Batteries For Electric Vehicle Applications Research Articles

Feature Selection and Optimal Neural Network Algorithm for the State of Charge Estimation of Lithium-ion Battery for Electric Vehicle Application

This paper presents the estimation of the state of charge (SOC) for a lithium-ion battery using feature selection and an optimal NN algorithm. Principle component analysis (PCA) is used to select the most influencing features. Out of nine variables, five input variables are selected based on the value of eigenvectors. An optimal neural network (NN) is developed by selecting the hidden layer neurons and learning rate since these parameters are the most critical factors in constructing a NN model. The model is tested and evaluated by using US06 driving cycle at 25°C and 45°C respectively. In order demonstrate the effectiveness and accuracy of the proposed model, a comparative study is performed between proposed NN model and two different NN models (NN1 and NN2). The proposed NN model estimates SOC with lower mean squared error (MSE) and root mean squared error (RMSE) compared to two NN models which proves that the proposed model is competent and robust in estimating SOC. The simulation results show an improvement in proposed NN model accuracy over NN1 and NN2 models in minimizing RMSE by 26% and 22% and MSE by 45% and 39% respectively at 25°C.

Mathematical fitting for the variation in capacity of lithium iron phosphate batteries corresponding to cycles

It is well known that the capacity of lithium iron phosphate (LiFePO\(_{4}\)) batteries decreases as the usage cycles increase. In order to investigate the relationship between the number of charge/discharge cycles and the battery capacity, two batteries were cyclically charged and discharged using an automatic testing system, while simultaneously collecting the capacity data. The tested batteries were composed of different materials and had a capacity of 15 Ah. Each battery was cyclically charged and discharged 800 times. The batteries are analyzed and compared with each other to create fitted curves. The developed mathematical fitting, which consists of both exponential and polynomial terms, is closely responsive to the battery capacity. References W. Su, H. Rahimi-Eichi, W. Zeng and M.-Y. Chow. A survey on the electrification of transportation in a smart grid environment. IEEE Trans. Ind. Inform. , 8:1–10, 2012. doi:10.1109/TII.2011.2172454 J. Wang, Z. Sun and X. Wei. Performance and characteristic research in LiFePO\(_4\) battery for electric vehicle applications. IEEE Proc. Vehicle Power Propul. Conf. , 1657–1661, 2009. doi:10.1109/VPPC.2009.5289664 T. Kim and W. Qiao. A hybrid battery model capable of capturing dynamic circuit characteristics and nonlinear capacity effects. IEEE Trans. Energy Conv. , 26:1172–1180, 2011. doi:10.1109/TEC.2011.2167014 A. Eddahech, O. Briat and J.-M. Vinassa. Determination of lithium-ion battery state-of-health based on constant-voltage charge phase. J. Power Sources , 258:218–227, 2014. doi:10.1016/j.jpowsour.2014.02.020 A. Shafiei, A. Momeni and S. S. Williamson. Battery modeling approaches and management techniques for plug-in hybrid electric vehicles. IEEE Proc. Vehicle Power Propul. Conf. , 1–5, 2011. doi:10.1109/VPPC.2011.6043191 M. Chen and G. A. Rincon-Mora. Accurate electrical battery model capable of predicting runtime and i-v performance. IEEE Trans. Energy Conv. , 21:504–511, 2006. doi:10.1109/TEC.2006.874229 L. Gao, S. Liu and R. A. Dougal. Dynamic lithium-ion battery model for system simulation. IEEE Trans. Comp. Pack. Tech. , 25:495–505, 2002. doi:10.1109/TCAPT.2002.803653 O. Erdinc, B. Vural and M. Uzunoglu. A dynamic lithium-ion battery model considering the effects of temperature and capacity fading. IEEE Inter. Conf. Clean Elec. Power , 383–386, 2009. doi:10.1109/ICCEP.2009.5212025 B. Schweighofer, K. M. Raab and G. Brasseur. Modeling of high power automotive batteries by the use of an automated test system. IEEE T. Instrum. Meas. , 52:1087–1091, 2003. doi:10.1109/TIM.2003.814827 S.-Y. Lee, W.-L. Chiu, Y.-S. Liao, K.-Y. Lee, J.-H. Chen, H.-J. Lin and K. Li. Modified empirical fitting of the discharge behavior of LiFePO\(_4\) batteries under various conditions. ANZIAM J. , 55:368–383, 2014. doi:10.21914/anziamj.v55i0.8182 S. Flandrois and B. Simon. Carbon materials for lithium-ion rechargeable batteries. Carbon , 37:165–180, 1999. doi:10.1016/S0008-6223(98)00290-5 H. Li and H. Zhou. Enhancing the performances of Li-ion batteries by carbon-coating: present and future. Chem. Comm. , 48:1201–1217, 2012. doi:10.1039/C1CC14764A J. Wang and X. Sun. Understanding and recent development of carbon coating on LiFePO\(_4\) cathode materials for lithium-ion batteries. Energ. Environ. Sci. , 5:5163–5185, 2012. doi:10.1039/C1EE01263K P. Bai and M. Z. Bazant. Charge transfer kinetics at the solid-solid interface in porous electrodes. Nature Comm. , 5:3585, 2014. doi:10.1038/ncomms4585

Superior Performance and Ageing Robustness of 17 a h Class, Graphite/NMC Stacked Cells Manufactured through Eco-Friendly Materials and Processes

Lithium-ion batteries represent one the best chances for the next BEV and HEV generation [1]. Taking into account the actual lithium-ion technology, the safety and cost appear the main drawbacks holding the introduction of these storage systems into the automotive market. However, the current Li-ion battery manufacturing process has been greatly advancing in the last twenty years and those developments are also the basis for the production of large format cells demanded for automotive application. The GREENLION consortium, a large scale collaborative project within the FP7 (GC.NMP.2011-1) [2] was aimed to tackle these issues, leading to the manufacturing of greener and cheaper lithium-ion batteries for electric vehicle applications via the use of water soluble, fluorine-free, high thermally stable binders, which would eliminate the use of organic compounds as solvent in the electrode coating process and reduce the cell assembly cost. The GREENLION project has identified three key levels in the value scale before the final battery pack integration for EVs, namely electrode processing, cell manufacturing and battery module assembly, which are the backbone of the lithium-ion battery production line. Here, the performance and ageing robustness of 17 A h class, graphite/NMC stacked cells designed, developed and manufactured within GREENLION project, are reported. Results and Discussions The cells exhibited analogous behavior in terms of charge/discharge features, displaying resistance below 5 m and nominal capacity equal or exceeding 17 A h at 23 °C (Figure 1). Slightly higher values, i.e., around 18 A h, are delivered at 45 °C whereas, at 0 °C, the cells are able to still deliver more than 80 % of their nominal capacity. Excellent cycling performance, reported in Figure 2, was exhibited, e.g., practically 100 % of the initial capacity was delivered after more than 500 full charge/discharge cycles run at 100 % DOD. The graphite/NMC stacked cells were subjected to prolonged ageing tests, carried out holding the GREENLION cells at 45 °C and monitoring their capacity every 4 weeks. The results, plotted in Figure 3 as capacity evolution recorded during storage tests, have revealed very remarkable robustness towards ageing phenomena. For instance, from 86 to 88 % of the initial capacity is retained upon 4 months. These characteristics, in combination with the excellent electrochemical performance, are clear indications of the good cell design and manufacturing of the GREENLION cells based on eco-friendly electrodes. Acknowledgements The authors wish to thank the financial support of the European Commission for the GREENLION project within the 7thFramework Program (Grant agreement n°: 285268).

On-board aging estimation using half-cell voltage curves for LiFePO4 cathode-based lithium-ion batteries for EV applications

The aim of this work is the design of an algorithm for on-board determination of the actual capacity of a lithium iron phosphate (LFP) cathode-based lithium-ion battery for electric vehicle applications. The presented approach is based on the detection of the predominant aging mechanisms (in terms of loss of lithium and loss of active material in both electrodes) by determining the single electrode voltage curves. The information related to the characteristic length and position of the voltage plateaus, which can be gathered during battery operation, can be used to obtain the actual aging state of the cells. The length of the plateaus depends on the respective position that the voltage curves of the single electrodes have in relation to each other. Relating the change of the plateau characteristics to the possible aging mechanisms allows the determination of the actual battery aging state in terms of total cell capacity. The work presents a possible implementation of an algorithm for capacity determination based on the described methodology. The algorithm is validated with various differently aged LFP cells. Furthermore, the work discusses the ability of the method to detect the actual battery capacity if the characteristics of only part of the quasi-OCV (open circuit voltage) curve are detected. Achieved accuracy and existing limitations are described and discussed in detail.

LiFePO4 battery state of charge estimation based on the improved Thevenin equivalent circuit model and Kalman filtering

Lithium iron phosphate (LiFePO4) batteries are widely used as power batteries for electric vehicle applications. For safety issues, it is important to estimate the State of Charge (SOC) of a battery accurately. The improved Thevenin equivalent circuit model is established according to the characteristics of the LiFePO4 battery, and the model parameters are identified by experimental testing. Furthermore, a novel algorithm of SOC online estimation is proposed, which combines the open-circuit voltage method, ampere-hour integration, and Kalman filtering. The simulations and experimental results show that the improved Thevenin equivalent circuit model can enhance the accuracy of SOC estimation. This proposed algorithm could estimate the SOC precisely even with inaccurate initial values and current measurement errors and distinguish the performances between the batteries. The performance of the proposed SOC estimation method when the voltage sensor is unavailable has been investigated and presented as well. From the characteristics mentioned above, this novel approach is able to guarantee the reliability and safety of the batteries.

Experimental investigation on performance of lithium-ion battery thermal management system using flat plate loop heat pipe for electric vehicle application

The development of electric vehicle batteries has resulted in very high energy density lithium-ion batteries. However, this growth is accompanied by the risk of thermal runaway, which can cause serious accidents. Heat pipes are heat exchangers that are suitable to be applied in electric vehicle battery thermal management for their lightweight and compact size, and they do not require external power supply. This study examined experimentally a flat plate loop heat pipe (FPLHP) performance as a heat exchanger in the thermal management system of the lithium-ion battery for electric vehicle application. The heat generation of the battery was simulated using a cartridge heater. Stainless steel screen mesh was used as the capillary wick. Distilled water, alcohol, and acetone were used as working fluids with a filling ratio of 60%. It was found that acetone gave the best performance that produces a thermal resistance of 0.22 W/°C with 50 °C evaporator temperature at heat flux load of 1.61 W/cm2.

Hybrid Sinusoidal-Pulse Charging Method for the Li-Ion Batteries in Electric Vehicle Applications Based on AC Impedance Analysis

A hybrid sinusoidal-pulse current (HSPC) charging method for the Li-ion batteries in electric vehicle applications is proposed in this paper. The HSPC charging method is based on the Li-ion battery ac-impedance spectrum analysis, while taking into account the high power requirement and system integration. The proposed HSPC method overcomes the power limitation in the sinusoidal ripple current (SRC) charging method. The charger shares the power devices in the motor inverter for hardware cost saving. Phase shifting in multiple pulse currents is employed to generate a high frequency multilevel charging current. Simulation and experimental results show that the proposed HSPC method improves the charger efficiency related to the hardware and the battery energy transfer efficiency.

Dynamical modeling of Li-ion batteries for electric vehicle applications based on hybrid Particle Swarm–Nelder–Mead (PSO–NM) optimization algorithm

In recent years, Li-ion batteries are widely used in various applications, such as electric and hybrid vehicles application. Their higher specific power and energy density, high cycle lifetime and decreasing costs have made them an attractive and alternative energy storage technology to lead-acid or nickel-metal hydride batteries in embedded power supplies. In the present work, the electric modeling of a Li-ion battery cell in real operation conditions imposed by an electric vehicle application is carried out. A dynamic equivalent circuit model has been used to simulate several electrochemical processes occurring in a commercially available 40 Ah Li-ion battery cell with NMC cathode material and graphitic anode. The model is parameterized with measurement data in time-domain using a hybrid Particle Swarm–Nelder–Mead (PSO–NM) optimization algorithm. This last one is used to solve the parameters identification problem of Li-ion battery model.The developed model of Li-ion battery cell has been validated on real driving cycle provided by an urban electric vehicle. Obtained results show that there is a good match between experiment and simulation results with a maximum modeling error less than 0.5%, which proves the well performance of our model and confirms the interest of a hybrid (PSO–NM) optimization algorithm for battery identification parameters.

A Review of SOH Estimation Methods in Lithium-ion Batteries for Electric Vehicle Applications

Abstract Aging of lithium-ion cells is an inevitable phenomenon limiting the lifetime in electric vehicle (EV) applications. Undesirable side reactions during cycle or calendar aging may affect the performance of the batteries. This results in a decreased capacity and an increase in the cell impedance. Furthermore, the aging phenomena are highly complicated to characterize due to the coupling of different factors. In this review, various aspects of recent research and developments, on lithium-ion battery aging mechanisms, analysis and estimations are put forward. To better understand a comparison and summary of techniques, models and algorithms used for battery aging estimation (SOH,RUL), going from a detailed spectroscopy and electrochemical technique to statistical methods based on data are presented in this paper, and their respective characteristics are discussed. In addition some considerations about the ideal method that can be derived from existing methods should be discussed and reported in this paper.

Ultra-Capacitors and Battery Management System of Electric Vehicles

This paper presents a bidirectional converter system to manage power transfer between ultra-capacitors (UC) and batteries for electric vehicle applications. A bidirectional buck-boost converter is used as a key converter to control power flow of the system. A control method is introduced to increase life cycle of both batteries and UC. Ultimately, the proposed converter system is able to (i) manage power between DC-bus, batteries and UC, (ii) control the current to flow between batteries and UC, (iii) maintains battery operation within charging limits, (iv) maintain the voltage at UC as setting value, (v) control the recharging at the UC by using the energy feedback from the battery instead of the common DC-bus and (vi) manage UC and battery to be recharged by using regenerative braking system. The validation of the proposed system is verified with analysis and simulation results by using MATLAB/Simulink.

Operando Imaging of Electrochemical Phase Transitions in LiNi0.8Co0.15Al0. 05O2 secondary Particles

The observation in dynamic conditions of electrochemical transformations relevant to energy storage creates the opportunity of identifying design rules that directly reflect the veiled electrochemical reaction mechanisms which are controlled by kinetics under load. Under the intrinsic limitations of ex-situ methods, opening the circuit to harvest multiple samples of interest at the different stages of reaction can lead to relaxation of the materials to a different, thermodynamically stable state. In addition, these operandoobservations must occur at multiple scales, but are particularly critical at the individual cathode particle level, where incomplete reactions and failure are prone to occur. In this study, we focused on LiNi0.8Co0.15Al0.05O2 (NCA)-based cathode materials, which are one of the leading candidates for use in high energy density Li-ion batteries for electric vehicle applications. This material offers high storage, but still presents limitations vis-à-vis capacity loss and concurrent increases in impedance. We adopted operando 2D full field transmission X-ray microscopy (FF TXM) coupled with X-ray absorption near edge spectroscopy (XANES) to follow the chemical and microstructural evolution at the secondary particles of NCA with sub-30 nm spatial resolution. Chemical phase maps from quantitative analysis of pixel-by-pixel X-ray absorption near edge structure spectra and morphological information of optical-density images at the post-absorption-edge region were obtained at the different state of charge as shown in Figure 1. As a result, this work successfully revealed the existence of kinetically-induced inhomogeneities during the electrochemical phase transition, and their morphological consequences, within a secondary particle, at nanoscale spatial resolution during an electrochemical reaction. We believe the rich insight provided by high resolution spectro-microscopy could inform the design of optimized architectures and the projection of performance using continuum models. Figure 1. A. Charge galvanostatic profile of operando Li metal half cell with an NCA composite cathode. The cell charged to capacity of 350 mAh·g-1 with cycling rate of C/15 (C was defined as the current density to achieve the theoretical capacity in 1 hour). Chemical maps were obtained at different state-of-charges (SOCs), labeled as 1 ~ 9, indicated as blue outlined circles. B. A map of white line peak positions for a secondary particle at SOC 5. Higher white line peak position indicates more oxidazed area. Figure 1

N, N-Diethyltrimethylsilylamine (DETMSA) As Electrolyte Additive for Si Based High Energy Lithium-Ion Batteries

Silicon is a highly promising high capacity anode material for high energy lithium-ion batteries for electric vehicle application. Great progress has been made especially in the area of material level on the nano scale.1 However, the electrolyte research tailored to further improve the performance of the Si anode is limited.2 In this paper, we present a new electrolyte additive N, N-diethyltrimethylsilylamine (DETMSA) which showed great improvement over the conventional carbonate electrolyte using the fluoroethylene carbonate as additive. Our experiment data showed that with addition of 0.5 wt. % of DETMSA to the baseline electrolyte 1.2 M LiPF6 EC/EMC+10% FEC, the Li/Si-graphite cell gained a 10% improvement in capacity retention increase with high coulombic efficiency at room temperature. The functioning mechanism of the new electrolyte additive for Si anode was investigated by the XPS, SEM/EDX and FT-IR. 3

Model-Based Parameter Identification of Healthy and Aged Li-ion Batteries for Electric Vehicle Applications

Model-Based Parameter Identification of Healthy and Aged Li-ion Batteries for Electric Vehicle Applications

On-board Aging Estimation using Half-cell Voltage Curves for LiFePO4 Cathode-based Lithium-Ion Battery for Electric Vehicle Application

The aim of this work is the design of an algorithm for on-board determination of the actual capacity of a LiFePO4 cathode-based lithium-ion battery in electric vehicle applications. The presented approach is based on the detection of the predominant aging mechanisms (in terms of loss of lithium and loss of active material in both electrodes) through the determination of the single electrode voltage curves. The information related to the characteristic length and position of the voltage plateaus which can be gathered during the battery operation can be used to obtain the actual aging state of the cells. The length of the plateaus depends on the respective position that the voltage curves of the single electrodes have in relation to each other. Relating the change of the plateau characteristics with the possible aging mechanism allows the determination of the actual battery aging state in terms of total cell capacity. The work presents a possible implementation of an algorithm for capacity determination based on the described methodology. The algorithm is validated with various differently aged LiFePO4 cells. Furthermore, the work discusses the ability of the method to detect the actual battery capacity when only part of the quasi-OCV curve is measured. Achieved accuracy and existing limitations are described and discussed in detail.

Modified empirical fitting of the discharge behavior of LiFePO\(_4\) batteries under various conditions

A mathematical model is developed by fitting the discharge curve of a new LiFePO\(_4\) battery and then used to investigate the relationship between the discharge time and the closed-circuit voltage. This model consists of exponential and polynomial terms where the exponential term dominates the discharge time of a battery and the polynomial term dominates the change in the closed-circuit voltage. Time shift and time scale processes modify the exponential and polynomial terms, respectively, so that the model is suitable for batteries under various conditions. References W. Su, H. Eichi, W. Zeng and M.-Y. Chow, A survey on the electrification of transportation in a smart grid environment, IEEE Intl. Conf. Ind. I. 8:1–10, 2012. doi:10.1109/TII.2011.2172454 J. Wang, Z. Sun and X. Wei, Performance and characteristic research in LiFePO\(_4\) battery for electric vehicle applications, IEEE Vehicle Power 1657–1661, 2009. doi:10.1109/VPPC.2009.5289664 A. Shafiei, A. Momeni and S. S. Williamson, Battery modeling approaches and management techniques for plug-in hybrid electric vehicles, IEEE Vehicle Power 1–5, 2011. doi:10.1109/VPPC.2011.6043191 P. Bai, D. A. Cogswell and M. Z. Bazant, Suppression of phase separation in LiFePO\(_4\) nanoparticles during battery discharge, Nano Lett. 11:4890–4896, 2011. doi:10.1021/nl202764f H. L. Chan and D. Sutanto, A new battery model for use with battery energy storage systems and electric vehicle power systems, IEEE Power Eng. Soc. 1:470–475, 2000. doi:10.1109/PESW.2000.850009 T. Kim and W. Qiao, A hybrid battery model capable of capturing dynamic circuit characteristics and nonlinear capacity effects, IEEE T. Energy Conver. 26:1172–1180, 2011. doi:10.1109/TEC.2011.2167014 D. N. Rakhmatov and S. B. K. Vrudhula, An analytical high-level battery model for use in energy management of portable electronic systems, IEEE ICCAD 488–493, 2001. doi:10.1109/ICCAD.2001.968687 V. Srinivasan and J. Newman, Discharge model for the lithium iron-phosphate electrode, J. Electrochem. Soc. 151:A1517–A1529, 2004. doi:10.1149/1.1785012 V. Rao, G. Singhal, A. Kumar and N. Navet, Battery model for embedded systems, VLSI Des. 105–110, 2005. doi:10.1109/ICVD.2005.61 S. Dargavillez and T. W. Farrell, Predicting active material utilization in LiFePO\(_4\) electrodes using a multiscale mathematical model, J. Electrochem. Soc. 157:A830–A840, 2010. doi:10.1149/1.3425620 R. Rao, S. Vrudhula and D. N. Rakhmatov, Battery modeling for energy-aware system design, Computer 36:77–87, 2003. doi:10.1109/MC.2003.1250886 M. Chen and G. A. Rincon-Mora, Accurate electrical battery model capable of predicting runtime and i-v performance, IEEE T. Energy Conver. 21:504–511, 2006. doi:10.1109/TEC.2006.874229 L. Gao, S. Liu and R. A. Dougal, Dynamic lithium-ion battery model for system simulation, IEEE T. Compon. Pack. T. 25:495–505, 2002. doi:10.1109/TCAPT.2002.803653 V. Agarwal, K. Uthaichana, R. A. DeCarlo and L. H. Tsoukalas, Development and validation of a battery model useful for discharging and charging power control and lifetime estimation, IEEE T. Energy Conver. 25:821–835, 2010. doi:10.1109/TEC.2010.2043106 B. Schweighofer, K. M. Raab and G. Brasseur, Modeling of high power automotive batteries by the use of an automated test system, IEEE T. Instrum. Meas. 52:1087–1091, 2003. doi:10.1109/TIM.2003.814827

Electromechanical Behavior of Large-Format Prismatic Lithium-Ion Batteries

Stress and strain evolution, both throughout the manufacturing process and in response to electrical loads during cell operation, may be important to the state-of-health of prismatic lithium-ion batteries for electric vehicle applications. At present the impacts of mechanical phenomena on battery performance are not very well understood. To avoid capacity fade incurred by operation near very low and very high charge states, many automotive batteries are designed to have a great deal of excess capacity, and to operate in a limited range of charge states.(1)Understanding relationships between mechanical effects at various length scales (electrode, cell, and pack) may inform the design of control schemes that will allow more utilization of battery materials.At every stage of automotive battery-pack assembly, the respective components are subjected to large stresses.(2) Intercalation-deintercalation stresses that evolve during electrical cycling can cause volume expansion and fracture in the active particles, leading to degradation processes in the electrodes that ultimately reduce cycle life of the battery. Failure mechanisms such as cracking and delamination of electrodes can make new surface area for solid-electrolyte interphase (SEI) formation.(3) Particle cracking can also reduce electrical contact with binders and conductivity-providing additives, isolating useful regions of active material and reducing apparent capacity over time.(4) Additional consequences of mechanical cycling include particle rearrangement and reduced pore volume, which can both decrease Li-ion mobility through porous electrode materials.(5) Mechanical effects can also be beneficial for battery health. During electrode fabrication, it is known that certain amounts of compression can lead to better-performing materials.(2) At the cell level, a constant compressive force is placed on the battery by fitting it inside of a rigid casing, which improves contact within individual layers of the jellyroll. On the pack level, compressing stacked battery cells within a module can help to reduce problems arising from vibration or other mechanical effects.Figure 1 shows a zero-displacement experiment, where the evolution of equilibrium stress within a constrained 3-cell battery pack is measured against SOC at a variety of ambient temperatures. In conjunction with zero-force (free-swelling) experiments, these data can be used to determine an effective bulk modulus of the battery cell, whose evolution over time may help to elucidate state-of-health. The data shown demonstrate quantitatively that thermal expansion and contraction contribute as much to mechanical forces within a battery pack as intercalation/deintercalation stresses, if not more. This effect is known but not well understood.We also will present dilatometry measurements on the electrode level. With these data and cell-level measurements in combination, it will be possible to elucidate the transfer functions that relate material properties within the battery to deformations of its exterior.REFERENCES 1. V. Ramadesigan, P. W. C. Northrop, S. De, S. Santhanagopalan, R. D. Braatz, V. R. Subramanian., Journal of The Electrochemical Society 159, R31-R45 (2012).2. Y. B. Yi, A. M. Sastry, C. W. Wang., Journal of Engineering Materials and Technology 128, 73-80 (2005).3. P. M. Gomadam, J. W. Weidner., Journal of The Electrochemical Society 153, A179-A186 (2006).4. X. Zhang, A. M. Sastry, W. Shyy., Journal of The Electrochemical Society 155, A542-A552 (2008).5. G. Sikha, B. N. Popov, R. E. White., Journal of The Electrochemical Society 151, A1104-A1114 (2004).

Invited Presentation: Lithium Dendrite Formation at the Interface Between Lithium Metal and PEO Complexed with Various Lithium Salts

Protected Lithium Electrode for Aqueous Lithium-Air Rechargeable BatteriesNobuyuki Imanishi Department of Chemistry, Mie University, Tsu , Japan The novelty of aqueous lithium air battery is the introduction of a protected lithium electrode using an impermeable lithium ion-conducting glass ceramics, Li1+x+y(Ti,Ge)1-xAlxP3-ySiyO12 (LTAP), which enables lithium metal anode feasible in the aqueous electrolyte with a narrow electrochemical window and boosts the specific energy density of this system coupling with a breathing air cathode [1]. The inherent drawback of LTAP is the high reduction tendency of its constituent, Ti4+ or Ge4+, in contact with lithium metal, which results in the generation of insulated reaction products. Several lithium-ion conducting interlayers have been proposed to address this issue, such as lithium nitride, lithium phosphorus oxynitride, polymer electrolytes as well as accustomed organic electrolytes. The two former materials are generally prepared by vapor deposition, which involves considerable cost and makes the preparation of large-sized cells difficult. In contrast, large-sized sheets of polymer electrolytes can be easily fabricated along with little concerns over lithium dendrites formation unlike the case for organic electrolytes.Protected lithium electrodes consisting of lithium metal anode, polymer electrolytes-based interlayers and LTAP are our proposed solution for the practical development of aqueous lithium air battery. However, two contradictory issues emerge; those are the lithium dendrite formation at the interface between lithium metal and polymer electrolyte interlayers and the rate capability of protected lithium electrodes.In our previous studies, the interfacial resistance between lithium metal and polymer electrolytes has been demonstrated a key factor in initiating lithium dendrite formation by addition of ionic liquids, oligomer ethers and/or nanofillers. All these additives can reduce the interfacial resistance substantially and prolong the onset time of lithium dendrite initiation. A prospective additive of tetraethylene glycol dimethyl ether was revealed to exhibit the onset time of ca. 13 h for protected lithium electrodes at 1.0 mA·cm-2. The anion of lithium salts complexed with polymer matrix was also identified to influence the dendrite initiation in our recent study, where bis (fluorosulfonyl) imide-FSI- exhibited the longest short-circuit time of symmetrical lithium pouch cell compared to other larger analogues.At high current densities, the onset time of lithium dendrite initiation is shortened and thus the capacity of protected lithium electrode that can deliver is limited. A protected lithium electrode capable of delivering a high areal capacity of 12 mAh·cm-2 or more at no less than 1.0 mA·cm-2 is critical to develop aqueous lithium air batteries for electric vehicle applications. However, the binary polymer electrolytes we are developing usually exhibit a low limiting diffusion current density and are prone to initiate lithium dendrite formation when the operating current density exceeds its inherent limiting diffusion current density due to the quick depletion of lithium cations and the accompanying instability of interface. Moreover, the low exchange current density for polymer electrolytes at the interface also limits the rate capability of protected lithium electrodes.Based on our previous findings, we are now envisaging a new concept for the lithium-ion conducting interlayer, such as solvent-in-salt systems and single-ion polymer electrolytes, to diminish the trade-off between the two aforementioned issues for protected lithium electrodes in the development of aqueous lithium air battery.[1] N. Imanishi and O. Yamamoto, Rechargeable lithium–air batteries: characteristics and prospects, Mater. Today, 17, 24 (2014).

Recursive modeling and online identification of lithium-ion batteries for electric vehicle applications

For safe and reliable operation of lithium-ion batteries in electric vehicles, the real-time monitoring of their internal states is important. The purpose of our study is to find an easily implementable, online identification method for lithium-ion batteries in electric vehicles. In this article, we propose an equivalent circuit model structure. Based on the model structure we derive the recursive mathematical description. The recursive extended least square algorithm is introduced to estimate the model parameters online. The accuracy and robustness are validated through experiments and simulations. Real-road driving cycle experiment shows that the proposed online identification method can achieve acceptable accuracy with the maximum error of less than 5.52%. In addition, it is proved that the proposed method can also be used to estimate the real-time SOH and SOC of the batteries.

Performance of LiFePO4/C and Carbon Fibers Composite as Cathodes in Lithium-Ion Batteries and Battery Modeling

Currently, the cathode material LiFePO4/C is the most promising active material for high-power lithium-ion batteries for electric vehicle applications and it has been observed that its response is also dependent on the nature of the carbon used as support. This paper presents the preliminary results from the synthesis of LiFePO4 supported on carbon fibers where phosphate and fibers are synthesized together at a single stage. The electrodes built with this material have a good electrochemical response with the typical behavior of LiFePO4. In addition, the behavior of these materials has been characterized from the viewpoint of the electrochemistry of charge/discharge processes in order to provide knowledge for the global modelling of batteries. A Battery Management System (BMS) is also currently being developed in order to control the operating conditions and the state of charge of Li-ion batteries. The development of a BMS requires an adequate modeling of the dynamic accumulation processes taking place in the battery. A general model has been derived and validated in terms of the experimental performance of a Li-ion battery and some results are presented below.

Three-Dimensional Modeling the Thermal Behavior of a Lithium-Ion Battery for Electric Vehicle Applications

not Available.