Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique

Battery equivalent circuit models (ECMs) are widely employed in online battery management applications. The model parameters are known to vary according to the operating conditions, such as the battery state of charge (SOC) and the ambient temperature. Therefore, online recursive ECM parameter estimation is one means that may help to improve the modelling accuracy. Because a battery system consists of both fast and slow dynamics, the classical least squares (LS) method, that estimates together all the model parameters, is known to suffer from numerical problems and poor accuracy. The aim of this paper is to overcome this problem by proposing a new decoupled weighted recursive least squares (DWRLS) method, which estimates separately the parameters of the battery fast and slow dynamics. Another issue is that, the ECM-based SOC estimator generally requires a full-order state observer, which will increase the algorithm’s complexity and the time required for the filter tuning. In this work, the battery SOC estimation is achieved based on the parameter estimation results. This circumvents the additional full-order observer, leading to a reduced complexity. An extensive simulation study is conducted to compare the proposed method against the traditional LS technique. The proposed approach is also applied to estimate the parameters of ECM where the experimental data are collected using a cylindrical 3Ah 18650-type Li ion NCA cell. Finally, both the simulation and experimental results in this study have demonstrated that the proposed DWRLS approach can improve not only the modelling accuracy but also the SOC estimation performance compared with the LS algorithm

Online battery electric circuit model estimation on continuous-time domain using linear integral filter method

Equivalent circuit models (ECMs) are widely used in battery management systems in electric vehicles and other battery energy storage systems. The battery dynamics and the model parameters vary under different working conditions, such as different temperature and state of charge (SOC) levels, and therefore online parameter identification can improve the modelling accuracy. This paper presents a novel way of online ECM parameter identification using a continuous time (CT) estimation method. The CT estimation method has several advantages over discrete time (DT) estimation methods for ECM parameter identification due to the widely separated battery dynamic modes and fast sampling. The proposed method can be used for online SOC estimation. Test data are collected using a lithium ion cell, and the experimental results show that the proposed CT method achieves better modelling accuracy compared with the conventional DT recursive least square method. The effectiveness of the proposed method for online SOC estimation is also verified on test data

Parameter estimation of the fractional-order Hammerstein–Wiener model using simplified refined instrumental variable fractional-order continuous time

© 2017 The Authors. Published by IET. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1049/iet-cta.2017.0284This study proposes a direct parameter estimation approach from observed input–output data of a stochastic single-input–single-output fractional-order continuous-time Hammerstein–Wiener model by extending a well known iterative simplified refined instrumental variable method. The method is an extension of the simplified refined instrumental variable method developed for the linear fractional-order continuous-time system, denoted. The advantage of this novel extension, compared with published methods, is that the static output non-linearity of the Wiener model part does not need to be invertible. The input and output static non-linear functions are represented by a sum of the known basis functions. The proposed approach estimates the parameters of the linear fractional-order continuous-time subsystem and the input and output static non-linear functions from the sampled input–output data by considering the system to be a multi-input–single-output linear fractional-order continuous-time model. These extra inputs represent the basis functions of the static input and output non-linearity, where the output basis functions are simulated according to the previous estimates of the fractional-order linear subsystem and the static input non-linear function at every iteration. It is also possible to estimate the classical integer-order model counterparts as a special case. Subsequently, the proposed extension to the simplified refined instrumental variable method is considered in the classical integer-order continuous-time Hammerstein–Wiener case. In this paper, a Monte Carlo simulation analysis is applied for demonstrating the performance of the proposed approach to estimate the parameters of a fractional-order Hammerstein–Wiener output model

A lumped thermal model of lithium-ion battery cells considering radiative heat transfer

Thermal management plays a critical role in battery operations to improve safety and prolong battery life, especially in high power applications such as electric vehicles. A lumped parameter (LP) battery thermal model (BTM) is usually preferred for real-time thermal management due to its simple structure and ease of implementation. Considering the time-varying model parameters (e.g., the varying convective heat dissipation coefficient under different cooling conditions), an online parameter estimation scheme is needed to improve modelling accuracy. In this paper, a new formulation of adaptive LP BTM is proposed. Unlike the conventional LP BTMs that only consider convection heat transfer, the radiative heat transfer is also considered in the proposed model to better approximate the physical heat dissipation process, which leads to an improved modelling accuracy. On the other hand, the radiative heat transfer introduces nonlinearity to the BTM and poses challenge to online parameter estimation. To tackle this problem, the simplified refined instrumental variable approach is proposed for real-time parameter estimation by reformulating the nonlinear model equations into a linear-in-the-parameter manner. Finally, test data are collected using a Li ion battery. The experimental results have verified the accuracy of the proposed BTM and the effectiveness of the proposed online parameter estimation algorithm

Parameter estimation of hybrid fractional-order Hammerstein-Wiener Box-Jenkins models using RIVCF method

This paper proposes an extension of the simplified refined instrumental variable algorithm for the parameter estimation of the stochastic single-input, single-output hybrid fractional-order continuous-time Hammerstein-Wiener Box-Jenkins model. The model parameters are directly estimated from observed input-output data with less constraints such as, that the output static nonlinearity must be invertible. The noise-free model is described by a series of an input static nonlinear sub-model, a fractional-order continuous-time linear model, and then an output static nonlinear sub-model. The two nonlinear sub-models are both given by a sum of the known basis functions. The noise process is described by a Box-Jenkins model. The proposed approach estimates the parameters of the nonlinear and linear sub-models in an iterative manner. In this paper, Monte Carlo simulation analysis shows the proposed algorithm provides accurate and fast converged estimates of the fractional-order Hammerstein-Wiener hybrid Box-Jenkins model

Estimation of battery parameters and state of charge using continuous time domain identification method

Battery equivalent circuit models (ECMs) are widely used in battery management systems (BMSs), such as in electric vehicles (EVs). The battery terminal voltage-current (VI) dynamics and the ECM parameters depend on the operating conditions, such as the state of charge (SOC) and temperature. Online parameter estimation can improve not only the modelling accuracy, but also the performance of modelbased SOC estimation, which plays a key role in BMS. This paper presents a continuous-time (CT) domain algorithm for online co-estimation of the battery ECM parameters and SOC, using the linear integral filter (LIF) method. Compared with the conventional discrete time domain least square algorithm, the proposed CT LIF technique has superior performance at capturing the battery slow dynamics, which can further improve the SOC estimation accuracy. Experimental data are collected using a Li ion battery (LIB), and the results are analyzed to verify the efficacy of the proposed algorithm