Abstract

The model parameters of the lithium-ion battery are of great importance to model-based battery state estimation methods. The fact that parameters change in different rates with operation temperature, state of charge (SOC), state of health (SOH) and other factors calls for an online parameter identification algorithm that can track different dynamic characters of the parameters. In this paper, a novel multiple forgetting factor recursive least square (MFFRLS) algorithm was proposed. Forgetting factors were assigned to each parameter, allowing the algorithm to capture the different dynamics of the parameters. Particle swarm optimization (PSO) was utilized to determine the optimal forgetting factors. A state of the art SOC estimator, known as the unscented Kalman filter (UKF), was combined with the online parameter identification to create an accurate estimation of SOC. The effectiveness of the proposed method was verified through a driving cycle under constant temperature and three different driving cycles under varied temperature. The single forgetting factor recursive least square (SFFRLS)-UKF and UKF with fixed parameter were also tested for comparison. The proposed MFFRLS-UKF method obtained an accurate estimation of SOC especially when the battery was running in an environment of changing temperature.

Highlights

  • Environmental crisis and fossil fuel depletion call for the implementation of new energy vehicles (NEVs) in recent years [1]

  • Inspired by the multiple forgetting factor recursive least square (MFFRLS) introduced by Francesco et al in [46], we propose a novel MFFRLS scheme in this paper for the online identification of battery parameters as:

  • This paper, novel procedure is introduced for the identification of lithium-ion beIneasily solvedaby the single forgetting factor recursive least square (SFFRLS)

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Summary

Introduction

Environmental crisis and fossil fuel depletion call for the implementation of new energy vehicles (NEVs) in recent years [1]. An algorithm that can update model parameters online according to the measurable variables is preferable to improve the performance of SOC estimation. Tang et al [24] used a model migration method to compensate model uncertainties caused by SOC, SOH, and temperature He et al [25] proposed a SOC and internal resistance joint estimation algorithm based on UKF. The parameters of the battery have different dynamic characters To address this conflicting problem, MFFRLS algorithms have been introduced. UKF is utilized to estimate the battery SOC track the battery dynamics in situations where the ambient temperature of the battery changes and combined with the online parameter identification scheme. Under different temperature variations indicates that the proposed MFFRLS-UKF algorithm estimates.

Battery Modeling and State of Charge Estimation Scheme
Nonlinear Kalman Filter Based SOC Estimation
A Novel Multiple Forgetting Factor Recursive Least Square Method
Single Forgetting Factoc Recursive Least Square Algorithm
Introducing the Multiple Forgetting Factors Scheme
The Inplementation of MFFRLS on Batteriey Parameter Identification
The Optimization of Forgeeting Factors
A of the the Test
Battery Characterization
NEDC Test under Constant Temperature
Conclusions
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