Lithium-ion Battery Management System Research Articles

Experimental investigation of thermal and strain management for lithium-ion battery pack in heat pipe cooling

Thermal and strain management is required for a considerate lithium-ion battery management system (BMS) to depress the operating temperature and strain. In this paper, non-destructive temperature equipment and strain gauges are used simultaneously to monitor the temperature and strain of 18650 lithium-ion battery pack with a heat pipe cooling device (HPCD) which is used to depress temperature. At the steady-state and dynamic-state discharge processes, the temperature of pack is dramatically different with cooling device or not, and the optimal operating temperature is kept when HPCD is embedded in the center of pack with forced convection. The strain decreases in discharge progress after the pack is equipped with HPCD and has the same trend of temperature change in charge-discharge cycle process. The battery management system of temperature and strain is also effective during multiple charge-discharge processes. Furthermore, this system has the superiority of low power consumption.

A New Method for State of Charge Estimation of Lithium-Ion Batteries Using Square Root Cubature Kalman Filter

State of charge (SOC) is a key parameter for lithium-ion battery management systems. The square root cubature Kalman filter (SRCKF) algorithm has been developed to estimate the SOC of batteries. SRCKF calculates 2n points that have the same weights according to cubature transform to approximate the mean of state variables. After these points are propagated by nonlinear functions, the mean and the variance of the capture can achieve third-order precision of the real values of the nonlinear functions. SRCKF directly propagates and updates the square root of the state covariance matrix in the form of Cholesky decomposition, guarantees the nonnegative quality of the covariance matrix, and avoids the divergence of the filter. Simulink models and the test bench of extended Kalman filter (EKF), Unscented Kalman filter (UKF), cubature Kalman filter (CKF) and SRCKF are built. Three experiments have been carried out to evaluate the performances of the proposed methods. The results of the comparison of accuracy, robustness, and convergence rate with EKF, UKF, CKF and SRCKF are presented. Compared with the traditional EKF, UKF and CKF algorithms, the SRCKF algorithm is found to yield better SOC estimation accuracy, higher robustness and better convergence rate.

Lithium-Ion Battery Management System for Electric Vehicles

The invention relates to a battery management system, in particular to a lithium ion battery management system of an electric bicycle. The lithium ion battery management system of the electric bicycle comprises a lithium ion battery protection chip, a battery charging historical record and communication module, a panel liquid crystal display (LCD) display module, a PC display module and a micro control unit. The battery charging historical record and communication module comprises a battery electric quantity metering chip which is communicated with the external world through the micro control unit, data recorded on the battery electric quantity metering chip are read through an I2C bus protocol and displayed on the panel LCD display module, and the battery electric quantity metering chip is connected with a lithium ion battery pack through the lithium ion battery protection chip. Compared with the prior art, the lithium ion battery protection chip, the battery charging historical record and communication module, and the micro control unit are adopted to achieve functions of reliable hardware protection, accurate electric quantity calculation, stable communication and comprehensive charging recording of the lithium ion battery pack, and cost for achieving the functions is low.

Lithium-Ion Battery Management System: A Lifecycle Evaluation Model for the Use in the Development of Electric Vehicles

The use of Lithium-ion batteries in the automobile sector has expanded drastically in the recent years. The foreseen increment of lithium to power electric and hybrid electric vehicles has provoked specialists to analyze the long term credibility of lithium as a transportation asset. To give a better picture of future accessibility, this paper exhibits a life cycle model for the key procedures and materials associated with the electric vehicle lithium-ion battery life cycle, on a worldwide scale. This model tracks the flow of lithium and energy sources from extraction, to generation, to on road utilization, and the role of reusing and scrapping. This life cycle evaluation model is the initial phase in building up an examination model for the lithium ion battery production that would enable the policymakers to survey the future importance of lithium battery recycling, and when in time setting up a reusing foundation be made necessary.

Lithium-Ion Battery Management System: A Lifecycle Evaluation Model for the Use in the Development of Electric Vehicles

The use of Lithium-ion batteries in the automobile sector has expanded drastically in the recent years. The foreseen increment of lithium to power electric and hybrid electric vehicles has provoked specialists to analyze the long term credibility of lithium as a transportation asset. To give a better picture of future accessibility, this paper exhibits a life cycle model for the key procedures and materials associated with the electric vehicle lithium-ion battery life cycle, on a worldwide scale. This model tracks the flow of lithium and energy sources from extraction, to generation, to on road utilization, and the role of reusing and scrapping. This life cycle evaluation model is the initial phase in building up an examination model for the lithium ion battery production that would enable the policymakers to survey the future importance of lithium battery recycling, and when in time setting up a reusing foundation be made necessary.

An On-Line State of Health Estimation of Lithium-Ion Battery Using Unscented Particle Filter

As one of the key functions in lithium-ion battery management system, the state-of-health (SOH) estimation is of great significance to ensure the safe and reliable operation and reduce the maintenance cost of the battery energy storage system. Unscented particle filter (UPF) algorithm is becoming a promising method for battery state estimation since it combines the latest measurement information to give the proposal distribution which is closer to the true posterior distribution. At the same time, UPF algorithm is able to represent the uncertainty involved in the estimation results, which makes great significance for battery SOH estimation. On the other hand, it is difficult to measure the battery actual capacity in practice despite the capacity is a direct indicator of battery SOH. In this paper, an on-line health indicator (HI) is extracted from the measurable parameters while battery is working. The mapping model between the extracted HI and battery SOH is established and applied as the observation in the state-space model. An on-line estimator based on UPF algorithm is developed for battery SOH assessment. The maximum estimation error based on battery cycling test data is less than 5%. This indicates that the proposed method has a good adaptability for lithium-ion battery degradation with non-linear and non-Gaussian characteristics. Additionally, the experiments on different types of lithium-ion battery show the good robustness and applicability of this approach.

Investigating and modeling the transmission channel of a prismatic lithium-ion cell and module for powerline communication

Current lithium-ion battery management systems require a huge amount of connecting units for monitoring and controlling single cells. A sophisticated way of simplifying a complex wiring architecture is the use of a powerline communication system. By this approach the existing powerline is simultaneously used for data transmission. However, a thorough understanding of the channel characteristics in the high frequency range (1–110MHz) is necessary to be able to design and implement such a system. This work deals with the characterization of the powerline channel of <10Ah prismatic hard case Lithium-Ion cells and 10 cell modules with special respect to the interaction between single cells using impedance measurements. Based on the obtained knowledge, a circuit-based model of the transfer channel has been designed, allowing to estimate the behavior of larger battery systems. The model was also used to simulate different coupling methods to transmit and receive signals to the powerline.

A Lithium-Ion Battery Current Estimation Technique Using an Unknown Input Observer

Current consumption measurements are useful in a wide variety of applications, including power monitoring and fault detection within a lithium-ion battery management system (BMS). This measurement is typically taken using either a shunt resistor or a Hall-effect current sensor. Although both methods have achieved accurate current measurements, shunt resistors have inherent power loss and require isolation circuitry, and Hall-effect sensors are generally expensive. This paper explores a novel alternative to sensing battery current by measuring terminal voltages and cell temperatures and using an unknown input observer to estimate the battery current. An accurate model of a LiFePO4 cell is created, validated, and then used to characterize a model of the proposed current estimation technique. Finally, the current estimation technique is implemented in hardware and tested in an online BMS environment. Results show that the current estimation technique is sufficiently accurate for a variety of applications, including fault detection and power profiling.

A Multi-Functional Electrolyte for Lithium-Ion Batteries

In the current two-part study, a new thermal management system for lithium-ion batteries that utilizes multi-functional electrolyte (MFE) with volatile co-solvents is investigated. For this system to function, new electrolyte mixtures that contain high vapor pressure co-solvents are needed. In the present investigation, the characteristics of a previously unstudied high vapor pressure co-solvent HFE-7000 (65 kPa at 25°C) in an MFE (1 M LiTFSI in 1:1 HFE-7000/EMC) are examined. In part I, non-boiling electrochemical performance of the MFE is determined by conductivity, stability window on a glassy carbon electrode, half and full cell cycling with LiFePO4, Li4Ti5O12, and Cu2Sb, and impedance spectroscopy measurements. The results show that the MFE has improved stability compared to a carbonate-only electrolyte and a higher ionic conductivity than other previously investigated electrolytes with hydrofluoroether co-solvents. The MFE-containing cells also had higher impedance than cells with the carbonate-based electrolyte due to lower ionic conductivity. The cells with MFE could not form a stable passivation layer on the LiFePO4 above 3.8 V, and the full Cu2Sb/LiFePO4 cells were limited by the anode. However, in spite of higher impedance, the remaining cells with low surface tension MFE had higher capacity than for cells with the carbonate electrolyte.

Stability Analysis for Li-Ion Battery Model Parameters and State of Charge Estimation by Measurement Uncertainty Consideration

Accurate estimation of model parameters and state of charge (SoC) is crucial for the lithium-ion battery management system (BMS). In this paper, the stability of the model parameters and SoC estimation under measurement uncertainty is evaluated by three different factors: (i) sampling periods of 1/0.5/0.1 s; (ii) current sensor precisions of ±5/±50/±500 mA; and (iii) voltage sensor precisions of ±1/±2.5/±5 mV. Firstly, the numerical model stability analysis and parametric sensitivity analysis for battery model parameters are conducted under sampling frequency of 1–50 Hz. The perturbation analysis is theoretically performed of current/voltage measurement uncertainty on model parameter variation. Secondly, the impact of three different factors on the model parameters and SoC estimation was evaluated with the federal urban driving sequence (FUDS) profile. The bias correction recursive least square (CRLS) and adaptive extended Kalman filter (AEKF) algorithm were adopted to estimate the model parameters and SoC jointly. Finally, the simulation results were compared and some insightful findings were concluded. For the given battery model and parameter estimation algorithm, the sampling period, and current/voltage sampling accuracy presented a non-negligible effect on the estimation results of model parameters. This research revealed the influence of the measurement uncertainty on the model parameter estimation, which will provide the guidelines to select a reasonable sampling period and the current/voltage sensor sampling precisions in engineering applications.

Numerical analyses on optimizing a heat pipe thermal management system for lithium-ion batteries during fast charging

Thermal management is crucial for the operation of electric vehicles because lithium ion batteries are vulnerable to excessive heat generation during fast charging or other severe scenarios. In this work, an optimized heat pipe thermal management system (HPTMS) is proposed for fast charging lithium ion battery cell/pack. A numerical model is developed and comprehensively validated with experimental results. This model is then employed to investigate the thermal performance of the HPTMS under steady state and transient conditions. It is found that a cylinder vortex generator placed in front of the heat pipe condensers in the coolant stream improves the temperature uniformity. The uses of cooper heat spreaders and cooling fins greatly improve the performance of the thermal management system. Experiments and transient simulations of heat pipe thermal management system integrated with batteries prove that the improved HPTMS is capable for thermal management of batteries during fast charging. The air-cooled HPTMS is infeasible for thermal management of batteries during fast charging at the pack level due to the limitation of low specific heat capacity.

Analytical, Numerical and Experimental Determination of Thermophysical Properties of Commercial 18650 LiCoO2 Lithium-Ion Battery

Thermophysical properties of a commercial 18650 LiCoO2 lithium-ion battery were determined using several different techniques, including analytical, numerical and experimental methods. A reasonable level of consistency was observed in values for heat capacity, which were found to be 972 ± 92 J/kg-K from the analytical method, 814 ± 19 J/kg-K from the numerical technique, and 896 ± 31 J/kg-K from calorimetry. The value for radial thermal conductivity with the best correlation to reported literature values obtained in this study (0.219 ± 0.020 W/m-K) was found via the numerical technique, and the axial thermal conductivity was found to be 22.3 ± 1.3 W/m-K. Values for heat capacity and thermal conductivity were also determined for aluminum and Teflon as controls, and all thermophysical properties in this work were found to be within, or very close to, reported literature values. These properties will aid in the development of ongoing and future lithium-ion battery simulation models and battery management systems.

Design and Safety Verification of a Distributed Charge Equalizer for Modular Li-Ion Batteries

This paper presents a novel charge equalization technique seamlessly integrated into a modular Battery Management System (BMS) for lithium-ion (Li-ion) batteries. The charge equalizer is a crucial element for an effective use of a Li-ion battery consisting of many series-connected cells. We describe a fully distributed charge equalizer based on a circular balancing bus, which outperforms other recently published approaches. Its safety requirements have formally been verified using a model checker, showing that formal methods and, in particular, the Symbolic Analysis Laboratory environment, can be effective to verify the safety requirements of a BMS.

Lithium-ion Battery SoC Estimation with UKF and RTOS μCOS-II Platform

To develop a battery management system for lithium-Ion batteries used in electric vehicles, the state of charge (SoC) estimation is improved with an unscented Kalman filter (UKF) and realized with the RTOS μCOS-II platform. The Kalman filters approach is broadly used for the battery SOC estimation recently. Here, we select the UKF algorithm, for which utilizes the unsented transform to solve filtering problems and is able to accurately capture the posterior mean and covariance to 3rd order of Taylor series expansion, as a result, the SoC estimation accuracy is improved with a faster convergence ability. To further evaluate the real-time performance of the SoC estimation, a battery-in-loop platform is built and the SoC estimation is calculated with a RTOS μCOS-II platform. The analog acquisition, communication system and SoC estimation algorithms were programmed, the performance of the proposed SoC estimation with UKF algorithm was finally investigated. The battery management system with UKF algorithm and RTOS μCOS-II platform has good performance and can apply for electric vehicles.

Development of an Experimental Testbed for Research in Lithium-Ion Battery Management Systems

Advanced electrochemical batteries are becoming an integral part of a wide range of applications from household and commercial to smart grid, transportation, and aerospace applications. Among different battery technologies, lithium-ion (Li-ion) batteries are growing more and more popular due to their high energy density, high galvanic potential, low self-discharge, low weight, and the fact that they have almost no memory effect. However, one of the main obstacles facing the widespread commercialization of Li-ion batteries is the design of reliable battery management systems (BMSs). An efficient BMS ensures electrical safety during operation, while increasing battery lifetime, capacity and thermal stability. Despite the need for extensive research in this field, the majority of research conducted on Li-ion battery packs and BMS are proprietary works conducted by manufacturers. The available literature, however, provides either general descriptions or detailed analysis of individual components of the battery system, and ignores addressing details of the overall system development. This paper addresses the development of an experimental research testbed for studying Li-ion batteries and their BMS design. The testbed can be configured in a variety of cell and pack architectures, allowing for a wide range of BMS monitoring, diagnostics, and control technologies to be tested and analyzed. General considerations that should be taken into account while designing Li-ion battery systems are reviewed and different technologies and challenges commonly encountered in Li-ion battery systems are investigated. This testbed facilitates future development of more practical and improved BMS technologies with the aim of increasing the safety, reliability, and efficiency of existing Li-ion battery systems. Experimental results of initial tests performed on the system are used to demonstrate some of the capabilities of the developed research testbed. To the authors’ knowledge, this is the first work that addresses, at the same time, the practical battery system development issues along with the theoretical and technological challenges from cell to pack level.

A Design Methodology for Lithium-Ion Battery Management System and its Application to an Autonomous Underwater Vehicle

Battery management system (BMS) in autonomous underwater vehicle (AUV) not only can measure the main parameters of battery packs such as current, voltage, and temperature, but also estimate the state of charge (SOC) of battery packs. This paper proposes a broad approach for the design of battery management system. The new design can improve the cycle life and safety capability. With the model well designed, the parameters required are obtained and the SOC estimation is completed. Extended Kalman filter (EKF) was chosen to make the last estimate with the reliable battery model which was used to the non-linear system to estimate SOC and suitable for AUV applications. The experiments results prove that the data measured by battery management system have high precision and reliability. The estimated error of SOC was also small, which was better than other approaches for estimate.

Management System for Large-Capacity Stationary Lithium-Ion Batteries

not Available.

Design Considerations of a Lithium Ion Battery Management System (BMS) for the STSAT-3 Satellite

This paper introduces a lithium ion battery management system (BMS) for the STSAT-3 satellite. The specifications of a lithium ion battery unit are proposed to supply power to the satellite and the overall electrical and mechanical designs for a lithium ion battery management system are presented. The structural simulation results will be shown to confirm the behavior of both the BMS and the cells.

과학기술위성 3호의 리튬 이온 배터리 운용 시스템 개발

본 논문은 과학기술위성3호 리튬이온 배터리의 배터리 운용 시스템에 대해 소개한다. 위성에 전력을 공급하기위한 리튬이온 배터리의 요구조건과 리튬 이온 배터리의 운용 시스템의 전반적인 디자인 방법들을 설명하였다. 더 나아가 이러한 설계 내용들을 검증하기 위하여 검증 모델을 제작하여 기능 테스트들을 수행하였고 이러한 결과 들을 통해서 BMS와 사용된 리튬이온 cell의 성능을 확인하였다. This paper introduces the lithium ion battery management system for STSAT-3 satellite. The specifications of lithium ion battery unit are proposed to supply power to the satellite and the overall electrical design for lithium ion battery BMS is presented. Furthermore, the test results of battery management system are shown to verify the design.

Estimate state of charge of power lithium-ion batteries used on fuel cell hybrid vehicle with method based on extended Kalman filtering

One of the most important tasks of the power lithium-ion battery management systems used on the fuel cell hybrid vehicles (FCHVs) is to precisely estimate the current state of charge (SOC) of the batteries online. A simple physical model and a complex physical model of the battery pack used on FCHVs are proposed, which describe the dynamic behavior of the battery pack by different equivalent circuits respec- lively, and the relationships among different physic variables and state variables of the two models are expressed by the discrete-time state-space functions. Then, based on the Extended Kalman Filter and these two models, the state of charge for the battery pack is estimated by using data such as current and voltage which are sampled when the vehicle is running, and the differences between the two results estimated are also analyzed by using two different models. Our results show that, model based Kalman filter SOC estimation for batteries used on FCHVs is effective. It is also shown that estimating accuracy depends highly on the selected model, and a better model can provide a higher accuracy.