Lithium Iron Phosphate Cells Research Articles

The impact of multi-layered porosity distribution on the performance of a lithium ion battery

This study investigates the impact of a multi-layered porosity profile on the electrical and thermal performance of a lithium-ion battery. Consideration is given to key attributes of the battery, namely its specific power and energy and the temperature distribution that may generated throughout the cell under electrical load. The COMSOl Multiphysics software tool has been employed to develop a 3D electrochemical–thermal model of a commercially available 10 Ah lithium iron phosphate cell. Through an extensive simulation study, for a fixed value of active material, the impact of varying the porosity profile across both the thickness and height of the electrode has been studied. For each case study, the distribution of reaction current and the corresponding localised state of charge and temperature profile are quantified for a constant current discharge of 5C. Simulation results highlight that a multi-layered porosity distribution across the thickness of the electrode has the potential to yield superior battery performance compared to when the porosity is varied along the electrode height. Moreover, the total heat generation within the cathode may be reduced by up to 14% compared to a Reference Case, along with 0.33% and 0.44% improvement in the specific energy and power, respectively.

X-ray computed tomography comparison of individual and parallel assembled commercial lithium iron phosphate batteries at end of life after high rate cycling

X-ray computed tomography (X-ray CT) across multiple length scales is utilized for the first time to investigate the physical abuse of high C-rate pulsed discharge on cells wired individually and in parallel.. Manufactured lithium iron phosphate cells boasting high rate capability were pulse power tested in both wiring conditions with high discharge currents of 10C for a high number of cycles (up to 1200) until end of life (<80% of initial discharge capacity retained). The parallel assembly reached end of life more rapidly for reasons unknown prior to CT investigations. The investigation revealed evidence of overdischarge in the most degraded cell from the parallel assembly, compared to more traditional failure in the individual cell. The parallel-wired cell exhibited dissolution of copper from the anode current collector and subsequent deposition throughout the separator near the cathode of the cell. This overdischarge-induced copper deposition, notably impossible to confirm with other state of health (SOH) monitoring methods, is diagnosed using CT by rendering the interior current collector without harm or alteration to the active materials. Correlation of CT observations to the electrochemical pulse data from the parallel-wired cells reveals the risk of parallel wiring during high C-rate pulse discharge.

Covariance controlled state-of-charge estimator of LiFePO4 cells using a simplified hysteresis model

Equivalent circuit model (ECM)-based state-of-charge (SOC) estimation has been considered as one of the most important aspects in battery management system (BMS). However, in case of a lithium iron phosphate (LiFePO4) cell, because of the flatness and hysteresis effect of the open-circuit voltage (OCV) curve, there are inevitable drawbacks directly related to both erroneous SOC information and the slow SOC convergence speed caused by incorrect OCV characteristics. Therefore, this approach gives insight to the design and implementation of the ECM-based SOC estimator that is suitable for an actual LiFePO4 cell. Two approaches for settlement in current OCV issues are as follows. Firstly, through linearization between coordinated charging and discharging OCVs, an OCV hysteresis model can be easily implemented. This model incorporating OCV measurement data is adequately applied to the model-based SOC estimator using the extended Kalman filter (EKF). Secondly, a well-adjusted measurement error covariance controlled in the EKF is used to alleviate an undesired SOC fluctuation that surely results in low BMS performance. This measurement error covariance additionally enables us to provide the fast SOC convergence speed against an inaccurate initial SOC value. This approach has been sufficiently validated by extensive experimental results conducted on LiFePO4 cells that had a rated capacity of 14 Ah by EIG. Consequently, our validation showed the clearness of the proposed work for a reliable SOC estimator of a LiFePO4 cell.

Graphical Interpretation of the Extended Kalman Filter: Estimating the State-of-Charge of a Lithium Iron Phosphate Cell

Electric vehicles (EVs) fall in line with a new ideology of less waste and more conscious usage of resources, slowly picking up speed. In this context, energy storage is of paramount i ...

Tracking degradation in lithium iron phosphate batteries using differential thermal voltammetry

Diagnosing the state-of-health of lithium ion batteries in-operando is becoming increasingly important for multiple applications. We report the application of differential thermal voltammetry (DTV) to lithium iron phosphate (LFP) cells for the first time, and demonstrate that the technique is capable of diagnosing degradation in a similar way to incremental capacity analysis (ICA). DTV has the advantage of not requiring current and works for multiple cells in parallel, and is less sensitive to temperature introducing errors. Cells were aged by holding at 100% SOC or cycling at 1C charge, 6D discharge, both at an elevated temperature of 45 °C under forced air convection. Cells were periodically characterised, measuring capacity fade, resistance increase (power fade), and DTV fingerprints. The DTV results for both cells correlated well with both capacity and power, suggesting they could be used to diagnose SOH in-operando for both charge and discharge. The DTV peak-to-peak capacity correlated well with total capacity fade for the cycled cell, suggesting that it should be possible to estimate SOC and SOH from DTV for incomplete cycles within the voltage hysteresis region of an LFP cell.

State of charge estimation for battery packs using H-infinity observer in underground mine electric vehicles

ABSTRACTAn H-infinity observer (HIO) for state of charge (SOC) estimation is proposed for a series-connected battery pack in underground mine electric vehicles (UMEVs). In the proposed method, an average virtual cell (AVC) model is defined and the SOC of the AVC model is estimated to represent the pack SOC when all terminal voltage differences (TVDs) between each individual cell in the pack and the AVC are within a pre-set voltage threshold. To make sure all the TVDs are within the threshold, lithium iron phosphate cells are clustered into the group with similar characteristics and the cells in the same group are used to build the sorted battery pack. The experiments of pulse current and current profiles based on both urban dynamometer driving schedule and underground mine driving cycle are conducted on the sorted battery pack to verify the effectiveness of the proposed HIO for the pack SOC estimation under UMEVs.

Electrochemical balancing of lithium-ion cells by nickel-based cells

This paper presents a topology to passively balance two types of lithium-ion cells without using additional active electrical components or employing any algorithm based balancing management systems. We connect nickel-metal hydride or nickel-zinc cells in parallel to lithium-ion cells. The hydrogen/oxygen recombination process of the nickel-based cells can be used to balance lithium-ion cells. Two serially connected lithium iron phosphate cells, combined each with two serially connected nickel-zinc cells, show a successful balancing of 8% of the lithium iron phosphate cell's initial capacity. A lithium titanate cell connected in parallel to two nickel-metal hydride cells can be fully charged without suffering from over-voltage effects by constant current charging.

A Detailed-Electrochemistry Model of an LiFePO4/Graphite Lithium-Ion Cell Capturing Abuse and Aging Phenomena

Numerical simulations are an important corner stone for the development of lithium-ion batteries as they can save expenses in time and cost for a large number of series of experiments. Empirical models have the advantage of a fast development and a low computational effort, but their predictions are only valid within the range of experimental data used for fitting. In contrast, physically-based models allow a deeper understanding and increase the applicability to different scenarios and the credibility of the model predictions. We present a multi-scale modelling approach coupling a 1D thermal cell-level (macro-scale) model, a 1D electrode-pair level (micro-scale) model, and a 1D particle-level (nano-scale) model (Figure 1) 1. A particular feature of the model is detailed electrochemistry, capturing multi-step main and side reactions occurring during ageing or thermal runaway. The model is parametrized to commercial lithium iron phosphate cells. We assume solid electrolyte interphase (SEI) formation at the anode as dominant aging mechanism, which is known to be the main contributor to calendaric aging 2. By capturing the nonlinear feedback between electrochemistry and transport, the model is able to simulate cell behavior not only within recommended operating conditions, but also during cell abuse, for example, due to external short circuit, and during long-time operation. Simulations of short time (external short circuit) and long time (lifetime estimation) scenarios are presented (Figure 2,3). These different events are compared and interpreted, for example in terms of reaction limitation or transport limitation. The sensitivity and reassessment of the model parameter is discussed, and the model as a comprehensive copy of the real battery is verified. Thereby insights into the battery which are experimentally hard to obtain, like concentration and temperature gradients or the distribution of lithium in the active material (Figure 4), are more justified than before.

Semi-Empirical Modeling of Temperature-Dependent Degradation Mechanisms in Lithium-Ion Batteries

Today, stationary energy storage systems utilizing lithium-ion batteries account for the majority of new storage capacity installed. In order to meet technical and economic requirements, the specified system’s lifetime has to be ensured. For reliable lifetime predictions, models for cell degradation are necessary. Physicochemical models that include ageing mechanisms are based on a detailed set of parameters, which are often not available, and require parametrization of the degradation rates. Instead, purely empirical models can be parametrized without knowledge of internal cell setup through extensive testing, however are prone to extrapolation errors due to the utilized mathematical functions. In this work, a semi-empirical model based on a reduced set of internal cell parameters and physically supported degradation functions is used (1). Due to the limited knowledge about degradation mechanisms, purely empirical models as well as semi-empirical models tend to lump multiple degradation effects into single functions. This leads to prediction errors when deviating from the parametrization test conditions. E.g. for cycle ageing, Waldmann et al. reported a transition of dominating ageing mechanisms at 25°C (2). The ageing above 25°C was attributed to SEI-growth and cathode degradation, for temperatures below 25°C to lithium plating. Model development therefore should aim for a separation of the degradation mechanisms where possible. The respective mechanisms then can be modelled through functions that are suitable for the degradation driving factors, which is the focus of this work: A semi-empirical cell degradation model is developed that separates one calendar and several cycle ageing effects. Emphasis is placed on the varying degradation at different temperatures. Degradation for cycle ageing at high and low temperatures is thus calculated separately, a novel approach in semi-empirical models. As such models require parametrization, a lifetime test study is conducted. Cell type and experimental parameters are in accordance with an application in stationary systems. Lithium iron phosphate batteries have shown capacity retention for more than 5,000 full cycles before usable capacities fall below 80%, which is suitable for stationary applications (3). The parametrization is based on a commercial 26650 lithium iron phosphate cell with a nominal capacity of 3 Ah. The lifetime study is separated into parametrization and validation tests. Validation tests are excluded from model parametrization and thus show the model performance in deviating, more realistic conditions. Temperatures in the tests are 0°C, 10°C, 15°C, 25°C, 35°C, 45°C and 55°C to accurately describe varying degradation mechanisms. Parametrization for calendar ageing consists of storage tests of cells at varying State of Charge (SOC) in steps of 12.5%. Cycle ageing parametrization is based on constant current full cycle tests at C-Rates of 0.5C, 1C and 1.7C with and without CV-phases at the end of charging. For validation, a dynamic profile representative for a summer and a winter day of a residential photovoltaic-battery system is tested. Regular characterization tests were performed at 25°C. As experimental results for the degradation in terms of capacity loss showed to be more severe than an impedance increase, we focused on modelling the battery capacity. Calendar ageing is modelled with temperature and anode-potential dependency based on the storage tests. Figure 1a shows the Arrhenius-dependency of the capacity loss rate at SOC of 100% for temperatures between 10°C and 45°C. Figure 1b shows the relation between anode open circuit potential and calendar ageing at a storage temperature of 25°C for SOC between 0% and 100%. For cycle ageing, degradation mechanisms are separated in low and high temperature effects and parametrized based on the constant-current tests. Cell degradation can thus be given as the linear combination of separate mechanisms. Figure 2 shows the cycle-induced capacity loss rate occurring additionally to comparative storage tests over temperatures from 0°C to 55°C. High and low temperature mechanisms are separately calculated. Summarizing, the model enables a cell degradation simulation over a wide temperature range. Through independent, yet temperature-dependent calculation of calendar and cycle ageing effects detailed studies on the effect of thermal management are enabled. Details on the prediction performance against the validation tests and model limitations are given in the presentation. Finally, model results for real-world grid applications as well as strategies for optimized operation temperatures are presented and evaluated.

Experimental analysis of Dynamic Charge Acceptance test conditions for lead-acid and lithium iron phosphate cells

This paper presents the results of a series of tests to determine the Dynamic Charge Acceptance (DCA) performance of small form-factor carbon-enhanced VRLA cells designed for use in Hybrid Electric Vehicle (HEV) applications, together with standard lead-acid and lithium iron phosphate (LFP) cells. The results demonstrate how varying the conditions and parameters of the standard DCA test regime can provide a superior evaluation of DCA performance and lead to a better understanding of cell behaviour under real-world conditions. A modified test procedure is proposed, based on the DCA Short Test profile (EN50342-6). Results are presented for a batch of carbon-enhanced cells, tested at various temperatures, rest periods and States of Charge (SoC) for the cell. These conditions having been chosen to mimic a range of real-life scenarios which could potentially be encountered during HEV operation. The resulting analysis demonstrates clear variations and trends in DCA performance which may be used to inform conditions for future testing regimes. The modified test procedure is then applied to standard lead-acid and LFP 26650-type cells and the results compared.

State of Charge and State of Health Estimation Using Electrochemical Acoustic Time of Flight Analysis

Ultrasonic analysis was used to predict the state of charge and state of health of lithium-ion pouch cells that have been cycled for several hundred cycles. The repeatable ultrasonic trends are reduced to two key metrics: time of flight shift and total signal amplitude, which are then used with voltage data in a supervised machine learning technique to build a model for state of charge (SOC) prediction. Using this model, cell SOC is predicted to ∼1% accuracy for both lithium cobalt oxide and lithium iron phosphate cells. Elastic wave propagation theory is used to explain that the changes in ultrasonic signal are related to changes in the material properties of the active materials (i.e., elastic modulus and density) during cycling. Finally, we show the machine learning model can accurately predict cell state of health with an error ∼1%. This is accomplished by extending the data inputs into the model to include full ultrasonic waveforms at top of charge.

Equalisation strategy for serially connected LiFePO 4 battery cells

Owing to different temperature gradients, charge/discharge rates, and coulomb efficiencies, various releasable capacities of each cell have significant impacts on a pack capacity. Therefore, the equalisation system is an essential part in a battery pack, especially for a high-power application such as electric vehicle. In this study, the authors first review several traditional equalisation strategies. Subsequently, a new equalisation strategy (ES) based on the real `state' of a battery is proposed. They further establish the destination state-of-charge versus open-circuit voltage (SOC-OCV) cluster which is the target of the ES. Moreover, not only each SOC in serially connected battery cells, but also the SOC error according to the destination SOC-OCV cluster can be estimated. To avoid over-equalisation, the SOC error caused by the dispersion of OCVs is taken into account during the calculation of equalisation SOCs. A battery pack with five lithium iron phosphate cells in series is employed to verify the method, and results show that the capacity of battery pack is increased by 11% with equalisation.

Advanced binary search pattern for impedance spectra classification for determining the state of charge of a lithium iron phosphate cell using a support vector machine

Abstract. Further improvements on the novel method for state of charge (SOC) determination of lithium iron phosphate (LFP) cells based on the impedance spectra classification are presented. A Support Vector Machine (SVM) is applied to impedance spectra of a LFP cell, with each impedance spectrum representing a distinct SOC for a predefined temperature. As a SVM is a binary classifier, only the distinction between two SOC can be computed in one iteration of the algorithm. Therefore a search pattern is necessary. A balanced tree search was implemented with good results. In order to further improvements of the SVM method, this paper discusses two new search pattern, namely a linear search and an imbalanced tree search, the later one based on an initial educated guess. All three search pattern were compared under various aspects like accuracy, efficiency, tolerance of disturbances and temperature dependancy. The imbalanced search tree shows to be the most efficient search pattern if the initial guess is within less than ±5 % SOC of the original SOC in both directions and exhibits the best tolerance for high disturbances. Linear search improves the rate of exact classifications for almost every temperature. It also improves the robustness against high disturbances and can even detect a certain number of false classifications which makes this search pattern unique. The downside is a much lower efficiency as all impedance spectra have to be evaluated while the tree search pattern only evaluate those on the tree path.

Modeling of battery dynamics and hysteresis for power delivery prediction and SOC estimation

A modeling approach for battery as an Electrical Energy Storage System is proposed in this paper. The model aims to predict non-linear power delivery dynamics, given charge and discharge demand as a controllable input, not only in normal operating range of batteries, but also in extreme cases such as battery over-charging. In order to achieve that, the model is composed of separated voltage and current models. Several non-linear models, including Hammerstein model, non-linear open-circuit voltage characteristics, and Takacs hysteresis model are combined in the voltage and the current model, respectively. The state of charge of the battery can also be estimated in a recursive optimization fashion by the model. The parameterization and estimation methods of the model are described and also demonstrated on experimental data from a lithium iron phosphate (LiFePO4) cell. The experiment validation shows excellent agreement between measured and simulated voltage and current signals provided by the model during both normal operating and over-charging conditions. The contribution of this paper is given by the unique combination of data-based models used to capture linear dynamics, static non-linearity, and non-linear hysteresis effects in a single dynamic voltage/current model to simulate and predict the non-linear dynamic behavior of a battery as an energy storage/delivery system.

Characterizing rapid capacity fade and impedance evolution in high rate pulsed discharged lithium iron phosphate cells for complex, high power loads

Abstract Three 26650 LiFePO4 (LFP) cells are cycled using a 40 A pulsed charge/discharge profile to study their performance in high rate pulsed applications. This profile is used to simulate naval pulsed power loads planned for deployment aboard future vessels. The LFP cells studied experienced an exponential drop in their usable high-rate recharge capacity within sixty cycles due to a rapid rise in their internal resistance. Differential capacitance shows that the voltage window for charge storage is pushed outside of the recommended voltage cutoff limits. Investigation into the state of health of the electrodes shows minimal loss of active material from the cathode to side reactions. Post-mortem examination of the anodic surface films reveals a large increase in the concentration of reduced salt compounds indicating that the pulsed profile creates highly favorable conditions for LiPF6 salt to break down into LiF. This film slows the ionic movement at the interface, affecting transfer kinetics, resulting in charge buildup in the bulk anode without successful energy storage. The results indicate that the use of these cells as a power supply for high pulsed power loads is hindered because of ionically resistant film development and not by an increasing rate of active material loss.

Internal short circuit and accelerated rate calorimetry tests of lithium-ion cells: Considerations for methane-air intrinsic safety and explosion proof/flameproof protection methods

Researchers with the National Institute for Occupational Safety and Health (NIOSH) studied the potential for lithium-ion cell thermal runaway from an internal short circuit in equipment for use in underground coal mines. In this third phase of the study, researchers compared plastic wedge crush-induced internal short circuit tests of selected lithium-ion cells within methane (CH4)-air mixtures with accelerated rate calorimetry tests of similar cells. Plastic wedge crush test results with metal oxide lithium-ion cells extracted from intrinsically safe evaluated equipment were mixed, with one cell model igniting the chamber atmosphere while another cell model did not. The two cells models exhibited different internal short circuit behaviors. A lithium iron phosphate (LiFePO4) cell model was tolerant to crush-induced internal short circuits within CH4-air, tested under manufacturer recommended charging conditions. Accelerating rate calorimetry tests with similar cells within a nitrogen purged 353-mL chamber produced ignitions that exceeded explosion proof and flameproof enclosure minimum internal pressure design criteria. Ignition pressures within a 20-L chamber with 6.5% CH4-air were relatively low, with much larger head space volume and less adiabatic test conditions. The literature indicates that sizeable lithium thionyl chloride (LiSOCl2) primary (non rechargeable) cell ignitions can be especially violent and toxic. Because ignition of an explosive atmosphere is expected within explosion proof or flameproof enclosures, there is a need to consider the potential for an internal explosive atmosphere ignition in combination with a lithium or lithium-ion battery thermal runaway process, and the resulting effects on the enclosure.

On-board capacity estimation of lithium iron phosphate batteries by means of half-cell curves

This paper presents a novel methodology for the on-board estimation of the actual battery capacity of lithium iron phosphate batteries. The approach is based on the detection of the actual degradation mechanisms by collecting plateau information. The tracked degradation modes are employed to change the characteristics of the fresh electrode voltage curves (mutual position and dimension), to reconstruct the full voltage curve and therefore to obtain the total capacity. The work presents a model which describes the relation between the single degradation modes and the electrode voltage curves characteristics. The model is then implemented in a novel battery management system structure for aging tracking and on-board capacity estimation. The working principle of the new algorithm is validated with data obtained from lithium iron phosphate cells aged in different operating conditions. The results show that both during charge and discharge the algorithm is able to correctly track the actual battery capacity with an error of approx. 1%. The use of the obtained results for the recalibration of a hysteresis model present in the battery management system is eventually presented, demonstrating the benefit of the tracked aging information for additional scopes.

Novel Method for Using Force in Incremental Capacity Analysis for Capacity Fading Estimation

Health monitoring techniques in batteries rely on voltage measurements. Several methods haven been introduced in literature for the evaluation of the aging in battery. In Cyclic Voltammetry (CV), the electrode potential is increased linearly with time. The resulting cyclic voltammogram is a plot of current vs. voltage. The shift in the plot peaks is correlated with aging. Statistical methods can also be used to track aging by fitting a probability density function (PDF) to a histogram of voltage data during charge/discharge. As the cell degrades, the PDF curve shifts which allows for aging detection. Another widely known method is the differential voltage (DV) method. The method plots the differential of voltage over capacity (dV/dQ) with respect to capacity and monitors peak shifting to relate with degradation. The inverse of the DV method is the incremental capacity analysis (ICA). In many cell chemistries, the cells are characterized by a voltage plateau for a wide range of SOCs. The ICA method plots the incremental capacity over voltage (dQ/dV) with respect to voltage, which allows for clearly identifiable peaks that correlate with capacity fading. This method has been recently shown to predict capacity fading with less than 1% error (1). Although the ICA method has been shown to be accurate in estimating capacity fade, it still has some major drawbacks. The method is sensitive to voltage measurement noise and accuracy since dV is small. This is especially the case in Lithium iron phosphate (LFP) cells, which are characterized by flat voltage curves. A second limitation of ICA arises from the peak location. In the case of an NMC cell, the ICA peaks in discharge are centered around 40% SOC. This means that the battery has to operate in the low SOC range in order to estimate and monitor the peak location and hence the capacity fading. Here we report a novel method of monitoring electrode expansion in the ICA method. The method uses force measurements to derive the IC (incremental capacity) curves. In lithium-ion batteries, charging causes volume change or swelling of the electrodes as the lithium ions intercalate in the negative electrode. In applications where the batteries are constrained or compressed to prevent expansion, the swelling causes a measurable stress. This stress can be measured using a force sensor (or strain gauge). Experiments are performed on 4 different fixtures each containing 3 battery cells, clamped together between two end plates (Figure 1). The fixtures are operating under an aggressive power cycle (Imax = 36 C-rate, Iavg = 12 C-rate) centered at various SOC ranges and preloaded under two different forces. The degradation of the cells was used to develop a model of the peak location as a function of capacity. Figure 2 shows the model error in estimating capacity fade based on the peak location using the measured force, coulomb counting, and voltage. As shown in Figure 2, the incremental capacity analysis based on force measurements (ICF) is promising since it can be used to predict capacity fade with 0.42% accuracy. Also, in the case of an NMC cell, the ICF peaks occur around 70%SOC while those of the ICV occur around 40%SOC (Figure 1). Hence the proposed SOH monitoring could update estimates more frequently within the regular use of an electric vehicle in urban and most driving conditions, given that drivers typically spend more time in the higher battery SOCs than in the lower ones. It is also shown that bulk force measured on the fixture holding 3 different cells balanced within 3% capacity can be used to estimate individual cell capacities of each fixture when individual cell voltages are measured. Results show that the maximum error is 3.1% with an absolute average and standard deviation on the error of 0.42% and 1.14% respectively as shown in Figure 2. Future work would include implementing this method in on-board state of health monitoring prognostic algorithms. Acknowledgment The information, data, or work presented herein was funded in part by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR00002691.

Optimization and experimental validation of a thermal cycle that maximizes entropy coefficient fisher identifiability for lithium iron phosphate cells

This article presents a framework for optimizing the thermal cycle to estimate a battery cell's entropy coefficient at 20% state of charge (SOC). Our goal is to maximize Fisher identifiability: a measure of the accuracy with which a parameter can be estimated. Existing protocols in the literature for estimating entropy coefficients demand excessive laboratory time. Identifiability optimization makes it possible to achieve comparable accuracy levels in a fraction of the time. This article demonstrates this result for a set of lithium iron phosphate (LFP) cells. We conduct a 24-h experiment to obtain benchmark measurements of their entropy coefficients. We optimize a thermal cycle to maximize parameter identifiability for these cells. This optimization proceeds with respect to the coefficients of a Fourier discretization of this thermal cycle. Finally, we compare the estimated parameters using (i) the benchmark test, (ii) the optimized protocol, and (iii) a 15-h test from the literature (by Forgez et al.). The results are encouraging for two reasons. First, they confirm the simulation-based prediction that the optimized experiment can produce accurate parameter estimates in 2 h, compared to 15–24. Second, the optimized experiment also estimates a thermal time constant representing the effects of thermal capacitance and convection heat transfer.

Scalable Energy Storage Systems for Effective Electrified Mobility Concepts

State of the art electrical energy storage systems for passenger cars and commercial vehicles use one type of cell to set up the module and pack level of the battery. The cell type is selected with respect to the specific application and its electrical and mechanical requirements. The number of cells in parallel and in series is defined by the needed energy, power and voltage within the electric power train. Hybridization concepts on battery system level enhance the degree of freedom towards power and energy scalability plus total cost of ownership and battery efficiency advantages. Hybridization here means to use two cell types each one optimized for energy content or power capability to be integrated in specific high power and high energy modules. Finally one has the opportunity to scale power and energy performance on vehicle level. Within this paper the companies AVL and Bosch present their results generated within the European project SuperLIB. The focus is on the evaluation and discussion of general pros and cons for this concept including simulation and hardware test results. Within SuperLIB power and energy optimized lithium iron phosphate cells were used to demonstrate the respective concept for hybridization on module level.