Hybrid Pulse Power Characterization Research Articles

New Additives for Li Ion Batteries

None of the battery chemistry would be possible without the component of electrolyte and additive, which is responsible for the formation of Solid Electrolyte Interface (SEI), the interface between the electrodes and the electrolyte. Studies have showed that EC would be reduced at ~ 0.7 V vs Li/Li+, and the reduction of EC forms SEI consisting of organic polymer, inorganic salts that serves as a filter for lithium ion conducting. The typical electrolyte consists of 1.0 to 1. 2 M of a lithium salt (such as LiPF6 or lithium bis(trifluoromethylsulfonyl)imide, LiTFSI) in mixed solvents of carbonates type. For example, the current prevalent electrolyte Gen 2 consists of 1.2 M LiPF6 in 30 wt % of ethylene carbonate (EC) and 70 wt % of ethyl methyl carbonate (EMC). Another vital role of the additive is to mitigate unavoidable electrolyte decomposition occurred both at anode and cathodes side during the charge/discharge process. Apparently, the quality of the SEI significantly affects the battery performance. We are reporting a series of new additives for lithium ion batteries, and the ability of the additives to mitigate the presence of HF in the electrolyte was probed by solution-state nuclear magnetic resonance (NMR) spectroscopy. A galvanostatic charge/discharge protocol cycled between 3.0-4.4V at 30°C with periodic hybrid pulse power characterization (HPPC) tests gave electrochemical information from which capacity and impedance data were derived. Investigation of the harvested electrodes using X-ray photoelectron spectroscopy (XPS) after being cycled shed light on surface films formed and transition metal dissolution.

Measurement of heat generation in a 40 Ah LiFePO4 prismatic battery using accelerating rate calorimetry

Accurate characterization of the heat generation behavior of a battery is crucial to a good design of its thermal management system. Concerning the thermal properties, much attention has been paid to small-sized batteries such as the 18650- or button-type and little information is available for large-capacity Li-ion prismatic cells under adiabatic conditions. In this work, heat generation of a commercial 40 Ah prismatic LiFePO4/C battery is evaluated using an accelerating rate calorimeter under an adiabatic condition. The battery cell is charged or discharged at an initial temperature from −12.5 to 40 °C and a current rate from 0.2C to 2C. The experiment results show that heat generated in the battery is highly dependent on its operating temperature, state of charge and current rate. Internal resistance and entropy coefficient of the battery cell are also determined by the Hybrid Pulse Power Characterization method and potentiometric method, respectively. Relationship between the internal resistance and the heat generation behavior is highlighted. Entropy coefficient and volumetric heat generation rate of the battery cell obtained in this work are compared with those of other Li-ion batteries reported in literature.

Power prediction method of lithium-ion battery for unmanned vehicles based on combined constraint intelligence algorithm

For the inaccurate data predicting problem of the actual attainable maximum charge and discharge power of the lithium-ion battery in practice for unmanned vehicles in the process of running, a combined constraint intelligence algorithm is proposed based on the result of SOC, the battery voltage and the battery current. In view of the hysteresis characteristics of lithium-ion battery presented in the charge and discharge process, a second-order RC hysteresis model was proposed in this paper. In addition, we used the cubature Kalman filter algorithm to estimate the state of charge (SOC) of the battery based on this model, which reduced both the model error and algorithmic error in the SOC estimation significantly. It is also of great importance to accurately predict the state of power (SOP). Then the result of SOC, the battery voltage and the battery current were settled as constraints to predict the actual attainable maximum charge and discharge power of the lithium-ion battery in practice. Compared with the pulse discharge/charge test method and he hybrid pulse power characterization (HPPC) method, the combined constraint intelligence algorithm proposed in this paper is of high accuracy in the process of predicting the battery power.

Reversible and irreversible heat generation of NCA/Si–C pouch cell during electrochemical energy-storage process

Abstract To meet the requirements of electronic vehicles (EVs) and hybrid electric vehicles (HEVs), the high energy density LiNi0.8Co0.15Al0.05O2 (NCA) cathode and Si–C anode have attracted more attention. Here we report the thermal behaviors of NCA/Si–C pouch cell during the charge/discharge processes at different current densities. The total heat generations are derived from the surface temperature change during electrochemical Li+ insertion/extraction in adiabatic surrounding. The reversible heat is determined by the entropic coefficients, which are related with open-circuit voltage at different temperatures; while the irreversible heat is determined by the internal resistance, which can be obtained via V–I characteristic, electrochemical impedance spectroscopy and hybrid pulse power characterization (HPPC). During the electrochemical process, the reversible heat contributes less than 10% to total heat generation; and the heat generated in charge process is less than that in discharge process. The results of thermal behaviors analyses are conducive to understanding the safety management and paving the way for building a reliable thermal model of high energy density lithium ion battery.

A study of the influence of measurement timescale on internal resistance characterisation methodologies for lithium-ion cells

The power capability of a lithium ion battery is governed by its resistance, which changes with battery state such as temperature, state of charge, and state of health. Characterizing resistance, therefore, is integral in defining battery operational boundaries, estimating its performance and tracking its state of health. There are many techniques that have been employed for estimating the resistance of a battery, these include: using DC pulse current signals such as pulse power tests or Hybrid Pulse Power Characterization (HPPC) tests; using AC current signals, i.e., electrochemical impedance spectroscopy (EIS) and using pulse-multisine measurements. From existing literature, these techniques are perceived to yield differing values of resistance. In this work, we apply these techniques to 20 Ah LiFePO4/C6 pouch cells and use the results to compare the techniques. The results indicate that the computed resistance is strongly dependent on the timescales of the technique employed and that when timescales match, the resistances derived via different techniques align. Furthermore, given that EIS is a perturbative characterisation technique, employing a spectrum of perturbation frequencies, we show that the resistance estimated from any technique can be identified – to a high level of confidence – from EIS by matching their timescales.

An extended single particle model-based parameter identification scheme for lithium-ion cells

The accurate modeling and parameter identification of lithium-ion battery are of great significance in real-time control and high-performance operation for advanced battery management system (BMS) in electrified vehicles (EVs). However, it is difficult to obtain the information about the interior state inside battery, because it cannot be directly measured by some electric devices. In order to accurately identify the key state parameters of lithium-ion cell applied to electric ground vehicles, an extended single particle model of lithium-ion cell with electrolyte dynamics behaviors is first built up based on the porous electrode theory and concentration theory in this article. Compared with the conventional single particle cell model, the parameter description of the solid electrolyte interface film is incorporated into this model, and the coupled effects of temperature-dependent and electrolyte-dependent electrochemical parameters on the cell discharge are also taken into consideration. Based on this extended single particle cell model, a simplified parameter sensitivity analysis method and a comprehensive parameter identification scheme for lithium-ion cell are proposed herein, in which a sensitivity analysis of the capacity to a subset of electrochemical parameters that are hypothesized to evolve throughout the battery's life, is conducted to determine the highly sensitive parameters to be identified under some particular operation scenarios, and further to solve the parameter optimization problem using the genetic algorithm. Based on this method, the test data under the working condition of 1 C discharge rate at 23℃ are employed to evaluate the identified parameters of lithium-ion battery cell with a peak value of voltage error less than 3.8%. Afterwards, the effectiveness and feasibility of the proposed parameter identification scheme are validated by the comparative study of the simulated output voltage and the experimental output voltage under the same input current profile. Specifically, the 0.05 C discharge and HPPC (hybrid pulse power characterization) current profile are used to verify the evaluated parameters under the 1 C discharge condition, and the maximum relative errors of voltage with 0.05 C galvanostatic discharge profile at 23 and 45℃ are 3.4% and 2.6% by using our proposed SPMe_SEI model, and 5.7% and 4.0% by using the traditional SPMe model, respectively. Moreover, the maximum relative errors of voltage with HPPC discharge profile at 23 and 45℃ are 1.9% and 1.5% by using our proposed SPMe_SEI model, and 2.1% and 1.8% by using the traditional SPMe model, respectively. It is concluded that the proposed parameter identification scheme for a lithium-ion cell model can provide a solid theory foundation for facilitating the estimation of state-of-health in BMS application.

An interconnected state observer for lithium-ion battery based on reduced electrochemical model

The accurate estimation of the solid concentration distribution in anode and cathode, and state-of-charge (SOC) for a Li-ion battery cell is significantly important for developing the real-time monitoring algorithm of the Li-ion cell's working operation, and further establishing an efficient and reliable advanced battery management system (BMS). Firstly, according to the porous electrode theory and concentration theory, in this article we present a systematic optimized model and a method of identifying the key internal state parameters based on a Li-ion cell's enhanced single-particle-model (ESPM), in which, an appropriate parameter vector is identified in the typical hybrid-pulse-power-characterization (HPPC) operation scenario by using the parameter sensitivity analysis method, and then this parameter optimization problem is evaluated by genetic algorithm. It is verified that the maximum relative errors of the cell's output voltage for ESPM are 1.92%, 3.18% and 2.86% under HPPC, 1C-discharge and urban dynamometer driving schedule (UDDS) current profiles, respectively. Secondly, by introducing some assumptions and reduction techniques, the battery ESPM is further reduced and then a novel interconnected state observer is proposed through the combination of the reduced ESPM and H∞ robust control theory framework, which can realize the concurrent estimation of solid concentration and SOC in anode and cathode. Finally, the comparative validation and analysis study are conducted by using the experimental data acquired in HPPC and UDDS condition to demonstrate the effectiveness and feasibility of the proposed interconnected observer. The results show that the maximum relative errors of output voltage for the ESPM, the single-electrode concentration observer (Obsv-1) and the proposed interconnected observer (Obsv-2) of Li-ion cell are 2.0%, 3.8% and 2.6%, respectively, under HPPC operation at 23 ℃; under the same input current profile and operating condition, the maximum relative errors of SOC estimation are 2.4%, 4.7% and 3.4%, respectively. Moreover, the maximum relative errors of cell's output voltage for ESPM, Obsv-1 and Obsv-2 model are 1.9%, 3.2% and 2.1%, respectively, and the maximum relative errors of SOC estimation values for these three mathematical models are 2.1%, 4.4% and 3.2%, respectively. It is concluded that the proposed robust observer for a Li-ion cell can accurately predict the output voltage and SOC, and can also improve the dynamic performance and robust stability of Li-ion cell, which provides a solid theoretical foundation for developing the BMS.

Impact of Pore Tortuosity on Electrode Kinetics in Lithium Battery Electrodes: Study in Directionally Freeze-Cast LiNi0.8Co0.15Al0.05O2 (NCA)

The prevailing electrode fabrication method for lithium-ion battery electrodes includes calendering at high pressures to densify the electrode and promote adhesion to the metal current collector. However, this process increases the tortuosity of the pore network in the primary transport direction and imposes severe tradeoffs between electrode thickness and rate capability. With the aim of understanding the impact of pore tortuosity on electrode kinetics, and enabling cell designs with thicker electrodes and improved cost and energy density, we use here freeze-casting, a shaping technique able to produce low-tortuosity structures using ice crystals as a pore-forming agent, to fabricate LiNi0.8Co0.15Al0.05O2 (NCA) cathodes with controlled, aligned porosity. Electrode tortuosity is characterized using two complementary methods, X-ray tomography combined with thermal diffusion simulations, and electrochemical transport measurements. The results allow comparison across a wide range of microstructures, and highlight the large impact of a relatively small numerical change in tortuosity on electrode kinetics. Under galvanostatic discharge, optimized microstructures show a three- to fourfold increase in area-specific capacity compared to typical Li-ion composite electrodes. Hybrid pulse power characterization (HPPC) demonstrates improved power capability, while dynamic stress tests (DST) shows that an area-specific area capacity corresponding to 91% of the NCA galvanostatic C/10 capacity could be reached.

Model-based unscented Kalman filter observer design for lithium-ion battery state of charge estimation

Accurate battery state-of-charge is essential for both driver notification and battery management units reliability in electric vehicle/hybrid electric vehicle. It is necessary to develop a robust state of charge (SOC) estimation approach to cope with nonlinear dynamic battery systems. This paper proposed an estimation method to identify the SOC online based on equivalent circuit battery model and unscented Kalman filter technique. Firstly, the parameters of dynamic battery model are identified offline and validated through typical electric vehicle road operation to guarantee its precision. Then the performance with respect to converge time, observer accuracy, robustness against system modeling errors, and mismatched initial SOC guess values is investigated. The accuracy of proposed estimation algorithm is validated under improved hybrid power pulse characterization test and New European Driving Cycle. Experiment and numerical simulation results clearly demonstrate that the proposed method is highly reliable with good robustness to different operating conditions and battery aging.

Influence of Mechanical Pressure on the Electrical Performance of Lithium-Ion Silicon Batteries

Lithium-ion batteries are the state of the art in electronic consumer products, ranging from smartphones to cars. Demands on lifetime and energy/power density are ever increasing to extend the duration the battery can be used. Hence, there is a continuous need to further improve lithium-ion batteries [1]. This is exactly what has been tackled in the European project FiveVB, which strives to increase the energy density of a battery as well as to perform a thorough study of the aging phenomena. One of these improvements in energy density can be achieved by using lithium-silicon alloys as negative electrode because of the high theoretical capacity of silicon (3.600 mAh/g) which is 10 times higher than the one of graphite, used in most lithium-ion batteries as negative electrode. The high theoretical capacity of Li-Si alloys has been known for a long time but their practical application has been prevented by serious issues associated with large volume expansion (>200%) during lithium uptake, that induce cracks and mechanical disintegration of the electrode, finally leading to losses of electronic contact and a severe capacity fading already after a number of cycles [2], [3]. This disadvantage has been investigated and improved by using a dedicated electrolyte as well as a special designed negative electrode. Throughout the project an extended electrical characterization has been performed on silicon based non-commercial pouch cells. One important conclusion is that effect of mechanical pressure on this cell has a significant influence on its performance: mainly capacity retention (ultimately cycle life) and energy density. In this paper this influence will be discussed in detail by using classical electrical characterization tests such as an Open-Circuit Voltage test (OCV), a capacity test and Hybrid Pulse Power Characterization (HPPC) test complying with the existing standards. In this abstract only the capacity test will be discussed, the remaining tests will be discussed in the full paper. Results and discussion: The procedure used during the capacity test consists of standard charging phase (constant current and constant voltage) followed by several discharges at different C-rates (C/5, C/3, 1C & 1.5C), based upon the international standard IEC 62660-1. The current and its corresponding voltage response of a capacity test of the clamped Si-alloy pouch cells is visualized in figure 1. Figure 2 provides a comparison of the discharge capacity of both the clamped an unclamped pouch cells. It can be clearly seen that when clamping the cell a significant increase of 10-20% in the discharge capacity can be observed. The most probable root cause of this difference is due to gassing of the negative electrodes because after the electrical characterization tests it can be clearly observed that the unclamped pouch cells are swollen and the clamped ones show no signs of swelling. These gasses are not conductive resulting in regions of low conductivity which are not taking part anymore in the electrochemical reactions. The clamped cell shows no swelling which can be explained by two reasons: firstly, it is possible that due to the pressure of clamping the gassing is reduced or secondly that the gas is pushed to the side of the cell where it doesn’t interfere with the electrochemical reactions. Since no gassing is observed the firstly is more likely. An extended analysis of the effect of mechanical pressure on the electrical performance will be discussed in detail in the full paper by means of the remaining electrical characterization tests (OCV and HPPC) as well as an extended investigation of the root cause will be discussed in the full paper.

Characterizing the Tortuosity of Aligned Pores and Its Effects on Electrode Kinetics in Directionally Freeze-Cast Cathodes

The prevailing electrode fabrication method for lithium-ion battery electrodes includes calendering at high pressures to densify the electrode and promote adhesion to the metal current collector. However, this process also increases the tortuosity of the pore network in the primary transport direction. In practice, to meet operational C-rates desired, and production throughput objectives, the thicknesses of commercial electrodes are restricted to less than ~100 µm, and the areal capacity to less than ~3.5 mAh/cm2.[ 1 ] With the aim of increasing cell energy density and decreasing cost by building thicker electrodes, several strategies to reduce pore tortuosity in the direction normal to the current collector have been proposed.[ 2-4 ] While improvements in the electrode transport and usable area capacity have been demonstrated when the pore tortuosity is reduced, specific correlations to pore tortuosity have been lacking. Here, we use freeze-casting, a shaping technique able to produce low-tortuosity structures by using ice crystals as a pore-forming agent, to fabricate LiNi0.8Co0.15Al0.05O2(NCA) porous cathodes with a wide range of pore topologies. Two complementary approaches were then used to evaluate the tortuosity of these electrodes. In the first approach, the electrodes were scanned using synchrotron X-ray micro-tomography, and a heat transfer simulation based on a voxel algorithm was directly applied to the 2D binarized image sequence. This algorithm mimics mass transport and diffusion behavior by computing and solving Fourier’s law equations directly across the voxel domain of the porous phase. The second approach used direct current (DC) measurements of polarization and depolarization kinetics to determine the lithium ion diffusivity of the electrolyte-infused porous electrodes. Finally, the electrochemical performance of the electrodes were evaluated in half-cells. The results allow comparison across a wide range of microstructures, and highlight the large impact of a relatively small numerical change in tortuosity on electrode kinetics. Under galvanostatic discharge, the lowest-tortuosity microstructures showed a three- to fourfold increase in area-specific capacity (15 mAh/cm2 at C/10 rate and >5 mAh/cm2 at 1C rate) compared to typical Li-ion composite electrodes. Hybrid pulse power characterization (HPPC) demonstrated improved power capability (150 mW/cm2 to 95 mW/cm2), while dynamic stress tests (DST) showed that an area-specific area capacity of 11.5 mAh/cm² could be reached with a lower discharge voltage limit of 2.5V, corresponding to 91% of the NCA theoretical capacity. This work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Vehicle Technologies of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, Subcontract No. 7056592 under the Advanced Battery Materials Research (ABMR) Program.

A new method to estimate the state of charge of the green battery

Green batteries have attracted great attention due to the characteristics of its high performance and non-pollution. In order to understand the working condition of the batteries and get a better estimation effect on the state of charge (SoC), the following works had been done in NMC18650 lithium ion battery. Firstly, the hybrid pulse power characteristic (HPPC) test was carried out on the battery with different currents. The extended Kalman filter (EKF) was used to estimate the SoC of the battery based on combined model and Thevenin model whose parameters were identified in advance; furthermore, the estimation results of the two models were compared. Secondly, an improved open circuit voltage (OCV) based method was proposed. Its improvements were as follows: the changes of OCV on battery were recorded during the current interruption, and it was assumed that the OCV had been restored to a certain degree if the change of OCV did not exceed 0.001V in 10s. Finally, two new improved methods were proposed based on the combined model, and the estimation effects of the above methods were compared under dynamic condition. The results showed that the accuracy of the Thevenin model was slightly higher than that of the combined model, and the accuracies of the two improved methods were both improved. Especially the second improved method had the least error and the best adaptability; the maximum error under dynamic conditions was 3.07%, and the average error was less than 1%, which only accounted for 22.46% of the un- improved. The improved OCV based method proposed in this study is applied to the SoC estimation of batteries, which greatly improves the accuracy of the estimation; moreover, the method is easy to implement and suitable for estimating SoC in real time.

Electrochemical Model-Based Condition Monitoring via Experimentally Identified Li-Ion Battery Model and HPPC

Electrochemical model-based condition monitoring of a Li-Ion battery using an experimentally identified battery model and Hybrid Pulse Power Characterization (HPPC) cycle is presented in this paper. LiCoO2 cathode chemistry was chosen in this work due to its higher energy storage capabilities. Battery electrochemical model parameters are subject to change under severe or abusive operating conditions resulting in, for example, Navy over-discharged battery, 24 h over-discharged battery, and overcharged battery. Stated battery fault conditions can cause significant variations in a number of electrochemical battery model parameters from nominal values, and can be considered as separate models. Output error injection based partial differential algebraic equation (PDAE) observers have been used to generate the residual voltage signals in order to identify these abusive conditions. These residuals are then used in a Multiple Model Adaptive Estimation (MMAE) algorithm to detect the ongoing fault conditions of the battery. HPPC cycle simulated load profile based analysis shows that the proposed algorithm can detect and identify the stated fault conditions accurately using measured input current and terminal output voltage. The proposed model-based fault diagnosis can potentially improve the condition monitoring performance of a battery management system.

Extended Kalman Filter-Based State of Charge and State of Power Estimation Algorithm for Unmanned Aerial Vehicle Li-Po Battery Packs

Customer requirements for unmanned aerial vehicles (UAVs) with long flight times are increasing exponentially in the personal, commercial, and military use areas. Due to their limited payload, large numbers of on-board battery packs cannot be used and this is the main reason behind the need for battery management software (BMS) packages with state of charge (SOC) estimation functions to increase the flight time. At the same time, as the UAV application range has extended widely, the size of UAVs has increased and heavy-duty UAVs are slowly appearing. As a result, the system operating power of the UAVs has been increased tremendously and their safe system power operation has become an issue. This is the main reason for the need of BMS having state of power (SOP) estimation functions. In this work a 6 S Li-Po battery pack is simulated with two ladder equivalent circuit models (ECMs) considering an impedance effect whose parameters are found using hybrid pulse power characterization (HPPC) current patterns with parameter determination using the table-based linear interpolation (TBLI) method. Two state estimation methods, including the current integration method and the extended Kalman filter (EKF) method are developed and the estimation accuracies of SOC and SOP are compared. Results show that the most accurate SOC estimation turns out to be 0.1477% (indoor test with HPPC), 0.1324% (outdoor test with 0 kg payload), and 0.2021% (outdoor test with 10 kg payload). Also, the most accurate SOP estimation error turns out to be 1.2% (indoor test with HPPC), 3.6% (outdoor test with 0 kg payload), and 4.2% (outdoor test with 10 kg payload).

Cycling degradation testing and analysis of a LiFePO4 battery at actual conditions

Summary This paper presents a degradation testing of a lithium-ion battery developed using real world drive cycles obtained from an electric vehicle (EV). For this, a data logger was installed in the EV, and real world drive cycle data were collected. The EV battery system consists of 3 lithium-ion battery packs with a total of 20 battery modules in series. Each module contains 6 series by 49 parallel lithium-ion cells. The vehicle was driven in the province of Ontario, Canada, and several drive cycles were recorded over a 3-month period. However, only 4 drive cycles with statistical analysis are reported in this paper. The reported drive cycles consist of different modes: acceleration, constant speed, and deceleration in both highway and city driving at −6°C, 2°C, 10°C, and 23°C ambient temperatures with all accessories on. Additionally, individual cell characterization was conducted using a C/25 (0.8A) charge-discharge cycle and hybrid pulse power characterization (HPPC). The Thevenin battery model was constructed in MATLAB along with an empirical degradation model and validated in terms of voltage and SOC for all drive cycles reported. The presented model closely estimated the profiles observed in the experimental data. Data collected from the drive cycles showed that a 4.6% capacity fade occurred over the 3 months of driving. The empirical degradation model was fitted to these data, and an extrapolation estimated that 20% capacity fade would occur after 900 daily drive cycles.

A unified modeling framework for lithium-ion batteries: An artificial neural network based thermal coupled equivalent circuit model approach

The thermal coupled equivalent circuit model provides a vital role not only in accurate and reliable state monitoring, but also in effective thermal management of lithium-ion batteries. However, it lacks appropriate modeling strategies for including both the temperature and state of charge effects into the thermal coupled equivalent circuit model. In this study, a unified artificial neural network based thermal coupled equivalent circuit model approach is proposed to accurately and reliably capture the electrical and thermal dynamics of lithium-ion batteries. Both reversible and irreversible heat generation mechanisms are introduced in the thermal model. The quantitative relationship between circuit parameters and temperature/state of charge in equivalent circuit model is modeled by artificial neural network. Both electrical and thermal related parameters are simultaneously identified by means of least square strategy with l1-norm penalty on output weights in artificial neural network and positive constraints on circuit parameters. The effectiveness of the proposed artificial neural network based thermal coupled equivalent circuit model approach is validated by the experimental constant current discharge, pulse current discharge test and hybrid pulse power characterization test of a commercial large-format pouch-type lithium-ion battery. It implies that the proposed hybrid modeling strategy can provide a general framework for the inclusion of other effects such as health state and current into battery models and can be easily extended to more complicated models such as first-principle electrochemical-thermal model.

A Neural Network Based State-of-Health Estimation of Lithium-ion Battery in Electric Vehicles

Abstract As one of the main promising power sources in electric vehicles (EVs), lithium-ion battery plays an important role in EVs’ power system. Its state-of-health (SOH) estimation is a key technology in the battery management system (BMS). Many battery parameters can reflect the battery's health, like internal resistance, internal capacity, etc. In this paper, we use maximum available capacity to indicate the battery's SOH based on a back propagation (BP) neural network. The main contributions of this paper include: (1) a direct parameter extraction method by HPPC (Hybrid Pulse Power Characterization) test is employed to identify the battery parameters of the first-order equivalent circuit model. (2) The parameters can be used to train the three-layer BP neural network, therefore the structure and parameters including weights and thresholds of the network can be determined. This well-trained neural network is used to estimate the SOH. (3) The static and dynamic current profile tests are carried out to verify the accuracy of the training results of the proposed neural network. The experimental results indicate that the proposed neural network based estimation method can present accuracy and suitability for SOH estimation with low computation cost.

Figure of Merit Approach for Evaluating L.I.B. Electrolyte Additives in a Combinatorial Study

In order to extend the lifetime and performance of lithium ion battery systems, the use of electrolyte additives continues to be one of the most effective and economical approaches currently under investigation. Common metrics used to measure the relative performance enhancement of these electrolyte additives include capacity retention and impedance rise. This study details efforts to 1) perform a combinatorial analysis of electrolyte additives for use in high voltage lithium ion battery systems, and 2) evaluate the performance of the tested electrolyte systems using two figures of merit (FOM) which address both capacity (energy) and impedance (power). Eight known additives were used throughout the course of the test: three additives known to work on the positive (cathode) electrode (TMSPi, TEPi, LiDFOB) and five additives known to work on the negative (anode) electrode (LiBOB, VC, tVCBO, PBE, PES). Electrochemical testing was performed on LiNi0.5Mn0.3Co0.2O2 /graphite full cells operating between 3.0-4.4 V. The cycling protocol combines several C rates (C/1, C/3, C/10) for capacity and rate information and intermittent hybrid pulse power characterization (HPPC) tests from which area specific impedance (ASI) values are calculated. Results from extensive testing reveal interesting trends in the electrolyte systems. In general, TMSPi performed well as an additive while a structurally analogous molecule, TEPi, performed poorly. Other trends, such as the effects of LiBOB on energy fade and VC on power fade, were found. Capacity, impedance and other electrochemical data will be presented to give a comprehensive overview of performance effects of the tested electrolyte additives. This work underscores the importance of developing evaluation methodologies in order to objectively view the performance of electrolyte systems and identify trends in electrolyte additive behavior.

Correlation between the model accuracy and model-based SOC estimation

State-of-charge (SOC) estimation is a core technology for battery management systems. Considerable progress has been achieved in the study of SOC estimation algorithms, especially the algorithm on the basis of Kalman filter to meet the increasing demand of model-based battery management systems. The Kalman filter weakens the influence of white noise and initial error during SOC estimation but cannot eliminate the existing error of the battery model itself. As such, the accuracy of SOC estimation is directly related to the accuracy of the battery model. Thus far, the quantitative relationship between model accuracy and model-based SOC estimation remains unknown. This study summarizes three equivalent circuit lithium-ion battery models, namely, Thevenin, PNGV, and DP models. The model parameters are identified through hybrid pulse power characterization test. The three models are evaluated, and SOC estimation conducted by EKF-Ah method under three operating conditions are quantitatively studied. The regression and correlation of the standard deviation and normalized RMSE are studied and compared between the model error and the SOC estimation error. These parameters exhibit a strong linear relationship. Results indicate that the model accuracy affects the SOC estimation accuracy mainly in two ways: dispersion of the frequency distribution of the error and the overall level of the error. On the basis of the relationship between model error and SOC estimation error, our study provides a strategy for selecting a suitable cell model to meet the requirements of SOC precision using Kalman filter.

Design and Demonstration of Three-Electrode Pouch Cells for Lithium-Ion Batteries

Simple three-electrode pouch cells which can be used in distinguishing the voltage and resistance in individual electrodes of lithium ion batteries have been designed. Baseline (1 mm-staggered alignment, cathode away from a reference electrode) and aligned electrodes to a reference electrode located outside of the anode and cathode were studied to see alignment effects on resistance analysis. Cells composed of A12 graphite anodes, LiNi0.5Mn0.3Co0.2O2 (NMC 532 or NCM 523) cathodes, lithium foil references, microporous tri-layer membranes, and electrolytes, were cycled with cathode cutoff voltages between 3.0 V and 4.3 V for formation cycles or 4.6 V for C-rate performance testing. By applying a hybrid pulse power characterization (HPPC) technique to the cells, resistances of the baseline cells contributed by the anode and cathode were found to be different from those of the aligned cells, although overall resistances were close to ones from aligned cells. Resistances obtained via electrochemical impedance spectroscopy (EIS) and 2D simulation were also compared with those obtained from HPPC.