Sensorless Battery Internal Temperature Estimation using a Kalman Filter with Impedance Measurement

This study presents a method of estimating battery cell core and surface temperature using a thermal model coupled with electrical impedance measurement, rather than using direct surface temperature measurements. This is advantageous over previous methods of estimating temperature from impedance, which only estimate the average internal temperature. The performance of the method is demonstrated experimentally on a 2.3 Ah lithium-ion iron phosphate cell fitted with surface and core thermocouples for validation. An extended Kalman filter, consisting of a reduced order thermal model coupled with current, voltage and impedance measurements, is shown to accurately predict core and surface temperatures for a current excitation profile based on a vehicle drive cycle. A dual extended Kalman filter (DEKF) based on the same thermal model and impedance measurement input is capable of estimating the convection coefficient at the cell surface when the latter is unknown. The performance of the DEKF using impedance as the measurement input is comparable to an equivalent dual Kalman filter using a conventional surface temperature sensor as measurement input.Comment: 10 pages, 9 figures, accepted for publication in IEEE Transactions on Sustainable Energy, 201

Methodology and Guidelines for Designing Flexible BMS in Automotive Applications

The fragile characteristics of Li-ion batteries lead to the need of battery management system (BMS) to carefully supervise them during the operation. Since there are so many variations in battery configurations, the BMS usually must undergo many iterations of the development cycle, which take a long time to optimize and finalize the design. Previously, many works adopted the idea of modularized BMS to address these issues, but they still have some skeptical issues such as measurement approaches or difficulties in reconfiguration. This paper presents a guideline on the crucial aspects of flexible BMS designs for automotive applications, which aims to reduce time and effort for developing a new BMS for automotive battery pack. The guideline covers some crucial aspects pertaining the automotive BMS hardware implementation, SOC estimation algorithm and its computational performance based on Extended Kalman Filter (EKF) and Luenberger Observer (LO) with 3 levels of Electrochemical model (ECM). All of the tests were carried out in a small-scale microcontroller. It was found that 2-RC ECM gives the best trade-off between SOC estimation accuracy and computational time. While the 3-RC ECM provides 9.5% and 31% higher accuracy than the 2-RC and 1-RC ECM, respectively, but taking 88% and 240% higher computational time than the latter two cases. The optimal speed of the observer poles of LO algorithm are suggested to be in the range of 2-5 times faster than the system poles, which makes the convergence speed to be comparable to the EKF algorithm but is still able to keep the SOC estimation error in the range of 3-5%. These results can be used to make a trade-off between estimation accuracy and computational time, to select the optimal SOC estimation algorithm for onboard BMSs

A systematic review of lumped-parameter equivalent circuit models for real-time estimation of lithium-ion battery states

This paper presents a systematic review for the most commonly used lumped-parameter equivalent circuit model structures in lithium-ion battery energy storage applications. These models include the Combined model, Rint model, two hysteresis models, Randles' model, a modified Randles' model and two resistor-capacitor (RC) network models with and without hysteresis included. Two variations of the lithium-ion cell chemistry, namely the lithium-ion iron phosphate (LiFePO4) and lithium nickel-manganese-cobalt oxide (LiNMC) are used for testing purposes. The model parameters and states are recursively estimated using a nonlinear system identification technique based on the dual Extended Kalman Filter (dual-EKF) algorithm. The dynamic performance of the model structures are verified using the results obtained from a self-designed pulsed-current test and an electric vehicle (EV) drive cycle based on the New European Drive Cycle (NEDC) profile over a range of operating temperatures. Analysis on the ten model structures are conducted with respect to state-of-charge (SOC) and state-of-power (SOP) estimation with erroneous initial conditions. Comparatively, both RC model structures provide the best dynamic performance, with an outstanding SOC estimation accuracy. For those cell chemistries with large inherent hysteresis levels (e.g. LiFePO4), the RC model with only one time constant is combined with a dynamic hysteresis model to further enhance the performance of the SOC estimator

Advanced Battery Technologies: New Applications and Management Systems

In recent years, lithium-ion batteries (LIBs) have been increasingly contributing to the development of novel engineering systems with energy storage requirements. LIBs are playing an essential role in our society, as they are being used in a wide variety of applications, ranging from consumer electronics, electric mobility, renewable energy storage, biomedical applications, or aerospace systems. Despite the remarkable achievements and applicability of LIBs, there are several features within this technology that require further research and improvements. In this book, a collection of 10 original research papers addresses some of those key features, including: battery testing methodologies, state of charge and state of health monitoring, and system-level power electronics applications. One key aspect to emphasize when it comes to this book is the multidisciplinary nature of the selected papers. The presented research was developed at university departments, institutes and organizations of different disciplines, including Electrical Engineering, Control Engineering, Computer Science or Material Science, to name a few examples. The overall result is a book that represents a coherent collection of multidisciplinary works within the prominent field of LIBs

Review on Battery State Estimation and Management Solutions for Next-Generation Connected Vehicles

The transport sector is tackling the challenge of reducing vehicle pollutant emissions and carbon footprints by means of a shift to electrified powertrains, i.e., battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). However, electrified vehicles pose new issues associated with the design and energy management for the efficient use of onboard energy storage systems (ESSs). Thus, strong attention should be devoted to ensuring the safety and efficient operation of the ESSs. In this framework, a dedicated battery management system (BMS) is required to contemporaneously optimize the battery’s state of charge (SoC) and to increase the battery’s lifespan through tight control of its state of health (SoH). Despite the advancements in the modern onboard BMS, more detailed data-driven algorithms for SoC, SoH, and fault diagnosis cannot be implemented due to limited computing capabilities. To overcome such limitations, the conceptualization and/or implementation of BMS in-cloud applications are under investigation. The present study hence aims to produce a new and comprehensive review of the advancements in battery management solutions in terms of functionality, usability, and drawbacks, with specific attention to cloud-based BMS solutions as well as SoC and SoH prediction and estimation. Current gaps and challenges are addressed considering V2X connectivity to fully exploit the latest cloud-based solutions

The parameter update of Lithium-ion battery by the RSL algorithm for the SOC estimation under extended kalman filter (EKF-RLS)

The lithium-ion battery is the key power source of an electric vehicle. The cornerstone of safe transportation vehicles is reliable real-time state of charge (SOC) information. Since batteries are the primary form of energy storage in electric vehicles (EVs) and the smart grid, estimation of the state of charge is a critical need for batteries. The SOC estimate approach is considered to be precise and simple to apply for such applications. In this paper, After studying a battery model with an appropriate resistor-capacitor (RC) circuit, A lookup table derived from experimental studies describes the nonlinear connection between the Open Circuit Voltage Voc and the the state of charge. However, if temperature or SOC varies, the equivalent circuit model's characteristics will vary, decreasing the accuracy of SOC calculation. The recursive least squares (RLS) and nonlinear Extended Kalman filters are used in this research to offer a charge estimate technique with online parameter identification to handle this problem. RLS dynamically updates the Thevenin model's parameters. In order to improve the precision of SOC prediction under charge and discharge settings, we presented a regression least-squares-extended Kalman filter (RLS-EKF) estimation approach in this study. The objective of this research is to ensure the updating of the battery parameters and to evaluate the influence of this improvement on the convergence of the state of charge towards the real value. The simulation results suggest that the RLS EKF estimation technique, which is based on precise modeling, may greatly increase SOC estimation accuracy

The parameter update of Lithium-ion battery by the RSL algorithm for the SOC estimation under extended kalman filter (EKF-RLS)

The lithium-ion battery is the key power source of an electric vehicle. The cornerstone of safe transportation vehicles is reliable real-time state of charge (SOC) information. Since batteries are the primary form of energy storage in electric vehicles (EVs) and the smart grid, estimation of the state of charge is a critical need for batteries. The SOC estimate approach is considered to be precise and simple to apply for such applications. In this paper, After studying a battery model with an appropriate resistor-capacitor (RC) circuit, A lookup table derived from experimental studies describes the nonlinear connection between the Open Circuit Voltage Voc and the the state of charge. However, if temperature or SOC varies, the equivalent circuit model's characteristics will vary, decreasing the accuracy of SOC calculation. The recursive least squares (RLS) and nonlinear Extended Kalman filters are used in this research to offer a charge estimate technique with online parameter identification to handle this problem. RLS dynamically updates the Thevenin model's parameters. In order to improve the precision of SOC prediction under charge and discharge settings, we presented a regression least-squares-extended Kalman filter (RLS-EKF) estimation approach in this study. The objective of this research is to ensure the updating of the battery parameters and to evaluate the influence of this improvement on the convergence of the state of charge towards the real value. The simulation results suggest that the RLS EKF estimation technique, which is based on precise modeling, may greatly increase SOC estimation accuracy