Comparison between RLS-GA and RLS-PSO for Li-ion battery SOC and SOH estimation: a simulation study

Abstract

This paper proposes a new method of concurrent SOC and SOH estimation using a combination of recursive least square (RLS) algorithm and particle swarm optimization (PSO). The RLS algorithm is equipped with multiple fixed forgetting factors (MFFF) which are optimized by PSO. The performance of the hybrid RLS-PSO is compared with the similar RLS which is optimized by single objective genetic algorithms (SOGA) as well as multi-objectives genetic algorithm (MOGA). Open circuit voltage (OCV) is treated as a parameter to be estimated at the same time with internal resistance. Urban Dynamometer Driving Schedule (UDDS) is used as the input data. Simulation results show that the hybrid RLS-PSO algorithm provides little better performance than the hybrid RLS-SOGA algorithm in terms of mean square error (MSE) and a number of iteration. On the other hand, MOGA provides Pareto front containing optimum solutions where a specific solution can be selected to have OCV MSE performance as good as PSO.

A Novel Variable Forgetting Factor Recursive Least Square Algorithm to Improve the Anti-Interference Ability of Battery Model Parameters Identification

“…Moreover, this variable forgetting factors can be used to other of RLS observers such as vector type RLS with multiple forgetting factors, and other equivalent circuit models of battery. For further research, this VFF-RLS algorithm can be optimized by evolution algorithm such as genetic algorithm and particle swarm optimization [16] and a quantity of experiment data of different working condition for higher adaptive ability in different working condition.…”

“…Moreover, the RLS parameters decoupling ability of this vector type RLS may not as satisfying as assumed, because the decoupled parameters are coupled during both the update gain vector calculation in vector type RLS and reversed solution from RLS parameters to battery's equivalent circuit model parameters. These multiple forgetting factors were optimized by Rozaqi et al [16], which also conducted comparison between the optimized performance by particle swarm optimization, single objective genetic algorithm and multiple objective genetic algorithm. However, this optimization by evolution algorithms are developed for fixed forgetting factors, instead of optimizing variable adaptive forgetting factors for more working conditions that required more training with more data.…”

Aging Study of In-Use Lithium-Ion Battery Packs to Predict End of Life Using Black Box Model

“…Among these optimization algorithms, the two most used in battery estimation are PSO and GA. Adaptation of PSO is observed in various works to estimate the state of charge and the state of health of batteries but it strongly depends on population size and quality of the data selected at the beginning, and its solution takes longer to converge and is voluminous to implement on a real-time basis [32] Hence, GA is chosen for this model, as its implementation for this problem is simpler and does not require long computation times. In addition, the PSO can become stuck in optimizations of multidimensional problems, by becoming blocked in a local optimum.…”

A Review of Battery State of Health Estimation Methods: Hybrid Electric Vehicle Challenges

“…Other widely used algorithms in adaptive filtering are the Least Square-based ones [47][48][49][50][51]. A lot of attention has been given recently to these algorithms and especially to the Recursive Least Square (RLS) due to its simple implementation and accuracy.…”