Li-ion Battery Management System Research Articles

An Efficient Li-ion Battery Management System with Lossless Charge Balancer for RUL and SoH Prediction

An Efficient Li-ion Battery Management System with Lossless Charge Balancer for RUL and SoH Prediction

Modeling of Li-ion Battery Management System for Unmanned Aerial Vehicles

Nowadays, electricity usage in aircraft has increased due to the increasing gas emission problem. This increase has created the need to store electrical energy and accelerated the trend towards batteries. Since energy storage in batteries occurs as a result of chemical reactions, problems may occur that will damage the battery group or the entire system. These problems are caused by high current, voltage and temperature, which affect the reaction rate during battery charging/discharging. A Battery Management System (BMS) is needed to prevent problems and to use the required electrical energy safely. In this study, it is aimed to meet the energy needs of the system in a controlled manner by disabling only the damaged cell in case of problems that may occur in the cells in the battery. For this purpose, a model of an Unmanned Aerial Vehicle (UAV) system was created by adding a BMS block to each cell to control the battery cells. The BYS model was realized based on the cell temperature, the SoC value of the cell and the output voltage values of the cell. The UAV system was modeled using MATLAB/Simulink software. Thanks to the proposed BMS, in case of a problem that may occur in any cell in the battery, that cell is disabled and the required energy is met through the remaining cells. It is observed from the results obtained that when the cell parameters become normal, it continues to feed the system again.

Enhanced state of charge estimation for Li-ion batteries through adaptive maximum correntropy Kalman filter with open circuit voltage correction

Due to the possible interference of non-Gaussian noise in Li-ion battery management systems, there is no guarantee of reliable accuracy when using the extended Kalman filter (EKF) algorithm for battery state of charge (SOC) estimation. A novel EKF algorithm based on the adaptive maximum correntropy criterion (AMCCEKF) is proposed to enhance the robustness of SOC estimation in the paper. The Gaussian kernel function is chosen as the cost function to reconstruct the state error variance and the measurement noise variance. And a kernel width adaptive update strategy is designed to address the constraints of fixed kernel width on SOC estimation performance. In addition, an open circuit voltage (OCV) correction strategy based on terminal voltage innovation and OCV-SOC curve gradient is designed to reduce the impact of OCV error caused by non-Gaussian noise on SOC estimation. Incorporating the AMCCEKF method and the OCV correction strategy, a novel estimation method based on the dOCV-AMCCEKF is offered to perform the SOC estimation. Simulation results under different operating conditions and temperatures show that the maximum absolute error of the dOCV-AMCCEKF method is close to 0.5%, which verifies it can reduce the SOC estimation error in a non-Gaussian noisy interference environment compared with the EKF based method.

New Energy Vehicle Power Lithium Battery Model Establishment Method and SOC Estimation Research

As the main source of power for pure electric vehicles, new energy vehicle power lithium batteries are also a key technology that restricts the development of pure electric vehicles, and can directly affect the driving performance of vehicles. A reliable and efficient management system (Battery Management System, BMS) can allow the lithium-ion battery to output its best performance stably, while ensuring that the battery has a long enough service life. State of charge (SOC) estimation plays an important role in Li-ion battery management systems. The establishment of the battery working model is an important part of the state of charge estimation. Therefore, higher requirements are put forward for the establishment of the battery model. At the same time, the error of the subsequent data processing and estimation results is smaller, and the Kalman filter is often used for processing. This paper starts from the current situation of model research and improvement, focuses on the equivalent circuit model, analyzes the advantages and disadvantages of each model, and summarizes the comparative research on the analysis and processing effect of Kalman filter. And further experiment verification of SOC estimation through battery test experiments and algorithms, use Thevenin model, use least squares method for parameter identification, and finally use Kalman filter for SOC estimation, according to the analysis of the results, the fitting accuracy is 1.69%, provided by battery research a strong theoretical foundation.

Review of the Li-Ion Battery, Thermal Management, and AI-Based Battery Management System for EV Application

With the large-scale commercialization and growing market share of electric vehicles (EVs), many studies have been dedicated to battery systems design and development. Their focus has been on higher energy efficiency, improved thermal performance and optimized multi-material battery enclosure designs. The integration of simulation-based design optimization of the battery pack and Battery Management System (BMS) is evolving and has expanded to include novelties such as artificial intelligence/machine learning (AI/ML) to improve efficiencies in design, manufacturing, and operations for their application in electric vehicles and energy storage systems. Specific to BMS, these advanced concepts enable a more accurate prediction of battery performance such as its State of Health (SOH), State of Charge (SOC), and State of Power (SOP). This study presents a comprehensive review of the latest developments and technologies in battery design, thermal management, and the application of AI in Battery Management Systems (BMS) for Electric Vehicles (EV).

A power-efficient acquisition front end for the Li-ion battery management systems

This paper presents a Coulomb sensing method-based power-efficient acquisition front-end (AFE) for Li-ion battery management systems (BMSs). The AFE, based on two self-calibrated incremental analog-to-digital converters (ADCs), measures the instant current flows in and out of the Li-ion battery, the cell voltage, and the internal and external temperature of the chip to compensate for the state of charge. A switched-capacitor CMOS bandgap reference (BGR) is proposed to obtain stable reference voltage over an operating temperature range of the BMS. A wake-up comparator indicates that a large charging and discharging current occurs while the BMS is in sleeping mode. This solution significantly reduces the obtained samples and has better power efficiency than an always-acquisition method. A prototype was fabricated in the 110-nm CMOS process, achieving a DNL of −0.85/+1.7 LSB and an INL of −3.2/+4.2 LSB. The total current consumption of the normal operation and the sleeping mode from a battery is 23 μA and 1.26 μA, respectively.

Development of a Simple Battery Management System for Cell Balancing

As the use of Lithium Ion (Li-Ion) battery is increasing, proper means of increasing the life cycle and reliability of the battery should be adopted. A discussion on the precautions to be carried out while integrating Li-Ion batteries are discussed. A Battery Management System (BMS) is developed to address the unbalance State of Charge (SoC) issue that arise in the battery pack. The proposed algorithm is simulated in MATLAB Simulink. The battery pack used in this simulation is inspired from Tesla. This paper can be used as a tutorial or as a reference for anyone getting started with Li-Ion batteries or battery management systems.

Investigation of Transport and Kinetic Nonideality in Solid Li-Ion Electrodes through Deconvolution of Electrochemical Impedance Spectra

The development of Li-ion battery management systems (BMSs) is highly dependent on high fidelity computer simulations. In traditional physics-based models (PBMs), Fick’s law coupled with the Butler–Volmer equation has been employed to describe both Li-ion diffusion and solid/liquid interfacial Li-ion intercalation/deintercalation, but this methodology makes the primary assumption that there is no nonideality caused by Li–Li interactions. Such nonideality phenomena are usually described by the activity coefficient (γ) of Li-ions in a solid solution. With the nonideality, the activity of Li-ion species is not equivalent to the concentration of Li-ions. This research has demonstrated, through the deconvolution of electrochemical impedance spectra, that significant nonideality exists in the solid active materials during charge/discharge cycles, and it leads to nonlinear variation of both transport and kinetic parameters of the electrodes. We also show that PBMs with new pre-factors derived from nonequilibrium thermodynamics of concentrated solution theory can make battery-level predictions that are consistent with EIS data. The pre-factors developed in this paper are functions of the activity coefficient of the solid phase. They show a three order-of-magnitude variation for diffusivity in the solid active material and a one-to-two order of magnitude change in the reaction rate constant at the solid/liquid interface. The results presented here could provide baseline parameters for PBMs and improve their accuracy for high-fidelity BMSs.

Highly Effective Cell Equalization in a Lithium-Ion Battery Management System

This paper proposes a highly effective voltage cell equalization method for lithium-ion (Li-ion) battery management systems (BMSs) for several applications, such as nearly zero energy buildings, microgrids, and electric vehicles. The concept of the suggested method is the proper control of the charging current of each cell of a battery stack through the suitable regulation of the gate source of each cell's parallel-connected mosfet . Thus, the mosfet can act as a controllable current source for providing the proper equalization current according to the specific needs of each battery cell. The correct voltage level of the gate-source of each mosfet is determined by a cell equalization controller through the comparison of the performance of each cell of a battery stack with the healthier cell, which is detected by an aging estimation algorithm based on the evaluation of the internal resistor and the capacity fade of each cell. Therefore, smoother and faster cell equalization is achieved compared to the conventional dissipative method. The practicality and the high effectiveness of the proposed cell equalization technique have been experimentally validated. Several experimental results are presented to demonstrate the operational improvements of the proposed cell equalization technique against the conventional one.

Relaxation-Induced Memory Effect of LiFePO4 Electrodes in Li-Ion Batteries.

In Li-ion batteries, memory effect has been found in several commercial two-phase materials as a voltage bump and a step in the (dis)charging plateau, which delays the two-phase transition and influences the estimation of the state of charge. Although memory effect has been first discovered in olivine LiFePO4, the origination and dependence are still not clear and are critical for regulating the memory effect of LiFePO4. Herein, LiFePO4 has been synthesized by a home-built spray drying instrument, of which the memory effect has been investigated in Li-ion batteries. For as-synthesized LiFePO4, the memory effect is significantly dependent on the relaxation time after phase transition. Besides, the voltage bump of memory effect is actually a delayed voltage overshooting that is overlaid at the edge of stepped (dis)charging plateau. Furthermore, we studied the kinetics of LiFePO4 electrode with electrochemical impedance spectroscopy (EIS), which shows that the memory effect is related to the electrochemical kinetics. Thereby, the underlying mechanism has been revealed in memory effect, which would guide us to optimize two-phase electrode materials and improve Li-ion battery management systems.

In Situ Stress Measurement Techniques on Li-ion Battery Electrodes: A Review

Li-ion batteries experience mechanical stress evolution due in part to Li intercalation into and de-intercalation out of the electrodes, ultimately resulting in performance degradation. In situ measurements of electrode stress can be used to analyze stress generation factors, verify mechanical deformation models, and validate degradation mechanisms. They can also be embedded in Li-ion battery management systems when stress sensors are either implanted in electrodes or attached on battery surfaces. This paper reviews in situ measurement methods of electrode stress based on optical principles, including digital image correlation, curvature measurement, and fiber optical sensors. Their experimental setups, principles, and applications are described and contrasted. This literature review summarizes the current status of these stress measurement methods for battery electrodes and discusses recent developments and trends.

On-chip Wide Range Bidirectional Current Sensor for Li-ion Battery Management System

Abstract An on-chip wide range bidirectional current sensor for monitoring Li-ion battery model 18650 is proposed in this paper. In order to detect the bidirectional current, two feedback sensing loops are employed. The feedback sensing loops generate differential sensed current signals. The output summation driver is included to convert the differential current signals to a single-ended output voltage signal and improve the driving ability. The proposed design is implemented using a typical 0.25 μm 1P3M 60V BCD process. The core area is 0.2943 mm × 0.5640 mm. Based on the post layout simulation results, the sensing current is from –1.0 A to +1.0 A. The transimpedance is simulated to be 1.407 V/A. The inaccuracy error is from −0.2010% to 0.1030%. The measurement results reveal that the linear error is −0.390% to 0.215% given a sensing current from −0.8 A to +0.8 A.

Estimate the States of Pseudo-Two-Dimensional (P2D) Reformulation Model Li-Ion Battery Management System Using Non-Linear Particle Filter (PF) Compared to Linear Kalman Filter (KF)

Today’s battery cell environment, with the steady advances in technology and limited global resources, makes it imperative to know the approximate battery potential. In order to reduce cost and extend replacement interval of lithium ion (Li-ion) battery by knowing the best estimation of the battery cell State of Charge (SOC), State of Health (SOH), and Remaining Useful Life (RUL). Previous studies have been completed to realize SOC, SOH, and RUL using Equivalent Circuit Model (ECM) with the Kalman Filter technic. ECMs results have been used in previous studies, because they are computationally faster than physical based modelling, however, ECMs do not fully account for the electrochemical material that makes up the battery in their mathematical equations or isothermal characteristics that continuously change during charging and discharging of the battery cells. Physical based battery modelling permits continuous monitoring of Li-ion cells during simple and complex isothermal changes for charging and discharging profiles. This paper examines multiple P2D Li-ion technologies using PF versus KF for a series, parallel, and series/parallel combination battery switching microcontroller based battery management system (BMS). Physical Models Physical based models are computationally complex but they have the advantage of performing comprehensive analysis on the effects of both the solid state and the liquid state of the Li-ion battery, by modelling, porous electrode theory coupled with transport phenomena, and electrochemical reactions represented by coupled nonlinear partial differential equations in one or two dimensions. Efforts by Subramanian et al. [1] focused on a physical based P2D reformulated model characterized by solid-electrolyte interface layer growth, constant current–constant voltage, and capacity fade as it related to battery degradation to determine the manner in which extreme temperature and high charging rates affect the battery “cycle-life” and “calendar life.” This paper will simulate a reformulated P2D SOC and SOH convergence based model using the governing equations of a Single Particle Model (SPM) with lithium active particles as in Northrop [2] and Prada [3]. Equations 1-3 establish the boundary condition and Butler-Volmer kinetics formula for the Li-ion battery. Eq (1), Eq (2), Eq (3) Kalman Filter Methodology The KF is a linear quadratic estimation algorithm which uses observation measurements during a given time period to achieve a greater estimation of the battery system. Equation 4 is the Kalman Filter state model; Santhanagopalan [4] and Rahimian [5] used to implement a SPM with Kalman filtering methods to estimate SOC of Li-ion cells. KF is an effective recursive prediction algorithm that works in two-steps: the prediction step (using the previous time step (k-1), measurement data, and Gaussian distributed white noise with covariance to forecast the current state) and the weighted average of the approximate next measurement. Eq (4) Particle Filter Methodology The PF is a non-linear recursive Monte Carlo algorithm which estimates the posterior density of the state variable given a set of observation variables. The recursive Bayesian filter is implemented by the sequential importance sampling (SIS) algorithm, by associating weights to random samples representing the required posterior density. The SIS features are then adapted by the sequential importance resampling (SIR) algorithm in order to complete the resampling phase. The posterior PDF approximation can be defined by equation 5 and in [5]. Eq (5) Results Figure 1 Simulated Measured (noisy), SIS, and SIR filtered battery discharge data. Figure 1 Battery Voltage Profile Future work will include multiple reformulated physical based P2D PF battery technology models to simulate SOC, SOH, and RUL of a BMS. References Subramanian, Venkat R., et al. "Mathematical Model Reformulation for Lithium-Ion Battery Simulations: Galvanostatic Boundary Conditions." ECS 156.4 (2009): A260-71. Print.Northrop, Paul W. C., et al. "Efficient Simulation and Reformulation of Lithium-Ion Battery Models for Enabling Electric Transportation." ECS 161.8 (2014): E3149-57. Print.Prada, E., et al. “Simplified Electrochemical and Thermal Model of LiFePO4-Graphite Li-Ion Batteries for Fast Charge Applications.” ECS 159.9 (2012): A1508-19. PrintSanthanagopalan, Shriram, et al. "Review of Models for Predicting the Cycling Performance of Lithium Ion Batteries." JPS 156.2 (2006): 620-8. Print.Rahimian, S.K., S. Rayman, and R.E. White. “State of Charge and Loss of Active Material Estimation of a Lithium Ion Cell under Low Earth Orbit Condition Using Kalman Filtering Approaches.” ECS (2014): A860-A872.Arulampalam, M. S., et al. "A Tutorial on Particle Filters for Online nonlinear/non-Gaussian Bayesian Tracking." Signal Processing, IEEE Transactions on 50.2 (2002): 174-88. Print. Figure 1

Health Monitoring of Li-Ion Battery Systems: A Median Expectation Diagnosis Approach (MEDA)

The operations of Li-ion battery management system (BMS) are highly dependent on installed sensors. Malfunctions in sensors could lead to a deterioration in battery performance. This paper proposed an effective health monitoring scheme using a median expectation-based diagnosis approach (MEDA). MEDA calculates the median of a possible set of values, rather than taking their weighted average as in the case of a standard expected mean operator. Furthermore, a smoother was developed to capture important patterns in the estimation. The resulting filter was first derived using an one-dimensional (1-D) system example, where the iterative convergence of median-based proposed filter was proved. Performance evaluations were subsequently conducted by analyzing real-time measurements collected from Li-ion battery cells used in hybrid electric vehicles (HEV) and plug-in HEVs (PHEV) duty cycles. Results showed that the proposed filter was more effective and less sensitive to small sample size and curves with outliers.

Using Pseudo-Two-Dimensional (P2D) Reformulation Model with a Particle Filter to Estimate State of Charge, State of Health, and Remaining Useful Life in Li-Ion Battery Management System (BMS)

Discovering the precise state of charge (SOC), state of health (SOH), and remaining useful life (RUL) while monitoring depth of discharge (DOD) for Lithium ion (Li-ion) batteries can be computational enormous. Electrical engineers using equivalent circuit models (ECM) or enhanced equivalent circuit models (EECM) have achieved an acceptable level of accuracy modelling the current, voltage, and temperature behaviors to get SOC, SOH, RUL, and DOD. ECMs and EECMs are computationally fast but do not fully account for the electrochemical material that makes up the battery in their mathematical equations or isothermal characteristics that continuously change during charging and discharging. Chemical engineers have achieved greater levels of accuracy using physical-based models over ECMs and EECMs. Unfortunately, the number of factors and variables needed to compute the system of differential algebraic equations for the nonlinear physical-based models to achieve this level of precise data is computationally too expensive for real-time systems. In today’s environment, the use of filters has provided a bridge between empirical and physical based models. This paper examines an ECM and a porous electrode pseudo two-dimensional (P2D) model using particle filter (PF) technology for a series, parallel, and series/parallel combination battery switching microcontroller based battery management system (BMS). Mathematical and Physical Models BMSs are developed to manage the charging and discharging profile, and basic knowledge of all the states that the battery topology goes through is paramount in order to design a safe and more sustainable battery system. ECMs are used in BMSs because they can model battery characteristics for a desired state using resistors and capacitors to emulate the internal battery cell resistance and capacitance behaviors. Because ECMs are linearly based mathematical equations, simulation of these models can be performed quickly. Additionally, Kalman filtering techniques (KFT) have been used extensively with ECMs to assist in the estimation of the SOC and SOH for BMSs. However, the effectiveness of ECMs and KFTs are not optimal for the non-linear behavior of batteries. Physical based models have the advantage of performing comprehensive analysis on the effects of both the solid phase and the liquid phase, but they can be computationally complex. Modelling, porous electrode theory coupled with transport phenomena, and electrochemical reactions represented by coupled nonlinear partial differential equations in one or two dimensions give physical based models an advantage over ECMs. Santhanagopalan [1] and Rahimian [2] have used the single particle model (SPM) implementing Kalman filtering methods to estimate SOC of Li-ion cells. Efforts by Subramanian et al. [3] focused on a physical based P2D reformulated model characterized by solid-electrolyte interface layer growth, constant current–constant voltage, and capacity fade as it related to battery degradation to determine the manner in which extreme temperature and high charging rates affect the battery “calendar life.” This paper will simulate a reformulated P2D SOC and SOH convergence based model using the governing equations of a SPM with lithium active particles as in Northrop [4]: Eq (1), Eq (2), Eq (3) Particle Filter Methodology The PF is an algorithm based on a recursive Monte Carlo technique of estimating the posterior density of the state variable given a set of observation variables. The sequential importance sampling (SIS) algorithm implements the recursive Bayesian filter by associating weights to random samples representing the required posterior density. The sequential importance resampling (SIR) filter adapts the SIS features and adds a resampling phase. The posterior PDF approximation can be defined as in [5]: Eq (4), Eq (5) Results Figure 1(a) shows the simulated discharge (at 1C rate) of a 1.2 Volt Li-ion battery at 100% SOC using an empirical ECM in MatLab Simulink. Figure 1(b) includes simulated measured (noisy), SIS, and SIR filtered battery discharge data. Figure 1 Battery Voltage Profile Future work will include a reformulated physical based P2D PF battery model to simulate SOC, SOH, and RUL of a BMS. References Santhanagopalan, Shriram, et al. "Review of Models for Predicting the Cycling Performance of Lithium Ion Batteries." JPS 156.2 (2006): 620-8. Print. Rahimian, S.K., S. Rayman, and R.E. White. “State of Charge and Loss of Active Material Estimation of a Lithium Ion Cell under Low Earth Orbit Condition Using Kalman Filtering Approaches.” ECS (2014): A860-A872. Subramanian, Venkat R., et al. "Mathematical Model Reformulation for Lithium-Ion Battery Simulations: Galvanostatic Boundary Conditions." ECS 156.4 (2009): A260-71. Print.Northrop, Paul W. C., et al. "Efficient Simulation and Reformulation of Lithium-Ion Battery Models for Enabling Electric Transportation." ECS 161.8 (2014): E3149-57. Print.Arulampalam, M. S., et al. "A Tutorial on Particle Filters for Online nonlinear/non-Gaussian Bayesian Tracking." Signal Processing, IEEE Transactions on 50.2 (2002): 174-88. Print. Figure 1

A method for state-of-charge estimation of Li-ion batteries based on multi-model switching strategy

The accurate state-of-charge (SOC) estimation and real-time performance are critical evaluation indexes for Li-ion battery management systems (BMS). High accuracy algorithms often take long program execution time (PET) in the resource-constrained embedded application systems, which will undoubtedly lead to the decrease of the time slots of other processes, thereby reduce the overall performance of BMS. Considering the resource optimization and the computational load balance, this paper proposes a multi-model switching SOC estimation method for Li-ion batteries. Four typical battery models are employed to build a close-loop SOC estimation system. The extended Kalman filter (EKF) method is employed to eliminate the effect of the current noise and improve the accuracy of SOC. The experiments under dynamic current conditions are conducted to verify the accuracy and real-time performance of the proposed method. The experimental results indicate that accurate estimation results and reasonable PET can be obtained by the proposed method.

Overcurrent Protection with Semiconductor Device Protection for Li-Ion Battery Management System in Electric Bicycles

This paper presents a design concept of an overcurrent protection for Li-Ion battery pack used in electric bicycles. This overcurrent protection scheme is implemented as a part of the battery management system (BMS). In addition to the conventional protection of the Li-Ion battery pack, the proposed concept extends the protection to cover also semiconductor devices in the motor converter from short-circuit fault and overloading. The protection scheme was developed based on i2t current-time protection characteristics in order to obtain interrupting characteristics in a similar way to conventional protection devices like circuit breaker or fuse. The developed protection concept was implemented by adapting the software algorithm of a high-grade battery management system platform without any hardware adaptation. The existing discharging MOSFETs are employed as the electronic circuit breaker mechanism for cutting-off the circuit. The functionality of the proposed concept was successfully validated by experimental results.

Online State-of-Charge Estimation of Li-Ion Battery Based on the Second-Order RC Model

The real-timely estimation of the SOC (state of charge) is the key technology in Li-ion battery management system. In this paper, to overcome the error of the SOC estimation of Extended Kalman filter (EKF), a new estimation method based on modified-strong tracking filter (MSTF) is applied to SOC estimation of Li-ion battery, based on the second-order RC equivalent circuit model. Experiments are made to compare the new filter with the EKF and Coulomb counting approach (Ah). The simulation results demonstrate that the new filter algorithm MSTF used in this paper has higher filtering accuracy under the same conditions.

Design of Li-Ion Battery Management System Based on Atmega16

Due to the negative effect of li-ion battery monomer inconsistent performance in electric bicycle, a low-cost intelligent li-ion BMS (battery management system) is designed based on atmega16. Multi-channel analog switch and differential amplifier are used for multi-channel data acquisition, and the equalization circuit is used for the balance of each single cell. The results show that the design can realize the intelligent management of li-ion battery.

A Li-ion Battery Management System Based on MCU and OZ8920

A management system based on MCU and OZ8920 chip for Li-ion battery series has been developed in this paper. It is successful in solving the defects in design of OZ8920 chip cascade and accomplishing I2C bus communication between MCU and OZ8920 chip. It has several significant functions in this design, which is combined with 16 series connected cells, such as charge and discharge protection, single cell voltage and temperature monitoring, cell pack balance, etc.