Examining the Influence of Battery Sizing on Hydrogen Fuel

Canadian Journal of Civil Engineering

Examining the influence of battery sizing on hydrogen fuel cell/battery hybrid rail powertrains (Hydrail) for regional passenger railway transport using dynamic component models

Journal: Canadian Journal of Civil Engineering

Manuscript ID cjce-2019-0464.R2

Manuscript Type: Article

Date Submitted by the 21-Mar-2020 Author:

Complete List of Authors: Hegazi, Mohamed; University of British Columbia, Applied Science Markley, Loïc; Faculty of Applied Science Lovegrove,Draft Gordon; Faculty of Applied Science

Rail electrification, Hydrail, Hydrogen fuel cell / battery hybridization, Keyword: Rail locomotive emission reduction, Hybrid Vehicle

Is the invited manuscript for consideration in a Special Not applicable (regular submission) Issue? :

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3 Examining the influence of battery sizing on hydrogen fuel cell/battery hybrid rail powertrains

4 (Hydrail) for regional passenger railway transport using dynamic component models

10 Mohamed A. Hegazi, PhD student

11 Loïc Markley, P.Eng., Assistant ProfessorDraft

12 Gord Lovegrove, P.Eng., Associate Professor (corresponding author)

13 Phone: 250.807.8717; E-mail: [email protected]

14 UBC School of Engineering

15 1137 Alumni Avenue, Kelowna, BC, Canada, V1V 1V7

20 Word Count: (6 figures, 3 tables × 250 words each) + 5398 = 7648 words

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23 Abstract

24 To address the transportation sector’s contribution to climate change problems across North

25 America (NA), passenger rail is an attractive solution. However, NA passenger rail traditionally

26 relies on diesel motive power, which has been associated with causing health problems of noise,

27 vibrations, and emissions. High costs of overhead and/or third rail infrastructure have mostly

28 precluded electrification. This paper examines the impact of battery size on fuel cell stack

29 efficiency for hydrogen fuel cell/battery hybrid (Hydrail) railway propulsion systems using

30 dynamic simulations as opposed to existing simulations in the literature which rely on static

31 efficiency values. The journey of the British Rail Class 156 diesel multiple unit is simulated over

32 the round trip from Trehafod to Treherbert (UK) using a series-hybrid architecture powertrain.

33 Dynamic simulations at incremental batteryDraft masses were used to assess fuel cell efficiency,

34 maximum power, and overall hydrogen consumption. Battery mass is employed as a proxy for

35 power and energy capability of the battery. Results suggest that Hydrail passenger railway systems

36 work well, with hydrogen fuel cells handling most load dynamics. Hybridization with batteries

37 works best and reduces fuel cell stack size and hydrogen consumption, with overall 64% stack

38 efficiency.

40 Key words: Rail electrification; Hydrail

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46 1 Introduction

47 Efforts are underway worldwide to address climate change, traffic congestion, transport equity,

48 and road safety problems. Public transit, and in particular, passenger rail is becoming more

49 competitive with the private auto, especially as solutions to traffic congestion and transport equity

50 problems. Railway propulsion technology in many parts of the world is dependent on diesel fuel.

51 Although diesel engines can handle the power dynamics associated with passenger rail, they are

52 noisy, inefficient, and environmentally unfriendly when compared to hydrogen fuel cell propulsion

53 (Hoffrichter, Miller, et al. 2012). While remaining more environmentally friendly than other

54 modes of land transport, the Railway Association of Canada (RAC) put 2016 emissions levels at

55 0.01609 kg carbon dioxide equivalent per revenue tonne-kilometer for regional and short line

56 passenger rail (for comparison, Class 1Draft freight locomotives emitted an average of 0.01347 kg

57 carbon dioxide equivalent per revenue tonne-kilometer) (Railway Association of Canada 2016).

58 Increased awareness of the danger that fossil fuel combustion poses on the environment and human

59 well-being has led to the development of several diesel alternatives.

60 Complete track electrification has the potential to reduce Well-to-Wheel (WTW) 퐶푂2 emissions

61 (Hoffrichter, Miller, et al. 2012). However, the cost of electrification using third-rail and/or

62 overhead retrofits in the North American railway sector, which is predominantly freight trains run

63 by private companies, is often prohibitive (Morrison and Lovegrove 2012). It has only been

64 justified on high traffic passenger railroads in urban areas, such as the US Northeast Corridor

65 (Boozarjomehri et al., 2012).

66 Recent developments in energy storage system (ESS) has prompted research into on-board hybrid-

67 electric power generation and energy storage for railway vehicles (Mir, et al. 2009; Ogasa 2010).

68 Specifically, hydrogen fuel cell hybrids (Hydrail) have been the focus of multiple railway vehicle

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69 retrofitting projects. Perhaps the first milestone achieved in the application of hydrogen fuel cells

70 to propel railway vehicles was the development of a fuel cell mining locomotive by Vehicle

71 Projects LLC (Miller, Tunneling and mining applications of fuel cell vehicles. 2000). The Railway

72 Technical Research Institute (RTRI) and East Japan Railway Company (JR East) have made

73 similar progress in Japan (Yoneyama et al., 2007). In the UK, similar research has been undertaken

74 by the Birmingham Center for Railway Research and Education (Hoffrichter et al., 2014). These

75 examples are relevant to this study because they demonstrate that fuel cell propulsion is indeed

76 possible with railway duty cycles associated with switching or passenger operations. The case

77 study presented in this paper, the British Rail Class 156 (BR C156) is similar to Diesel Multiple

78 Units (DMUs) used in Canada, such as the Budd Rail Diesel Cars, Nippon Sharyo DMU, and

79 Alstom Corodia LINT, among others. Draft

80 This paper presents the work done to simulate a fuel cell/battery series-hybrid powertrain to replace

81 the conventional diesel/hydraulic powertrain in a BR C156 DMU. The BR C156 DMU was

82 selected due to its widespread use in the UK and the widespread use of similar designs elsewhere,

83 providing a practical case study for the time when the railway industry adopts on-board hybrid-

84 electric ESS for multiple units. The primary objective of the research presented in this paper is to

85 examine the impact on performance of ESS sizing in a fuel cell/battery hybrid powertrain (this

86 sizing is also referred to as the hybridization ratio). Unlike studies with similar objectives, this

87 study relies on detailed dynamic simulation which greatly emphasizes the response times of the

88 various subsystems and impacts overall efficiency. Specifically, this paper will examine the impact

89 of sizing on fuel cell efficiency, energy regeneration, hydrogen consumption, and maximum fuel

90 cell power. The need for appropriate sizing of hydrail vehicles stems from the high cost of

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91 electrification and the environmental impact of diesel engines. Well-to-wheel emissions estimates

92 are outside the scope of this paper.

94 2 Methodology

95 2.1 Overview

96 The results presented in this paper were generated through a two-step computational simulation

97 process. The first step was to construct a vehicle velocity trajectory. The trajectory planning

98 algorithm generated a velocity profile which achieved minimal trip time given track and vehicle

99 velocity constraints (Jong and Chang 2005). This algorithm took input data relating to the

100 infrastructure of the railway track as well as data from the vehicle to be studied. This included the

101 track elevation profile, station and terminalDraft locations, station dwell times, track speed limits, train

102 consist, aerodynamics, rolling resistance, power, and acceleration and braking limits. The

103 equations of motion were then solved by treating the entire train as a point mass (Rochard and

104 Schmid 2000).

105 The second step of the simulation consisted of a powertrain dynamics simulator. The powertrain

106 in a vehicle powered by fossil fuels is a complex mechanical arrangement that controls the flow of

107 power from the prime mover (the combustion engine) to the wheels of that vehicle. In a hybrid

108 vehicle, multiple power sources are employed and additional components that regulate the power

109 drawn from each source must be included. Hybrid electric powertrains are composed of electric

110 power sources, power conditioning electronics, electrical traction machines, energy management

111 systems, and gearing mechanisms. A series-hybrid fuel cell/battery powertrain was developed as

112 shown in Fig. 2. This powertrain architecture was chosen because it allows the battery bank to

113 handle all of the traction power dynamics, while the fuel cell acts as a battery charger. By allowing

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114 the fuel cell to deliver relatively steady power, fuel cell efficiency is enhanced. An ideal

115 unidirectional boost converter regulated the electrical output of the fuel cell stack that fed into the

116 lithium-ion battery bank. The battery bank was then used to feed the traction machines through a

117 bidirectional buck/boost converter to allow for battery regeneration during braking. Unlike

118 existing simulations in the literature, this two-step approach reveals more about the dynamics of

119 the employed power sources, and is more accurate in efficiency and fuel consumption estimates

120 as it doesn’t rely on static values for subsystem efficiency.

121

122 2.2 Longitudinal Dynamics of Trains

123 The longitudinal force at the wheel-rail contact generated by the prime mover is known as the

124 tractive effort. To initiate movement, theDraft tractive effort must exceed any retardation forces such as

125 wheel-rail friction, air resistance, track gradient, and additional accelerating force. The maximum

126 tractive effort a railway vehicle can produce to propel a stationary train is the starting tractive effort

127 (푇퐸푠푡푎푟푡 ). The starting tractive effort is a function of the vehicle’s weight (푊 ) and the wheel-rail

128 friction factor (µ), as given by Eq. 1, and is independent of the vehicle’s power. The friction factor

129 is determined by the materials from which the wheels and rails are made, and environmental factors

130 that could affect the surface of the rails (ice, water, leaves, etc.). (Yi, Lyu and Olofsson 2015)

131 (1) 푇퐸푠푡푎푟푡 = 휇푊

132 The relationship between the longitudinal tractive effort (TE), and velocity (v) with the prime

133 mover’s power (P), is defined by Eq. 2, where η is the energy conversion efficiency:

푃휂 134 (2) 푇퐸 = 푣

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135 The retardation forces on a moving train are challenging to calculate and are often approximated

136 using empirical data. The resistance of the train to movement along level track can be determined

137 by the well-known, original Davis equation (3) below, where 푓푅 is the resistive force and 퐴 , 퐵 ,

138 and 퐶 are the Davis coefficients. These coefficients account for both the rolling resistance and

139 aerodynamic drag of the train.

2 140 (3) 푓푅 = 퐴 + 퐵푣 + 퐶푣

141 The gravitational force, 푓퐺 , on the train due to a non-zero track gradient can be calculated using

142 Eq. 4, where 퐺 is the gradient of the track, and W is the weight of the train.

143 (4) 푓퐺 = 푊퐺

144 2.4 Offline Trajectory Planning Draft 145 The trajectory profile for a railway vehicle can be generated by solving the equation of motion

146 along a given trip path. There are many software packages that compute velocity profiles for

147 railway trips. The complexity of these packages varies considerably, with some models accounting

148 for train-wagon interactions, coupling between railcars, air-brake system dynamics, the impact of

149 environmental conditions on friction, and more. These detailed models are usually employed

150 through commercial licensing, whereas simpler models that approximate the entire train as a single

151 rigid body can be used in research studies such as this one (Jong and Chang 2005).

152 Fig. 1 illustrates the steps taken to determine the velocity profile of the train along the route that

153 minimizes the total trip time. In general, the velocity profile changes with the optimization criteria.

154 For example, the velocity profile generated using an algorithm for minimum trip time would

155 typically have higher velocities than the one developed for minimum energy consumption.

156 Route characteristics determine the maximum allowable speed at every section of the track. These

157 speed limits are determined using industry manuals and typically depend on track curvature and

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158 grade. The acceleration and deceleration rates of a railway vehicle are also limited in order to

159 maintain safety and passenger comfort.

160 Offline trajectory planning techniques such as the one used in this work takes vehicle data and

161 infrastructure data as inputs. This includes information about the railway vehicle, such as its Davis

162 equation coefficients (Eq. 3), inertial mass, friction coefficient, speed, and acceleration limits, as

163 well as information relating to the infrastructure of the route, such as its elevation profile, curve

164 profile, and speed limits on each section of the route (Martin 2008).

165

166 2.5 Fuel Cell/Battery Series-Hybrid Powertrain

167 Dynamic models of the various subsystems that make up a fuel cell / battery series hybrid

168 powertrain were implemented and simulated.Draft No independent effort was taken into modelling the

169 subsystems, instead existing models in the literature were employed. A detailed illustration of the

170 series hybrid powertrain employed in this study is presented in Error! Reference source not

171 found. 2. This diagram presents how each powertrain subsystem is connected. A boost converter

172 is used to increase the output voltage of the fuel cell stack from 400 V to 1500 V so it can feed

173 into the lithium-ion battery bank. A bidirectional buck/boost converter is then used to provide

174 power from the battery bank to the traction machine.

175 The bidirectional buck/boost converter is controlled by an energy management system (EMS) and

176 either supplies or receives power from a permanent-magnet DC traction machine. The traction

177 machine is modelled as an equivalent electrical circuit coupled to an ideal gearing mechanism with

178 a 3:1 gear ratio, at which point the rotational velocity of the railcar wheelsets is converted to

179 translational velocity.

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180 The translational speed is then used to calculate the resistive forces using the empirical rolling

181 resistance function. The vehicle’s velocity is integrated to calculate the total distance travelled

182 from the starting location. The vehicle’s location is then used to look up the track gradient at that

183 particular section and calculate the gravitational resistance. These resistive forces are then summed

184 and converted to torque on the traction machine.

185 Gas flow to the fuel cell stack is regulated using two independent gas regulators. The gas regulators

186 are controlled through a rate limited proportional controller that responds to the error between the

187 battery bank’s state of charge (SOC) and a reference value. In this study, both the reference value

188 and the initial SOC were set to a value of 50%. This setting was chosen to maintain a net zero

189 change in battery SOC over the course of the trip.

190 To strike a balance between model accuracyDraft and simulation complexity, several simplifying

191 assumptions had to be made. The dynamic simulation of the power electronic converters assumes

192 ideal switching devices. All mechanical couplings are considered ideal with no energy loss. The

193 lithium-ion battery model doesn’t account for temperature effects or aging effects. The direction

194 of power flow is controller through a two-mode heuristic controller described below.

195

196 2.6 Energy Management System

197 To reduce computation time, the simulation used a two-mode state-machine driver model. State-

198 machine control is a heuristic rule based control mechanism (Torreglosa, et al. 2014) where the

199 rules should be chosen to minimize oscillations (chatter) between different modes (Thounthong,

200 et al. 2009). In this work, a two-mode logic was used: the train would be in either a motoring mode

201 or a regenerative braking mode. Motoring mode is the mode of operation where the energy transfer

202 is from the battery bank to the traction machine. In this mode the bidirectional converter operates

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203 in its buck configuration. Regenerative mode is the mode of operation where the energy flow is

204 reversed (i.e. from the traction machine to the battery bank) and the converter is in boost

205 configuration.

206

207 3 Case Study

208 3.1 Vehicle and Route Description

209 The BR C156 is a Diesel Multiple Unit (DMU) consisting of two railway cars each running on

210 two dual-wheelset trucks. The vehicle, shown in Fig. 3, has a 213 kW diesel engine coupled to a

211 hydraulic transmission system. Typically, two railcars make up a trainset. Error! Reference

212 source not found. contains the DMU specifications. In order to retrofit this vehicle, significant

213 alterations to the drivetrain are required.Draft However, for simplification purposes, the study focuses

214 on the space and mass available due to the removal of the fuel tank and engine only. This will free

215 up to approximately 4000 liters of space and 4 tonnes of mass per train (2000 liters and 2 tonnes

216 per car). The Trehafod to Treherbert route is approximately 14 kilometers in length. The Treherbert

217 station is reached after 13.9 km and an elevation gain of approximately 100 m. This corresponds

218 to an average gradient of 0.7%, a maximum gradient of 2.13%, and minimum gradient of -0.24%.

219 3.2 Selection of Power Sources

220 The architecture of a hybrid powertrain with ESS depends on the intrinsic properties of each

221 individual power source—in particular, their energy densities, power densities, and transient

222 responses. Given the objective of eliminating rail emissions at the point of use, this work presents

223 a fuel cell/battery hybrid system under a range of hybridization ratios.

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224 3.2.1 Proton Exchange Membrane Fuel Cell Selection (PEMFC):

225 PEMFCs combine hydrogen and oxygen to generate electricity, producing water and heat as a by-

226 product. Similar to an internal combustion engine, PEMFCs do not store energy. They simply

227 convert it from one form to another with a certain efficiency. The peak PEMFC system efficiency

228 typically ranges between 50-60% (Fragiacomo and Piraino 2018; Zhang, Chen and Li 2017).

229 Although this efficiency level is typically lower than that of batteries, the high energy density of

230 hydrogen can more than make up for fuel cell losses with respect to range per volume/mass of

231 storage (Hoffrichter, Miller, et al. 2012). The fuel cell stack chosen for this study is the Honda

232 FCX family of experimental fuel cell stacks and a dynamic model developed in (Motapon,

233 Tremblay and Dessaint 2012) was utilized in the simulations. This fuel cell stack was chosen

234 because it is indicative for heavy duty systemsDraft and its data was available. The stack’s efficiency

235 curve is presented in Fig. 4. As will be shown later, the BR C156 will require two to four FCX

236 stacks, depending on the level of hybridization.

237 3.2.2 Battery Selection:

238 The battery chosen for this study is the UPF454261 lithium-ion 3.7 V cell manufactured by

239 Panasonic. In order to produce a 1500 V armature voltage, the batteries were arranged in parallel

240 branches of 405 series-connected cells. Each branch would weigh 10.9 kg and could store 587 Ah

241 of energy. Lithium-ion polymer batteries are appropriate for traction applications due to their high

242 energy density, power density, and charge (C) rates (Corbo, Migliardini and Veneri 2010).

243

244 4 Results and Discussion

245 This section presents the simulation results obtained using the Mathworks Simulink simulation

246 platform. The first study compares a non-hybridized electric powertrain for a retrofitted BR C156

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247 with a fuel cell power source (FCEMU) with a hybridized fuel cell/battery electric powertrain

248 (FCBEMU). The second study examines the impact of battery bank sizing on fuel cell stack

249 performance and hydrogen consumption.

250 4.1 FCEMU vs FCBEMU:

251 Fig. 5(a) presents the velocity, power, and FC efficiency profiles for the FCEMU over the 27.8 km

252 round trip between Trehafod to Treherbert. The velocity profile of the train is plotted alongside

253 the track speed limits in Fig. 5 (a.1), demonstrating that the fuel cell can handle the associated

254 dynamics to complete the trip in just over 45 minutes. The power required by the load is plotted in

255 Fig. 5 (a.2) beside the power output from the fuel cells. These curves show the FC stack to be

256 frequently operating above its 200 kW rated power limit. These curves also demonstrate the power

257 wasted in frictional brakes (black filled Draftportions), which could have otherwise been regenerated if

258 an appropriate ESS was used. The figure demonstrates that braking during the uphill trip (first

259 1400 seconds) is completely gravity dependent as evidenced by the lack of frictional brake power.

260 Similarly, acceleration during the downhill trip is primarily due to gravity and braking is entirely

261 dependent on the use of frictional brakes. For the hybrid, it is important to note that the FC system

262 is primarily used as a battery charger and therefore will operate during dwell time and at terminal

263 stops if the battery SOC is below a pre-set value. If the FC only operates when the vehicle is not

264 braking, higher magnitude swings in the battery SOC are to be expected for a fixed FC power.

265 Peak regenerative power is approximately 100 kW which would require higher charge (C) rates at

266 lower battery masses.

267 The FC stack efficiency is shown in Fig. 5 (a.4) to be negatively impacted from swings in power

268 drawn from the FC, thereby reducing the overall trip efficiency. These efficiency values are not

269 inclusive of Balance of Plant (BoP) components and therefore the actual FC system efficiency is

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270 likely to be up to 10% lower than the values reported in this study (Lohse-Busch, et al. 2018).

271 These results are now compared to a simulated trip in which a 100 kg lithium-ion battery bank is

272 added to the multiple unit. Fig. 5 (b) presents the velocity, power, SOC, and efficiency profiles for

273 the FCBEMU over the Trehafod to Treherbert round trip. The velocity profile is plotted in Fig. 5

274 (b.1) and is in close agreement with the FCEMU velocity profile in Fig. 5 (a.1) with a similar

275 overall trip time. It is important to note that while higher acceleration rates are possible with the

276 addition of a battery bank, the acceleration rate in these simulations is capped to a maximum of

277 0.5 m/s2 due to passenger comfort considerations. Fig. 5 (b.2) plots the net power delivered to the

278 load as well as the power contributions from each source. The FC system supplies relatively steady

279 power throughout each half of the trip with the power supplied by the battery changing

280 dynamically to cope with load changes.Draft This allows the FC system to operate at near optimal

281 efficiency levels. For this particular case, the average FC stack efficiency was calculated at 63.1%.

282 Average FC efficiencies are calculating by averaging the instantaneous stack efficiency as

283 calculated by the dynamic model described in (Motapon, Tremblay and Dessaint 2012) using stack

284 characteristics detailed in Error! Reference source not found.. It is important to note that this is

285 stack efficiency excluding the efficiency of the FC BoP and that the power draw is entirely due to

286 traction requirements and that hotel power is unaccounted for. Fig. 5 (b.2) also highlights the

287 intervals during which energy is regenerated. The energy consumed for the simulated round trip

288 was 40.4 kWh; this is approximately 92% of the energy consumed by the FCEMU. Lack of

289 hybridization led to higher energy consumption due to the combined effect of reduced stack

290 efficiency and lack of a medium in which to store the regenerated energy.

291 The battery SOC is plotted in Fig. 5 (b.3). The final SOC is equal to the initial SOC, indicating a

292 net change of zero over the course of the trip. This confirms that the battery’s role is to provide

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293 superior dynamic power handling, not to provide energy for the trip. Although the net change in

294 SOC is zero, proper battery bank sizing must take into consideration the overall battery SOC

295 swing, mass of the battery module, as well as the value of the initial SOC. To avoid reducing the

296 lifetime of the battery, it is generally advisable to size the battery such that the instantaneous SOC

297 always remains between 20% and 80%.

298 As mentioned previously, changing the on-board energy mix will impact certain trip performance

299 indicators, such as energy regeneration, FC efficiency, maximum FC stack power, and hydrogen

300 consumption. To investigate the impact of battery bank mass on these indicators and find an

301 optimal hybridization ratio, a range of battery sizes were simulated and compared.

302

303 4.2 Impact of battery bank mass: Draft

304 Given the high energy density of hydrogen relative to lithium-ion batteries, a hybrid-electric train

305 can maximize travel range by using the minimum battery mass necessary to achieve a certain trip

306 performance and then storing as much hydrogen as possible given the space and mass limits of the

307 multiple unit. In this study, Matlab’s parallel computation tool was used to analyze different

308 hybridization configurations in order to examine the impact of component sizing and determine a

309 minimum recommended battery size. Battery bank mass is commensurate with ESS net energy

310 stored and maximum power potential.

311 In the previous section, the FC system was shown to meet the dynamic power demand of a

312 passenger rail car, however it came at the cost of reduced FC stack efficiency, increased peak FC

313 stack power, and lack of regenerative capability. Furthermore, although this paper does not discuss

314 FC life cycle, evidence from the literature suggests that subjecting the FC system to higher-order

315 power dynamics has a significant negative impact on its expected lifetime (Wu, et al. 2008).

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316 Fig. 6 presents the relationship between the battery bank mass and the power system performance

317 indicators. The trip average FC stack efficiency plotted in Fig. 6 (a) increases with battery mass

318 from 61% for a no battery scenario to a peak of 63.6% for a 200 kg battery which is maintained

319 with larger battery masses. It is important to mention that Balance of Plant (BoP) efficiency is not

320 considered. A bigger battery can provide power for longer periods, allowing the FC system to

321 operate at a steady power level, thereby increasing the stack efficiency. The maximum stack

322 efficiency occurs at 200 kg, with larger batteries providing no additional performance gains.

323 Fig. 6 (b) presents the relationship between the mass of the battery bank and total energy

324 regenerated relative to the total energy expended in a complete trip. Again, once a minimum

325 threshold is met, increasing the battery size further does not increase the amount of energy

326 recaptured through regeneration with theDraft possibility of local maxima below 200 kg. In this case,

327 having a battery with a mass of at least 50 kg is sufficient to regenerate up to 7 to 8% of the total

328 energy expended in the trip assuming it can handle charge (C) rates of up to 10.

329 Increased FC stack efficiency coupled with increased regeneration leads to a reduction in the total

330 amount of hydrogen consumed in each trip. Fig. 6 (c) plots the decrease in hydrogen consumption

331 from 3.56 kg per trip for a 0 kg battery mass (i.e. the FCEMU case) to 3.1 kg using a 500 kg battery

332 mass, a 12.9% reduction in hydrogen consumption. Assuming 18 round trips per day for a total

333 distance travelled of approximately 500 km, the amount of onboard hydrogen storage for daily

334 refueling would be in the range of 56 - 64 kg. Compressed hydrogen gas at room temperature and

335 at pressures of 300 - 500 bar has a density of 0.02 - 0.03 kg/L, resulting in a total volume

336 requirement of 1900 – 3200 L depending on the storage pressure. Given the previously mentioned

337 volume constraint of 4000 L, this amount of hydrogen can be accommodated. Since a smaller

338 battery bank is less capable of handling power surges (i.e. sudden changes in current), the

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339 maximum power required from the FC will increase as the battery becomes smaller. The maximum

340 FC power is plotted in Fig. 6 (d) decreasing from 332 kW for no battery to 105 kW for 500 kg or

341 larger batteries. This corresponds to three or four FCX stacks required for the FCEMU versus one

342 or two for the 500 kg FCBEMU. The additional stacks fit within the total mass constraint with the

343 addition of ballast to ensure the total mass and therefore tractive effort remains constant.

344

345 5 Conclusion

346 In efforts to address emerging climate change, road safety, and traffic congestion problems,

347 transport authorities worldwide are looking at clean rail power. Hydrail – hydrogen fuel cell /

348 battery hybrid rail power – can theoretically provide sufficient on-board power and energy storage

349 to address these global problems in an economicallyDraft feasible way. This paper presents a case study

350 application of hydrail, assuming it was retrofitted into an existing diesel passenger rail service in

351 the UK. A dynamic model for a BR C156 DMU retrofitted with FCEMU and FCBEMU

352 powertrains was presented. A 27.8 km round trip from Trehafod to Treherbert was chosen as a test

353 case to study the effect of battery sizing on fuel cell/battery hybrid powertrains. Dynamic models

354 of the fuel cell and battery were used to assess their ability to handle dynamic power demands.

355 Simulation runs were conducted at different battery masses and several performance indicators

356 were monitored.

357 According to the simulations, increasing battery mass improved the FC stack efficiency to close

358 to 64%, increased the energy regenerated, decreased the hydrogen consumed by up to 13%, and

359 decreased the maximum FC power required for the trip. Each indicator improved up to a battery

360 size of approximately 200 kg, beyond which no significant further improvements were observed.

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361 This study suggests that hydrogen fuel cell systems are a natural fit for retrofitting railway vehicles.

362 Moreover, detailed simulations conducted on a passenger rail test case show that the addition of a

363 small lithium-ion battery can improve fuel cell performance for higher efficiency and longer

364 lifetimes and decrease hydrogen consumption by up to 13%.

365 ACKNOWLEDGEMENTS

366 The authors gratefully acknowledge funding of the Canadian Natural Sciences and Engineering

367 Research Council (NSERC), and the Transport Canada Clean Rail Program, in support of this

368 research. Data on the duty cycle was provided by the UK’s Birmingham University Railway

369 Program and was critical to a proper case study application.

370 Draft

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383 Hoffrichter, Andreas, Peter Fisher, Jonathan Tutcher, Stuart Hillmansen, and Clive Roberts. 2014. 384 "Performance evaluation of the hydrogen-powered prototype locomotive ‘Hydrogen Pioneer’." 385 Journal of Power Sources 250: 120-127. 386 Jenn-Jiang, Hwang, Yu-Jie Chen, and Jenn-KunDraft Juo. 2012. "The study on the power management system 387 in a fuel cell hybrid vehicle." International journal of hydrogen energy 37 (5): 4476-4489.

388 Jeong, Kwi Seong, and Byeong Soo Oh. 2002. "Fuel economy and life-cycle cost analysis of a fuel cell 389 hybrid vehicle." Journal of Power Sources 105 (1): 58-65.

390 Jong, Jyh-Cherng, and Sloan Chang. 2005. "Algorithms for generating train speed profiles."." Journal of 391 the Eastern Asia Society for Transportation Studies. 6: 356-371.

392 Kwon, Jason, Xiaohua Wang, Rajesh K. Ahluwalia, and Aymeric Rousseau. 2011. "Impact of fuel cell 393 system design used in series fuel cell HEV on net present value (NPV)." IEEE Vehicle Power and 394 Propulsion Conference. 1-7.

395 Lohse-Busch, Henning, Michael Duoba, Kevin Stutenberg, Simeon Lliev, Mike Kern, Brad Richards, 396 Martha Christenson, and Arron Loiselle-Lapointe. 2018. Technology Assessment of a Fuel Cell 397 Vehicle: 2017 Toyota Mirai. Argonne, IL (United States): Argonne National Lab. 398 doi:10.2172/1463251.

399 Marcinkoski, Jason, John P. Kopasz, and Thomas G. Benjamin. 2008. "Progress in the US DOE fuel cell 400 subprogram efforts in ploymer electrolyte fuel cells." International Journal of Hydrogen Energy 401 33 (14): 3894-3902.

402 Marin, Gabriel D., Greg F. Naterer, and Kamiel Gabriel. 2010. "Rail transportation by hydrogen vs. 403 electrification – Case study for Ontario, Canada, II: Energy supply and distribution." International 404 Journal of Hydrogen Energy 35 (12): 6097-6107.

405 Martin, Paul. 2008. "Train performance & simulation." IET Seminar Digest. 215-230.

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406 Mercuri, R., Ausilio W. Bauen, and David R. Hart. 2002. "Options for refuelling hydrogen fuel cell vehicles 407 in Italy." Journal of power sources 353-363.

408 Miller, Arnold R. 2000. "Tunneling and mining applications of fuel cell vehicles." Fuel Cells Bulletin 3 (22): 409 5-9.

410 Miller, Arnold R., Kris S. Hess, David L. Barnes, and Timothy L. Erickson. 2007. "System design of a large 411 fuel cell hybrid locomotive." Journal of Power Sources 173 (2): 935-942.

412 Mir, Luis, Ion Etxeberria-Otadui, Igor Perez de Arenaza, Izaskun Sarasola, and Txomin Nieva. 2009. "A 413 Supercapacitor Based Light Rail Vehicle: System design and operations modes." IEEE Energy 414 Conversion Congress and Exposition. 1632-1639.

415 Morrison, Ellen, and Gordon Lovegrove. 2012. "The Economics of Electrifying North American Railways." 416 Annual Meeting of the Transportation Research Board.

417 Motapon, Souleman Njoya, Olivier Tremblay, and Louis A. Dessaint. 2012. "Development of a generic 418 fuel cell model: application to a fuel cell vehicle simulation." International Journal of Power 419 Electronics 4 (6): 505-522.

420 Ogasa, Masamichi. 2010. "Application of energy storage technologies for electric railway vehicles— 421 examples with hybrid electric railway vehicles." IEEJ Transactions on Electrical and Electronic 422 Engineering 5 (3): 304-311. Draft

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453

454 455 Figure Captions: Draft 456 Fig. 1 A flowchart illustrating the steps taken to calculate a reference velocity profile in the

457 trajectory planning simulation. The train velocity is given by v, the acceleration is a, the tractive

458 effort of the prime mover is TE, the gravitational force is f_G, and the resistive force is f_R.

459 Velocity profiles are generated in the forward direction (trip start to finish, in red) and in the

460 backward direction (trip finish to start, in blue) to account for acceleration and deceleration limits,

461 respectively. The rectangular stepped velocity profiles in black indicate the track speed limits.

462 Fig. 2 The combined fuel cell/battery series-hybrid powertrain and control system model.

463 Fig. 3 British Rail Class 156 DMU railcar. Components to be removed are highlighted in red.

464 Hydrogen tanks are shown on the bottom of the vehicle in green. Image is for illustrative purposes

465 only.

466 Fig. 4 Fuel Cell Stack Efficiency Curve using Parameters in Table 2.

467 Fig. 5 A comparison of simulated trips between Trehafod and Treherbert using a BR C156

468 retrofitted with fuel cell system (a) and with a fuel cell/battery hybrid system (b). The trip velocity

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469 profiles are plotted in dashed red in (a.1) and (b.1) alongside the maximum allowable speed for

470 each track section in solid black. The FC power (solid grey line), battery power (dotted red line),

471 and load power (dashed black line) are plotted in (a.2) and (b.2). The frictional breaking power is

472 indicated by the black filled regions in (a.2) and correspond to regenerative braking in (b.2). The

473 battery state of charge is plotted in (b.3) and the instantaneous fuel cell efficiency is plotted in (a.4)

474 and (b.4).

475 Fig. 6 The relationship between the battery bank mass and (a) the mean FC stack efficiency, (b)

476 energy regeneration as a percentage of total trip energy, (c) hydrogen gas consumption, and (d) the

477 maximum fuel cell power.

478

479 Draft

480

481

482

483

484

485

486

487

488

489

490

491 LIST OF SYMBOLS

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492 ESS-Energy storage System

493 RTI-Railway technical institute

494 JR East- East Japan Railway company

495 DMU-Diesel Multiple Unit

496 푇퐸푠푡푎푟푡-Tractive effective

497 W-Weight

498 µ-Friction Factor

499 P-Power

500 v-Velocity

501 η-Efficiency

502 푓푅-Resistive Force Draft

503 A, B, and C- Davis Coefficients

504 푓퐺-Gravitational Force

505 SOC- State of Charge

506 Chatter- Oscillations

507 FC-Fuel Cell

508 BoP-Balance of Plant

509 PEMFC-Proton Exchange Membrane Fuel Cell

510 EMU-Electric Multiple Unit

511 FCEMU- Fuel Cell Electric Multiple Unit

512 FCBEMU-Fuel Cell/Battery Electric Multiple Unit

513

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Draft

A flowchart illustrating the steps taken to calculate a reference velocity profile in the trajectory planning simulation. The train velocity is given by v, the acceleration is a, the tractive effort of the prime mover is TE, the gravitational force is f_G, and the resistive force is f_R. Velocity profiles are generated in the forward direction (trip start to finish, in red) and in the backward direction (trip finish to start, in blue) to account for acceleration and deceleration limits, respectively. The rectangular stepped velocity profiles in black indicate the track speed limits.

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Draft

The combined fuel cell/battery series-hybrid powertrain and control system model.

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British Rail Class 156 DMU railcar. Components to be removed are highlighted in red. Hydrogen tanks are shown on the bottom of the vehicle in green. Image is for illustrative purposes only.

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Draft

Fuel Cell Stack Efficiency Curve using Parameters in Table 2.

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A comparison of simulated trips between TrehafodDraft and Treherbert using a BR C156 retrofitted with fuel cell system (a) and with a fuel cell/battery hybrid system (b). The trip velocity profiles are plotted in dashed red in (a.1) and (b.1) alongside the maximum allowable speed for each track section in solid black. The FC power (solid grey line), battery power (dotted red line), and load power (dashed black line) are plotted in (a.2) and (b.2). The frictional breaking power is indicated by the black filled regions in (a.2) and correspond to regenerative braking in (b.2). The battery state of charge is plotted in (b.3) and the instantaneous fuel cell efficiency is plotted in (a.4) and (b.4).

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Draft

The relationship between the battery bank mass and (a) the mean FC stack efficiency, (b) energy regeneration as a percentage of total trip energy, (c) hydrogen gas consumption, and (d) the maximum fuel cell power.

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1 TABLES

2 Table 1: The specifications of one British Rail Class 156 DMU

3 [obtained from publicly available manufacturer datasheets and brochures unless explicitly

4 cited].

Trainset Mass: 76.4 tonnes

Davis equation coefficients: a = 2.089 b = 0.0098 unitless

c = 0.0065

Maximum speed: 120 Km/h

Maximum tractive effort: 37.5 kN

Car length: 23.025 meters

Car width: Draft2.73 meters

Car height: 3.805 meters

Engine power: 213 kW

Engine mass: 1500 kg

Engine volume: 1500 (1 x 1 x 1.5 m) L

Transmission mass: 800 kg

Transmission volume: 650 L

Full Fuel Tank mass: 1700 kg

Full Fuel Tank volume: 1500 L

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7 Table 2: The specifications of the Honda FCX fuel cell stack (Hwang et al., 2012).

Mass: 96 kg

Volume: 66 L

Stack power: Nominal 85 kW

Maximum 100 kW

Fuel cell resistance: 0.17572 Ω

Nernst potential: 1.1729 V

Nominal utilization: Hydrogen 95.24 %

Oxidant 50.03 %

Fuel supply pressure: 3 bar

Air supply pressure: Draft3 bar

Fuel flow rate at Nominal 374.8 lpm

nominal hydrogen Maximum 456.7 lpm

utilization:

Fuel flow rate at Nominal 1698 lpm

nominal oxidant Maximum 2069 lpm

utilization:

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10 Table 3: The specifications of Panasonic’s Lithium Cobalt Oxide UPF454261 battery.

11 [obtained from manufacturer datasheet]

Rated capacity: 1450 mAh

Nominal voltage: 3.7 V

Weight: 27.0 g

Energy density: Volumetric 462 Wh/L

Gravimetric 199 Wh/kg

13 Draft

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14 TABLE CAPTIONS

15 Table 1 The specifications of British Rail Class 156 DMU [obtained from manufacturer

16 datasheets unless explicitly cited].

17 Table 2 The specifications of the Honda FCX fuel cell stack (Hwang et al., 2012).

18 Table 3 The specifications of Panasonic’s UPF454261 Lithium-ion battery.

24 Draft

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