm ◽  
Lithium Ion ◽  
Swarm Optimization ◽  
Electrochemical Model

scholarly journals Real-Time State-of-Charge Estimation via Particle Swarm Optimization on a Lithium-Ion Electrochemical Cell Model

Batteries ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 4 ◽  
Author(s):  
Arun Chandra Shekar ◽  
Sohel Anwar
Keyword(s):  
Particle Swarm Optimization ◽  
Single Cell ◽  
Real Time ◽  
Cell Model ◽  
Particle Swarm ◽  
Lithium Ion ◽  
State Of Charge ◽  
Swarm Optimization ◽  
Soc Estimation ◽  
Electrochemical Model

With the ever-increasing usage of lithium-ion batteries, especially in transportation applications, accurate estimation of battery state of charge (SOC) is of paramount importance. A majority of the current SOC estimation methods rely on data collected and calibrated offline, which could lead to inaccuracies in SOC estimation under different operating conditions or when the battery ages. This paper presents a novel real-time SOC estimation of a lithium-ion battery by applying the particle swarm optimization (PSO) method to a detailed electrochemical model of a single cell. This work also optimizes both the single-cell model and PSO algorithm so that the developed algorithm can run on an embedded hardware with reasonable utilization of central processing unit (CPU) and memory resources while estimating the SOC with reasonable accuracy. A modular single-cell electrochemical model, as well as the proposed constrained PSO-based SOC estimation algorithm, was developed in Simulink©, and its performance was theoretically verified in simulation. Experimental data were collected for healthy and aged Li-ion battery cells in order to validate the proposed algorithm. Both simulation and experimental results demonstrate that the developed algorithm is able to accurately estimate the battery SOC for 1C charge and 1C discharge operations for both healthy and aged cells.

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