Li-Ion Pack Selection for Regional Hybrid Airliner

Li-Ion Pack Selection for Regional Hybrid Airliner Colin Grossman-Cross, Aliaksei Hauryliuk, and Douglas Schmidt Acknowledgements to Zunum Aero Inc., Venkat Viswanathan, Shashank Sripad Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213 Abstract Modern hybrid aircraft currently under development require high-output battery cells to enable flights up to 1,100 km. Even with turbo-generator loadsharing throughout the flight, high power draw and long range of even small airliners require high capacity, high rate of discharge packs. With several flight path and performance parameters specified by Zunum Aero, we developed a model for selecting lithium-ion cells and evaluating their performance and degradation over their anticipated life. Several battery chemistries were considered, and a convective heat model was tested for packs located in the aircraft wings. Performance is compared for different cell component materials, and variations in pack surface area over an 800km flight.

1

Introduction

Traditional fixed-wing aircraft, over the course of their century-long development period, have been thoroughly optimized for internal combustion engines. They are designed for high power takeoff, medium power cruise, and low power descent/landing regimes, with the combination of the airframe and the power plant dictating flight profiles. However, as the internal combustion engine is slowly losing its economic viability, some in the aviation industry are looking for alternatives. Many are moving toward electric aircraft. Increasing battery power densities and improvements in electric motors are pushing such flight architectures to become ever more competitive with traditional ones.

This project investigates Zunum Aeros hybrid electric regional airliner. In our assessment, we investigate different pack chemistries for a typical flight profile. Specifically, we will consider and attempt to size a pack that can meet power and charge density requirements as put forward by Zunum Aero over a representative 800km flight. The performance of these packs will be examined with an emphasis on degradation analysis and extending the maximum useful pack life.

Zunum Aero Prototype Aircraft

2

Methods

Battery selection depends primarily on weight, maximum power requirement, and total capacity required per application. To that end, given a flight of varying distance, cruise altitude, rate of climb, etc., our team needed a flexible model for converting physical flight paths into power, airspeed, and altitude profiles. Next, with the power requirements for a specific distance flight calculated, we fit several battery packs to analyze performance in pack heat dissipation, degradation over 1000 flight and charge cycles, pack size, and pack weight.

Using battery simulation software, AutoLion ST, we created battery profiles and charge/discharge cycling functions for use with Matlab Simulink. We then simulated full flights for a given pack with and without our convective heat dissipation model. Upon completion of a functional coupled battery and thermal model, several pack chemistries and configurations were tested.

2.1

Flight Profile Dynamic Solver

Several M AT LAB T M functions were developed for generating dynamic flight paths for fixed-wing aircraft.

Before focusing on the Zunum Aero aircraft, the model was developed using published performance

specifications for a similarly sized B-100 Beechcraft King Air. The values for internal combustion, fixed wing aircraft can be found in table 3.

For given airframe characteristics and dynamic model, blade efficiency coupled with forward aircraft velocity yields output thrust per given power consumption. Integrating power over the duration of flight yields total power consumption, which determines the amount of fuel needed for turbogenerator load sharing. The original attempts were to broadly classify architectures by flight path, which required roughly constructed flight profiles.

Our thrust regimes followed the fixed-wing dynamics of Falck, et al [2] and can be found in Fig 1. For each segment of the flight, constraints on path geometry were set according to FAA standards and well established practices for conventional propulsion flight. Flights were considered on the range of 100km to 1,300km to encompass the predicted ranges of flight for the Zunum design.

Falck et al. Dynamic Flight Model With a full velocity and acceleration profile, the next step is to develop a force balance at each time step. For example, a constant velocity pitch downward from ascent into level cruise will necessitate an accompanying change in X and Y velocity. This normal acceleration for direction change, while potentially increasing drag minimally, requires no additional thrust. In fact, leveling off has the effect of reducing the thrust required for a given velocity. Our model takes into account these dynamic thrust requirements by categorizing each flight regime, and applying a specific force balance. We utilized two-dimensional equations of motion formally proposed in Trajectory Optimization of Electric Aircraft Subject to Subsystem Thermal Constraints, Falck et al.

[2] Specifically, the advance ratio J and propeller efficiency η provide performance maps if propeller characteristics are well known.

Control within the parameters set by the profiles was difficult to achieve due to large system inertia and dynamic constraints. To resolve these issues, a dynamic physical model with simplified constraints was used to procedurally generate a power profile for a regulation flight.The profile is built up from two main physical models. The first was an adaptation of the 2D flight dynamics described in Falck et al. Unlike Falck et al.

However, the flight equilibrium constraint was removed to allow for dynamic transition between certain flight phases, such as during takeoff rotation or level-off followed by a climb to altitude. This had the additional effect of reducing the number of flight variables required and allowed the testing of limitations imposed by the aircraft itself.

2.2

Pack Sizing

The required pack capacity, as determined from our flight profile generator was 471kWh, with a maximum current draw at roughly 1.2C during takeoff and ascent. A 2.0C criteria was established for pack selection to: • Avoid operating the pack near maximum discharge rate during each flight • Limit heat generation by the pack during power-intensive portions of the flight • Limit pack degradation per flight cycle, especially due to high current and high temperature effects • Specify the pack for a higher voltage, likely needed by the 1MW motors specified by Zunum Aero Cell selection and sizing rationale for subsequent chemistries can be found in Table 1.

Since we could easily adjust pack voltage and capacity by varying the number and arrangement of cells in series and parallel configuration, we picked a reasonable initial pack voltage for baseline calculations. 500V was

selected as a low starting point. The first battery was selected with Nickel Cobalt Manganese 1:1:1 (NCM111 ) concentration as the cathode material and Graphite for the anode.

Other chemistries and pack voltages were selected to achieve lower mass and optimal surface area for cooling.

2.3

Thermal Model

We implemented a two phase convective heat transfer model for batteries housed in the wings. During flight, battery pack heat generation conducts through a thin, aluminum composite (Al 2099) wing material coupled with high velocity, turbulent convection over the wing surface. To simplify approximations, turbulence onset was assumed at the leading edge of the wing. In addition, a simplifying flat-plate approximation was selected to allow simple, analytical calculations within our Simulink loop.

Bergman [1], The flat plate in parallel flow and free convection During charging, a second thermal model allows for free-convective cooling between standard sea-level temperature and a heated battery pack. Although initial simulations indicated that expected gradients between the pack and atmospheric temperatures after flights and during charging rarely exceeded +/- 20 degrees Celcius, our model should fit less thermally stable chemistries.

2.4

Simulink Model

While AutoLion ST simulation software provides stock cycling and insulation models, these models proved inadequate for the case of aircraft battery simulation. To address this, a custom cycling function was generated

based on the power data provided by Zunum Aero. Additionally, altitude data (provided by Zunum Aero) was processed to output thermal properties of air at a particular point in the flight. This was used to calculate forced convection away from the battery based on the altitude and velocity data at a particular time step. To prevent continuous cycling (an unrealistic scenario for this application) as well as to allow the battery to equilibrate with the environment once per cycle, a 15-minute rest period was added between the charge and discharge cycles. Using the model shown in Fig.1, the battery pack was charged for a full flight, allowed to rest, then discharged in flight. This cycle was repeated one thousand times. This is equivalent to three years of once per day maximum-range flights.

3 3.1

Results and discussion Zunum Aero Power Profile

The power profile provided by Zunum Aero (Fig.7) is for a hybrid electric aircraft. It employs the use of a range extender in the form of a turbogenerator to ease the power load on the battery during take off and the majority of the cruise segment. Looking at Fig. 7, the turbogenerator provides a constant amount of power.

Therefore during take off, the battery must provide the extra power required to lift the aircraft and increase its altitude. As the take off segment transitions into cruise, battery power output drops because the turbogenerator is able to provide most of the power required to sustain cruising altitude. However, towards the end of the flight, the turbogenerator runs out of fuel and is unable to provide any more power. This immediate loss of power is made up for by the battery, requiring the largest power load of the entire flight during the landing segment. This power profile is what was used for our pack analysis.

3.2

Intial Simulation of NCM 111 Pack

The initial simulation was conducted for an NCM 111 pack with a stock thermal model (constant h ¡ A = 100 W K ), sized for 471 kWh and a 500 V OCV. The pack was cycled without a rest period. The cycling profile observed can be seen in (Fig.2). As shown in the figure, the power spikes around takeoff and generator shutdown contribute the most salient features to the profile. The power spike at the end causes a voltage drop as high as 15% compared to its full SoC OCV. This should be noted as such a voltage drop can significantly decrease motor speed right before the crucial landing cycle. Even upon reaching cruise, an 8% voltage drop is observed. This means that the power profile presented by Zunum Aero (Fig.7) will likely require modification to deal with rapidly decreasing battery voltage during the high-power takeoff cycle.

Figure 5 The degradation profile, shown in (Fig.5) indicates a rather high rate of capacity loss for this configuration.

Over 1000 cycles, the end capacity is shrunk by 18%, nearly a fifth of the capacity. Looking at the temperature mAh profile (Fig.4), it stands to reason that the high rate of capacity loss (around 1.28 cell¡cycle ) could be due to thermal degradation. To address this, an improved thermal was developed to analyze pack degradation within more reasonable temperature bounds.

3.3

Improved Thermal Model

Although the pack does not meet the weight specification put forward by Zunum Aero, it was decided to use NCM 111 as a test case for a thermal model. The obtained degradation data would be compared against the data for the stock cooling model, which was already available.

The thermal model, represented in Fig.1 was initially configured for a worst-case cooling scenario, with only 0.5m2 of the pack exposed to forced convection. To address the lack of forced convection while charging, a 15-minute rest period was added to let the battery equilibrate with the surroundings.This resulted in the characteristic cycle profile seen in Fig.3.

Figure 6: Degradation behaviour of NCM 111 under two different thermal models As seen in Fig.6, even with a small area of the pack exposed, there was a significant (23%) improvement in capacity after 1000 cycles. Most notably, there was a large reduction in loss due to the solid-electrolyte interface at the anode. This is a strong indicator that capacity degradation observed in the previous simulation was indeed due to thermal effects. Capacity degradation of the pack was decreased, however, temperature cycling was still occurring near 30o C. In an effort to observe the effects of cycling near a lower temperature, it was decided to simulate two more values for the cooled surface area.

3.4

Effects of Exposed Surface Area on Degradation

The values chosen for simulating the effect of surface area were 5 and 2.5 m2 . The former was chosen as it represented the area of the largest surface of the pack if the cells were arranged in two layers. Additionally, making the exposed area any bigger would be impractical given the spatial constraints of the wing. The latter represents four layers of cells, chosen to provide an in-between sampling point. The temperature range comparison plot (Fig.8) shows the effects of changing the exposed area on the minimum and maximum temperatures of the pack. None but the 2.5m2 packs managed to equilibrate with ambient every cycle. This suggests that the rest cycle is not long enough for free convection to either warm or cool the battery to ambient, or conversely, natural convection was not able to bring the pack to ambient within a 15-minute window.

As expected, an increase in the exposed surface area lowers both the maximum and minimum temperatures in the cycle. Of note is the fact that increasing surface area also increases the temperature range experienced during the cycle. A battery operating with 5m2 of exposed surface area,for example, sees temperature changes of around 20o C, while a pack with 0.5m2 exposed sees only around 5o C. This increase in range likely means that batteries with a higher surface area, while experiencing more cooling and thus less thermal degradation, will also see much higher thermal stresses caused by the high-amplitude cycling.

Figure 9 Comparing the degradation models (Fig.9), it is evident that higher exposed surface areas tend to have less cyclic degradation. However, it appears that this effect is not monotonic and has an optimum value. As observed, the 5m2 pack experiences more degradation than a 2.5m2 . As mentioned before, this could be due to thermal stress due to higher amplitude temperature cycling. This could also be the effect of low-temperature operation. As the temperature drops below freezing during cruise, it is entirely possible that a phenomenon such as crystal formation due to a drop in the solubility limit occurs in the electrolyte, leading to higher rates of degradation.

The overall takeaway of analyzing multiple cooling areas is that thermal degradation can be limited by increasing the area of contact between the battery and the forced convection stream. This can yield improvements of over 5% for degradation performance. However, there is also an optimal value for exposed pack area, which needs to be carefully calibrated to flight parameters such as aircraft velocity, cruise altitude, and rest time. The task of optimizing this area is left to the engineers at Zunum. For subsequent pack chemistries, an exposed area of 2.5m2 will be used.

3.5

NCM 622 Chemistry

The NCM 622 pack used for this simulation is sized similarly to the NCM 111 pack used in prior sections. Its characteristic cycling profile (Fig.10) also exhibits very similar features. Some key differences are a deeper discharge cycle and a higher voltage drop during high power operation. Just as for NCM 111, this is potentially problematic for aviation applications. Reduced motor speed due to a drop in voltage can have a large impact on the thrust output of the fan.

As pictured in Fig.11, the initial per cell capacity of NCM 622 is lower than that of NCM 111, however, the two appear to degrade at virtually the same rate. As indicated by comparison of individual loss factors in Fig.11, NCM 111 experiences a slightly higher anode isolation loss, but a lower anode SEI loss. These two effects seem

to cancel themselves out within a margin of error. However, examining the system at a higher cycle count and a more refined mesh may see these effects exaggerated. As the useful life of such a pack is unlikely to be longer than the timespan of the simulation, the difference in degradation can be stated to be negligible.

There is one advantage to using an NCM 622 pack. The higher charge density of the 622 cathode enables it to be lighter than the 111 pack. Over the pack, disregarding the mass of packaging and wiring, a 622 pack comes in at almost 100 kg lighter. This overrides the higher initial capacity advantage of NCM 111 in the largely weight-sensitive aviation application.

3.6

NCA and Pack Chemistry Comparison

The cycling profile for the NCA pack turned out to be visually indistinguishable from the NCM 622 profile. An interesting comparison lies in Fig.12, the temperature profiles. It appears that NCM 622 has a wider and higher temperature range than the other two chemistries, NCM 111 operates around room temperature, and NCA operates below ambient on average. This provides an interesting insight into the thermal engineering behind integration of the pack into the aircraft. For an identical surface area, each pack exhibited different thermal cycling properties, with a difference in excess of 5o in some places. This will likely have an effect on the degradation performance, even if it is not as dramatic as the difference in scenarios mentioned previously.

As for the degradation behaviour (Fig.13), the percent capacity lost is similar across all three chemistries.

However, it is evident from the plot that NCA does degrade more slowly than the other packs (0.846 mAh/cycle as opposed to 0.882 for NCM 111 and 0.897 for NCM 622). There is also the matter of a degradation bump observed for the NCA chemistry. We are unsure as to what could be the cause of such behaviour. It is possible that the bump is an artifact of simulation, but further investigation would be required.

Mass-wise, NCA is a marginally better option than NCM 622. An NCA pack comes in at around 30 kg lighter than a 622 pack, although the improvement is not as drastic as of a 622 pack over a 111. This comes at the expense of charge per cell, although the reduced density of the cell compensates for the addition. It should be noted that this analysis excludes packaging and wiring mass, which may as much as double the weight. It should additionally be noted that deeper cycling of the two packs may not present a completely fair comparison between the chemistries. However, seeing that deeper cycling does not reduce the average capacity loss rate of NCA, it does not appear that deeper cycling came with any tradeoff rather than reduced cell potential near the landing phase.

4

Conclusions

As discussed in section 3.1, our findings indicate that the power profile provided by Zunum Aero puts a sizeable amount of strain on the packs driving that profile. This results in things like high potential drop, thermal and high-current degradation, and large capacity losses over the battery life cycle. However, the effects of thermal degradation can be mitigated by adequate thermal management of the battery, as explored in sections 3.2-3.3.

Data indicates that properly managing the thermal cycling of the battery can yield a nearly 20% improvement in cyclic degradation. It should be noted, that this effect is not monotonic. In fact, overcooling the pack during the cruise phase at low power draw detrimentally affects the life of the battery. There exists an optimal cooled area that minimizes the range of the thermal cycle and brings it closest to ambient. This area is highly dependent both on flight parameters (cruise speed, cruise altitude, duration of high power regimes, power draw, etc.), as well as the particular chemistry of the battery. Our findings in section 3.5 showed that NCA required least cooling of the three and NCM 622 required the most. Careful analysis of both the aircraft and the battery is required to maximize the lifetime of the pack.

Variations in cell chemistry affected pack degradation minimally. NCA degraded the slowest, at 0.846 mAh/cycle, and NCM622 the fastest, at 0.895 mAh/cycle, although this effect is highly temperature-dependent. The degradation for all three chemistries are about 12 +/- 0.5% of initial capacity. Of the three cell chemistries evaluated, NCM622 experienced the highest operating temperatures during all cycles.

Conversely, voltage drop across different cell chemistries was more apparent. NCA saw the greatest drop in voltage and NCM622 saw the least, although coarse sampling from our Simulink model makes these observations subject to further investigation.

We modeled one-sided convective heat transfer, making a flat plate simplification. Airflow over an airfoil will differ in convective heat transfer. If batteries are exposed at both top and bottom surfaces, an axisymmetric heat model must be applied to extend convection to both the wing surfaces. Secondly, we modeled our packs

with a coarse mesh, with two cells for the anode and cathode each, and one for the separator. Refined mesh, although exponentially more computationally expensive, may yield more accurate performance characteristics per each flight cycle. With incomplete atmospheric data published, our thermal model used interpolated density and temperature values. Additionally, the flat-plate convective model only guarantees physical accuracy within 15% due to non-deterministic turbulence effects [1]. The model would improve with a real dataset of heat flux from a scaled wing test or an actual prototype test. A thermocouple reading at the pack-wing interface could provide insight into the transient behavior of pack heat generation.

Many batteries are not allowed to cycle below 0.4 SOC. However this generally applies to consumer electronics which will experience many more cycles than an aircraft battery pack. Long term, highly repetitive deep cycling will significantly degrade capacity. This does not apply to electric aircraft because of the relatively few charging cycles they will undergo compared to a cell phone. Allowing our SOC to deep cycle below 0.4 SOC would mean a smaller capacity would be required and thus a lighter pack, which is critical in aerospace applications. Although technically an airplane battery pack could cycle below this threshold because of the fewer number of cycles expected, there is an issue of safety regarding flying with low energy reserves. Taking this into account we determined 0.4 to still be the minimum allowable SOC. Notably, however, not for the same reasons as many other batteries with similarly imposed limits. This introduces an interesting trade off between pack weight, battery health, aircraft safety, and the economics of pack replacement schedules. Engineers and planners must determine the cost/benefit analysis of more frequent pack replacements for the sake of reduced takeoff weight and increased agility.

5

Tables

Table 1

Table 2

6

Figures

Figure 1: Simulink Thermal Model with Cycling.

Figure 2: NCM 111 Characteristic Cycling Profile.

Figure 3: NCM 111 Cycling profile with added rest period

Figure 4: Temperature Profile of Stock Cooling Model

Figure 5: Initial NCM 111 simulation.

Figure 6: Comparison of thermal models.

Figure 7: Zunum Profile

Figure 8: Pack area effect on operating temperature.

Figure 9: Pack area effect on degradation.

Figure 10: NCM622 cycling profile.

Figure 11: Comparison of NCM111 and NCM622

Figure 12: Minimum and maximum temperatures during cycling for each pack chemistry.

Figure 13: Pack degradation comparison of multiple chemistries.

7

References

1. Bergman, Theodore L. Fundamentals of Heat and Mass Transfer. Vol. 7, Wiley, 2011.

2. Falck, Robert D., et al. Trajectory Optimization of Electric Aircraft Subject to Subsystem Thermal Constraints. AIAA 3. Aviation Forum, 5 June 2017. AIAA Aviation Forum, doi: 10.2514/6.2017-4002.

4. Hogge, Edward F., et al. NASA Technical Reports Server (NTRS)2008364NASA Technical Reports Server (NTRS). Washington, DC: NASA Center for Aerospace Information Last Visited June 2008.

Gratis URL: Http://Ntrs.nasa.gov/. Reference Reviews, vol. 22, no. 8, 2008, pp. 4041., doi:10.1108/09504120810914619.

5. Vegh, Julius M., et al. Flight Path and Wing Optimization of Lithium-Air Battery Powered Passenger Aircraft. 53rd AIAA Aerospace Sciences Meeting, Mar. 2015, doi:10.2514/6.2015-1674.