Carbon impression and the growing reliance on fossil fuels are two unique concerns for world emission regulatory agencies. These issues have placed electric vehicles (EVs) powered by lithium-ion batteries (LIBs) on the forefront as alternative vehicles. The LIB has noticeable features, including high energy and power density, compared with other accessible electrochemical energy storage systems. However, LIB is exceedingly nonlinear and dynamic; therefore, it generally requires an accurate online state-of-charge (SOC) estimation algorithm for real-time applications. Accurate battery modelling is an essential and primary requirement of online SOC estimation to simulate the dynamics. In this paper, different modelling methods suitable for online SOC estimation are discussed, and four groups of available online SOC estimation approaches are reviewed. After the general survey, the study explores the available Kalman filter (KF) family algorithms suitable for model-based online SOC estimation. The mathematical process and limitations of different KF family algorithms are analysed in depth and discussed. Moreover, challenging steps in the implementation of KF family algorithms in model-based online SOC estimation processes, such as selection of battery model, initial SOC and filter tuning, are elaborated for the efficient development of a battery management system, especially for EV application. The on-going research is propelled by KF-based online SOC estimation approaches distinctly emphasised through reviewing various studies for future research progression.

Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries

Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium-ion (Li-ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart battery management system (BMS) is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life. The accurate estimation of the state of charge (SOC) of a Li-ion battery is a very challenging task because the Li-ion battery is a highly time variant, non-linear, and complex electrochemical system. This paper explains the workings of a Li-ion battery, provides the main features of a smart BMS, and comprehensively reviews its SOC estimation methods. These SOC estimation methods have been classified into four main categories depending on their nature. A critical explanation, including their merits, limitations, and their estimation errors from other studies, is provided. Some recommendations depending on the development of technology are suggested to improve the online estimation.

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

Joint estimation of battery state-of-charge and state-of-health based on a simplified pseudo-two-dimensional model

Co-estimation of model parameters and state-of-charge for lithium-ion batteries with recursive restricted total least squares and unscented Kalman filter

With the development of electric vehicle (EV) technologies, efficient and reliable use of rechargeable batteries (RBs) has become the key subject for automotive manufacturers. Among RBs, lithium-ion battery technology is the most prominent about high specific energy and specific power. The lithium-ion battery pack consists of battery cells with low terminal voltage connected in series to meet the voltage requirement of the EV system. However, the useable capacity of the battery pack is restricted by the low charge cell among the string. The manufacturing inconsistency and different operating conditions of each cell cause the charge imbalances in the battery pack. These imbalances lead to the reduction of the useable capacity, acceleration of cell degradation, and, more importantly, serious safety hazards like overcharging. In all EVs and hybrid electric vehicles (HEVs) using lithium-ion battery systems, the cell balancing controller is an essential task which managed by the battery management system (BMS) to improve battery life cycle and safety. This paper presents a comprehensive overview of the DC-DC converter-based battery balancing system because of the impactful contribution to the charge balancing control and the design of balancing control algorithms. Moreover, other BMS functions such as state of charge (SOC) estimation, which affects balancing performance, are handled in this review.

A comprehensive overview of the dc-dc converter-based battery charge balancing methods in electric vehicles

State of charge (SOC) estimation is the core of any battery management system. Most closed-loop SOC estimation algorithms are based on the equivalent circuit model with fixed parameters. However, the parameters of the equivalent circuit model will change as temperature or SOC changes, resulting in reduced SOC estimation accuracy. In this paper, two SOC estimation algorithms with online parameter identification are proposed to solve this problem based on forgetting factor recursive least squares (FFRLS) and nonlinear Kalman filter. The parameters of a Thevenin model are constantly updated by FFRLS. The nonlinear Kalman filter is used to perform the recursive operation to estimate SOC. Experiments in variable temperature environments verify the effectiveness of the proposed algorithms. A combination of four driving cycles is loaded on lithium-ion batteries to test the adaptability of the approaches to different working conditions. Under certain conditions, the average error of the SOC estimation dropped from 5.6% to 1.1% after adding the online parameters identification, showing that the estimation accuracy of proposed algorithms is greatly improved. Besides, simulated measurement noise is added to the test data to prove the robustness of the algorithms.

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Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Forgetting Factor Recursive Least Squares and Nonlinear Kalman Filter

Battery sorting is an important process in the production of lithium battery module and battery pack for electric vehicles (EVs). Accurate battery sorting can ensure good consistency of batteries for grouping. This study investigates the mechanism of inconsistency of battery packs and process of battery sorting on the lithium-ion battery module production line. Combined with the static and dynamic characteristics of lithium-ion batteries, the battery parameters on the production line that can be used as a sorting basis are analyzed, and the parameters of battery mass, volume, resistance, voltage, charge/discharge capacity and impedance characteristics are measured. The data of batteries are processed by the principal component analysis (PCA) method in statistics, and after analysis, the parameters of batteries are obtained. Principal components are used as sorting variables, and the self-organizing map (SOM) neural network is carried out to cluster the batteries. Group experiments are carried out on the separated batteries, and state of charge (SOC) consistency of the batteries is achieved to verify that the sorting algorithm and sorting result is accurate.

https://www.mdpi.com/1996-1073/12/15/2980/pdf

Using Self Organizing Maps to Achieve Lithium-Ion Battery Cells Multi-Parameter Sorting Based on Principle Components Analysis

The state of charge (SOC) and the state of health (SOH) are the two most important indexes of batteries. However, they are not measurable with transducers and must be estimated with mathematical algorithms. A precise model and accurate available battery capacity are crucial to the estimation results. An improved speed adaptive velocity particle swarm optimization algorithm (SAVPSO) based on the Thevenin model is used for online parameter identification, which is used with an unscented Kalman filter (UKF) to estimate the SOC. In order to achieve the cyclic update of the SOH, the concept of degree of polarization (DOP) is proposed. The cyclic update of available capacity is thus obtainable to conversely promote the estimation accuracy of the SOC. The estimation experiments in the whole aging process of batteries show that the proposed method can enhance the SOC estimation accuracy in the full battery life cycle with the cyclic update of the SOH, even in cases of operating aged batteries and under complex operating conditions.

https://www.mdpi.com/1996-1073/12/15/2939/pdf

Online Parameter Identification and Joint Estimation of the State of Charge and the State of Health of Lithium-Ion Batteries Considering the Degree of Polarization

Guanghao Chen

Papers

Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Forgetting Factor Recursive Least Squares and Nonlinear Kalman Filter

Using Self Organizing Maps to Achieve Lithium-Ion Battery Cells Multi-Parameter Sorting Based on Principle Components Analysis

Online Parameter Identification and Joint Estimation of the State of Charge and the State of Health of Lithium-Ion Batteries Considering the Degree of Polarization