Internal Resistance Estimation Research Articles

Enhanced energy extraction from wind driven PMSG using digital twin model of battery charging system

The pressing need to meet sustainability and climate objectives has prompted a call for expanding the use of renewable energy sources, improving the intelligence and flexibility of electricity grids, and increasing the adoption of electric vehicles and other electricity-powered products. In light of their numerous benefits, widespread electrification and digital technologies can form the basis of energy and climate policy. This research presents an innovative biased transformer and digital twin battery model to support the wind energy based electric vehicle battery charging system. During periods of low and moderate wind, the biased transformer is utilized to increase the voltage for charging electric vehicles. A digital twin of the battery system creates a window into battery charging and aging levels by evaluating the data gathered based on internal resistance. Furthermore, the State of Charge estimation model and different internal resistance estimation algorithms are also exploited. A novel method for precise State of Charge (SOC) estimation in third-gen battery models is presented, integrating Collaborative Gradient Boosting and Adaptive Extended Kalman Filter (AEKF). This approach, implemented in MATLAB Simulink, combines machine learning and adaptive filtering, enhancing accuracy and adaptability for efficient battery management. The functionalities and resilience of both hardware and software of the proposed system are validated under field operation.

Estimating the Health Status of Li-ion NMC Batteries From Energy Characteristics for EV Applications

Capacity and Direct Current Internal Resistance are good health indicators for Li-ion batteries. But they cannot be measured. This work proposes to estimate these indicators from already available current and voltage measurements. The estimators are based on third-order polynomials and energy features extracted during partial discharge and different depths of discharge under a dynamic profile (World harmonized Light vehicles Test Cycles). The estimations are validated with experimental measurements obtained from cycling three Lithium-Nickel-Manganese-Cobalt-Oxide/Graphite cells at a controlled temperature. The mean relative error for the Direct Current Internal Resistance estimation is less than 5% when the depth of discharge lies between 25% and 40%. It is lower than 2% for the capacity estimation. The method is simple and suitable for embedded battery monitoring as it uses already available voltage and current measurements.

Embedded real-time fractional-order equivalent circuit model for internal resistance estimation of lithium-ion cells

This paper introduces a new on-board capable method for model-based estimation of the internal resistance of lithium-ion cells. The method relates to a fractional-order electrical Equivalent-Circuit-Model (ECM) consisting of ohmic resistances, ZARC and Warburg elements. Based on a Begin-of-Life (BOL) model parameterization by means of Electrochemical-Impedance-Spectroscopy (EIS), a subset of the model parameters is tracked offline over cell aging to generate End-of-Life (EOL) model parameters, using time domain measurements and a nonlinear optimization routine. The model parameters over lifetime are trained in quadratic polynomials for on-board application on the Battery-Management-System (BMS). The on-board aging estimation uses filtering algorithms and feedback control of the difference between the measured and calculated cell voltages during dynamical driving. An embedded real-time implementation, combining float- and fixed-point arithmetic, is carried out for a state-of-the-art BMS. Validation tests using a vehicle with an aged battery containing 108 cells confirm fast and stable convergence of all cell’s State-of-Health-Resistance (SOHR). The SOHR estimation convergence is reached within at most one hour of dynamical driving with an estimation error under 3.5 %. Furthermore, a proposed model-based Open-Circuit-Voltage (OCV) estimation is validated with relaxation experiments, confirming an estimation error of less than 1 mV.

Estimation of battery internal resistance using built-in self-scaling method

This paper proposes the use of the built-in self-scaling (BS) method for the effective estimation of the internal resistance of lithium-ion batteries. The internal resistance is a measure of the battery's state-of-health and an important parameter to monitor, especially in safety-critical applications such as hybrid electric vehicle applications. The BS technique works by identifying the system's impulse response and then computing the resistance from this response. This approach makes use of a prior DC gain which capitalizes on the fact that the state-of-health changes slowly with time. The BS method can be utilized on the fly in real time, is passive, and has high accuracy which is invariant with respect to the battery dynamics. Simulation results show that the BS method reduces the mean square error by factors of 32, 69 and 20 compared with the series resistance, the least squares and data pieces, and the kernel-based techniques, respectively, in the absence of hysteresis. The corresponding values in the presence of hysteresis are 42, 62 and 21, respectively. Experimental results using a lithium nickel manganese cobalt oxide battery and a dynamic current profile based on the Federal Urban Driving Schedule further confirm the superiority of the proposed BS approach.

Fast capacity and internal resistance estimation method for second-life batteries from electric vehicles

The success of second-life (SL) Li-ion batteries from electric vehicles is still conditioned by their technical and economic viability. The knowledge of the internal parameters of retired batteries at the repurposing stage is key to ensure their adequate operation and to enlarge SL lifetime. However, traditional characterization methods require long testing times and specific equipment, which result in high costs that may jeopardize the economic viability of SL. In the seek of optimizing the repurposing stage, this contribution proposes a novel fast characterization method that allows to estimate capacity and internal resistance at various state of charge for reused cells, modules and battery packs. Three estimation models are proposed. The first of them is based on measurements of AC resistance, the second on DC resistance and the third combines both resistance types. These models are validated in 506 cells, 203 modules and 3 battery packs from different Nissan Leaf vehicles. The results achieved are satisfactory, with mean absolute percentage errors (MAPE) below 2.5% at cell and module level in capacity prediction and lower than 2.4% in resistance estimation. Considering battery pack level, MAPE is below 4.2% and 1.8% in capacity and resistance estimation respectively. With the proposed method, testing times are reduced from more than one day to 2 min per cell, while energy consumption is lowered from 1.4 kWh to 1 Wh. In short, this study contributes to the reduction of repurposing procedures and costs, and ultimately to the success of SL batteries business model.

Optimized State of Charge Estimation of Lithium-Ion Battery in SMES/Battery Hybrid Energy Storage System for Electric Vehicles

With the increasing capacity of large-scale electric vehicles, it's necessary to stabilize the fluctuation of charging voltage in order to achieve improvement of lithium-ion battery lifecycle, and the hybrid energy storage system (HESS) including superconducting magnetic energy storage (SMES) and lithium-ion battery is introduced, which is significant to reduce the high frequency discharge cycles and to manage the state of charge (SOC) for batteries with an optimized integration scheme based on power grading approach. With the unscented Kalman filter (UKF) method for battery SOC estimation and the extended Kalman filter (EKF) method for battery internal resistance estimation, the approach for estimating the lithium-ion battery state through adaptive unscented Kalman filter (AUKF) is derived. The AUKF approach takes the form of iterative loop to estimate system parameters and state, so the system strategy has well adaptive characteristics. Experimental results illustrate that the SOC estimation based on the proposed optimized approach for lithium-ion battery in HESS can achieve the well robustness and fast convergence.

Thermal characteristics of ultrahigh power density lithium-ion battery

Ultrahigh power density lithium-ion batteries (LIBs) are widely applied in transportation and energy storage systems. However, the thermal characteristics of power lithium-ion batteries under high discharge rates remain unclear. In this work, a commercial lithium-ion battery with lithium titanate oxide (LTO) as the anode material is investigated under discharge rates up to 40C. The heat generation power and temperature rise ratio increase with the discharge rate. A maximum heat generation rate of 358 W is obtained under 40C discharge. Due to the limited discharge capacity with high discharge rates, the highest temperature rise appears under 25C discharge, which is 38.9 °C. The percentage of irreversible heat increases with the discharge rate, but it only accounts for 83% under 40C discharge. Furthermore, different internal resistance estimation methods are used to predict the heat generation of lithium-ion batteries. It is found that hybrid pulse power characteristic (HPPC) method is more accurate than electrochemical impedance spectroscopy (EIS) method, and heat generation in higher discharge rate can be estimated by HPPC with a shorter time scale.

Sampled-Data Observer for Estimating the State of Charge, State of Health, and Temperature of Batteries

This paper deals with the problem of sampled-data observer design to estimate the state of health (SoH), the state of charge (SoC), and the internal temperature for batteries. The observation is based on the estimation of the battery internal resistance, which is supposed to be unknown and varying with temperature. The difficulty is that the system state equation contains an unknown parameter (which represents the internal resistance) and an output-dependent term only accessible to measurement at sampling time. The proposed approach consists of several levels of estimation and calculation using non-linear observers and fitting functions exploiting experimental data. It has been shown, using theoretical analysis, simulations, and experimental results, that the proposed method gives a novel approach to estimate the quantities (SoH, SoC, and temperature) useful for the battery management system (BMS). The point is that this approach only involves the estimation of the internal resistance of the battery.

State of Charge and Lithium Manganate Batteries Internal Resistance Estimation at Low Charge/discharge rates

State of Charge and Lithium Manganate Batteries Internal Resistance Estimation at Low Charge/discharge rates

Combined internal resistance and state-of-charge estimation of lithium-ion battery based on extended state observer

Lithium-ion battery is considered as one of the most successful energy storage methods which enables the sustainability of the renewable energy systems subject to high intermittency. To avoid the permanent damage and the potential explosion, the battery state-of-charge (SOC) serves as a characteristic operational parameter that should be maintained within a safe range. However, accurate SOC estimation is challenging in the presence of uncertainties, particularly due to the problems of unknown initial SOC value and the uncertain internal resistance. To address this uncertainties, this paper critically reviews the state-of-the-art of the current SOC researches and takes actions to propose a joint SOC and internal resistance estimation algorithm in a real-time data-driven manner. Both static and dynamic experiments are carried out to identify an equivalent circuit model (ECM) of the battery dynamics. The semi-empirical model's parameters are optimized using the experimental data. Sensitivity analysis is then carried out to reveal that the internal resistance is the dominating parameter that affects the model accuracy. A conventional model-based nonlinear state observer is developed to accommodate the initial value uncertainty. To deal with the uncertain internal resistance through this state observer, internal resistance is treated as an augmented state, which is estimated together with SOC based on extended state observer (ESO). The conclusions are drawn from the experimental results in two aspects, i) a 83% performance improvement of the proposed ESO method is achieved when compared with the conventional observer without internal resistance augmentation; ii) the dynamic variation of the internal resistance is captured by the proposed method, which is shown to increase with the service time. The proposed method is able to give a simultaneous estimation of SOC and internal resistance, depicting a promising prospect in the future commercial application.

Battery Internal Resistance Estimation Using a Battery Balancing System Based on Switched Capacitors

Battery management systems (BMSs) are key components in battery storage systems in order to guarantee their safe operation and improve their performance, reliability, and efficiency. BMSs monitor critical parameters in the battery as state of charge (SOC), state of health (SOH), or temperature. Direct measure of SOC or SOH is not possible, while temperature, on the other hand, can be measured with different types of sensors. These sensors, although cost-effective, raise concerns regarding cabling, signal conditioning and acquisition systems, increasing cost, complexity, and decreasing reliability. The internal resistor of the battery has already been successfully used to estimate these parameters. BMS can also include the function of balancing (equalizing) cells of a battery pack. Among all available equalizing systems, those based on switched capacitors are interesting due to their simplicity and easy scalability. This article proposes an internal resistance (IR) estimation method for LiFePO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> batteries using signals naturally produced by a switched-capacitor equalizer (SCE). The IR will be used to estimate the battery temperature. It will be shown that the method can operate online and without interfering with the regular operation of the SCE.

Battery life estimation based on cloud data for electric vehicles

To evaluate the safety and life-cycle of electric vehicles (EVs), automobile companies usually retain the driving data of EVs on the cloud for monitoring and management. The recording period of the cloud data is generally as long as 10–30 s at present, so the dynamic driving condition of EVs is hard to be revealed with the cloud data. But the charging data are stable, which makes it possible to estimate the battery life based on the charging cloud data. Battery life estimation includes capacity estimation and internal resistance estimation. In this paper, the capacity is directly estimated by the ampere hour integral method. The estimation results are modified based on the temperature data and optimized by the Kalman filter (KF). We further propose the Fuzzy logic (FL) to control the observation noise which effectively improves the accuracy of the estimation results. Then, the battery life is predicted by the Arrhenius empirical model. The sudden changes of voltage and current in the charging data are used for estimating the internal resistance. The internal resistance prediction is achieved using a similar process to the capacity prediction. The sampling test shows that the errors of the battery life estimation method are less than 4%.

Cell quality measurement using cloud computing in case of NiCd/NiMH battery study

Currently, 3.6 V 700 mAh NiCd/NiMH battery is the most popular one used in emergency lights or other devices. It is well known that the state of health is related to the battery internal resistance and charging/discharging characteristics. However, the information needed may not be unveiled sufficiently from the manufactory although some certain specifications are provided. To provide users more accurate information, this system proposes an online battery quality measurement system based on two-pulse approach for internal resistance estimation along with long-term charging/discharging life cycle test. The measurement tasks are carried out using the microprocessor that connects with the computer via universal serial bus. Through the data acquisition system, every measured data from the microprocessor can be immediately transmitted to the data server via transmission control protocol/internet protocol. Accordingly, all testing results can be viewed online from the website, and their history record can be also tracked in the database. The experimental results verify that the proposed scheme can perform the quality measurement effectively and accurately.

Implementation of sensor based on neural networks technique to predict the PEM fuel cell hydration state

Abstract Proton exchange hydrogen fuel cells have the potential to produce clean and environmentally friendly energy. However, this technique should be adapted to technical challenges, such as performance and durability prior to its marketing. These challenges are closely related to water management. In this research, a PEM fuel cell simulation model was designed for water management. This model consisted of a voltage evolution model based on electrochemical and dynamics gases. It also comprised a model of water activity to estimate the relative humidity. Meanwhile, in identifying the PEMFC hydration state, impedance was estimated by the humidity sensor model, which was based on neural network technology for diagnosis. This model predicted the changes of behaviour in the step response of load demand and the rate of water which flowed into the fuel cell. In the case of flooding or drying, the proposed neural network sensor model was executed through the estimation of internal resistance and biasing resistance values at high and low frequencies. These frequencies corresponded to the model of PEMFC electrical performance. As a result, it was found that the efficacy of this new neural network sensor model led to improved PEMFC hydration and a controlled humid airflow in the fuel cell. Overall, it was indicated that the proposed model can be used in the control system to improve water management by adjusting the relative humidity of supplied air.

Revisiting the dual extended Kalman filter for battery state-of-charge and state-of-health estimation: A use-case life cycle analysis

Abstract One of the most discussed topics in battery research is the state-of-charge (SOC) and state-of-health (SOH) determination of traction batteries. Unfortunately, neither is directly measurable and both must be derived from sensor signals using model-based algorithms. These signals can be noisy and erroneous, leading to an inaccurate estimate and, hence, to a limitation of usable battery capacity. A popular approach tackling these difficulties is the dual extended Kalman filter (DEKF). It consists of two extended Kalman filters (EKFs), that synchronously estimate both the battery states and parameters. An analysis of the reliability of the DEKF estimation against realistically fading battery parameters is still a widely discussed subject. This work investigates the DEKF performance from a high-level perspective, involving different load dynamics and SOH stages. A numerical optimization-based approach for the crucial filter parameterization is employed. We show that the DEKF partly improves the accuracy of the SOC estimation compared to the simple EKF over battery lifetime within the operational limits of an automotive application. However, capacity and internal resistance estimation becomes unreliable and partly diverges from the reference under constant and realistic load scenarios coupled with advanced degradation. As a consequence, a downstream use of both parameters in a SOC or SOH estimation is hampered over the battery lifetime. Extensions are needed to improve reliability and enable employment in real-world applications.

High Precision Estimation of Internal Resistance of Battery

In the article we propose the new method of increasing the accuracy of the estimation of internal resistance and estimation of the residual capacitance of battery, taking into account the duality of the regime of battery. The estimation of the internal resistance of battery is widespread, but without research of the influence of the regime of battery. The duality of battery assumes a change in the activity of electrochemical active masses, which is examined in this work. In the article examined method of evaluating of residual capacitance of battery according to the value of internal resistance realized without presence of known model of battery. The method of determining of residual capacitance and internal resistance into one express test for the unknown battery without the extraction of the system is considered. Analysis of dependence, obtained in a research, leads the estimation of the internal resistance of battery to the point of testing. This makes it possible to reveal the connection of the point of testing with the residual capacitance.

Online estimation of internal resistance and open-circuit voltage of lithium-ion batteries in electric vehicles

State-of-charge (SoC) and state-of-health (SoH) define the amount of charge and rated capacity loss of a battery, respectively. In order to determine these two measures, open-circuit voltage (OCV) and internal resistance of the battery are indispensable parameters that are obtained with difficulty through direct measurement. The motivation of this study is to develop an online, simple, training-free, and easily implementable scheme that is capable of estimating such parameters, particularly for the lithium-ion battery in battery-powered vehicles. Based on an equivalent circuit model (ECM), the electrical performance of a battery can be formulated into state-space representation. Also, underdetermined model parameters can be arranged to appear linearly so that an adaptive control approach can be applied. An adaptation algorithm is developed by exploiting the Lyapunov-stability criteria. The OCV and internal resistance can be extracted exactly without limitations of a system input signal, such as persistent excitation (PE), enhancing the method applicability for vehicular power systems. In this study, both simulations and experiments are established to verify the capability and effectiveness of the proposed estimation scheme.

State-of-health monitoring of lithium-ion batteries in electric vehicles by on-board internal resistance estimation

For reliable and safe operation of lithium-ion batteries in electric or hybrid vehicles, diagnosis of the cell degradation is necessary. This can be achieved by monitoring the increase of the internal resistance of the battery cells over the whole lifetime of the battery. In this paper, a method to identify the internal resistance in a hybrid vehicle is presented. Therefore, a special purpose model deduced from an equivalent circuit is developed. This model contains parameters depending on the degradation of the battery cell. To achieve the required robustness and stable results under these conditions, the method uses specific signal intervals occurring during normal operation of the battery in a hybrid vehicle. This identification signal has a defined timespan and occurs regularly. The identification is done on vehicle measurement data of terminal cell voltage and current collected with a usual vehicle sampling rate. Using the adapted internal resistance value in the model, a degradation index is calculated by compensating other influences, e.g. battery temperature. This task is the main challenge, as the impact of the temperature on the resistance, for example, is one order of magnitude higher than the influence of the degradation for the investigated lithium-ion cell. The developed estimation and monitoring method is validated with measurement data from single cells and shows good results and very low computational effort.