Evaluation of Battery Pack Requirements for Mild ...

Evaluation of Battery Pack Requirements for Mild Hybrid Electric Heavy Duty Vehicle Steven Wilkins Mechanical Engineering, Imperial College London, Tel: 020 7594 7024, Fax: 020 7594 7127 Sejul Shah Mechanical Engineering, Imperial College London, Tel: 020 7594 7024, Fax: 020 7594 7127 Alan Walker Mechanical Engineering, Imperial College London, Tel: 020 7594 7024, Fax: 020 7594 7127 Michael Lampérth Mechanical Engineering, Imperial College London, Tel: 020 7594 7020, Fax: 020 7594 7239

Abstract Designing a heavy duty hybrid electric delivery vehicle requires careful selection of batteries, considering chemistry and size, to accommodate the vehicle’s energy requirements from driving demands. This paper presents an assessment of traction batteries suitable for Hybrid Electric Vehicles, examining the impact on performance and battery pack size of the vehicle’s duty capability. The effect of the battery capacity on cargo carrying capacity is explored for different control strategies, evaluating EV range, depth of discharge (DOD) and predicting battery pack life. Keywords: Battery Pack, Heavy Duty, Hybrid Strategy, Simulation, Lead Acid

1. Introduction Research and development on the energy and power densities of traction batteries is an important factor in the success of Electric Vehicles (EVs) and Hybrid Electric Vehicles (HEVs). [1][2] 1.1 Battery Chemistries There are many types of battery chemistry available, though only a number are suitable for vehicular use. The battery chemistries that are of most use are: Lead Acid, Nickel Metal Hydride, and Lithium Ion. These offer advantages and disadvantages according to the selection criteria presented in Figure 1.

Figure 1 Radar Plot of Traction Battery Technologies, derived from [3]

1.1.1 Lead Acid Lead Acid based batteries have traditionally been the most widely used battery technology, being proven, well understood and readily available. However, the batteries are heavy and have limited life cycles. 1.1.2 Nickel Metal Hydride Nickel Metal Hydride batteries are a much newer technology, having higher energy densities and charge capabilities than Lead Acid (since 1980, specific capacity has improved four-fold [3]). While similar to Nickel Cadmium, the batteries are non-toxic and readily recyclable, with cycle life better than Lead Acid (although not as good as NiCd). However the batteries have a higher cost and suffer from higher rates of self discharge than Lead Acid. The batteries require a more complex arrangement for charging. 1.1.3 Lithium Ion Lithium Ion batteries are one of the most promising technologies, with very high theoretical energy densities. Li-Ion batteries are capable of high cycle lives (>1500 @ 80% DOD), are low maintenance and have a low self-discharge rate. Additionally, the batteries do not have memory effects. However, LiIon batteries suffer from limited shelf lives, irrespective of use and are expensive. The batteries currently require additional protection to avoid over voltage and have unusual charging.

1.2 Battery Charge-Discharge Control Strategies Optimisation of the HEV powertrain is an integrated process between hardware and control strategy design. The control system should adapt to optimise the powertrain for a given application. [4, 5] Control strategy design is primarily influenced by three objectives [6]. These are to: •

Maximise fuel economy

•

Minimise emissions

•

Minimise system cost

1.2.1 Charge-Sustaining versus Charge-Depleting Operation of the HEV’s prime mover, according to demand and battery level, can be classified by two charge-discharge strategies for the battery; “charge-sustaining” and “charge-depleting”. The vehicle may operate using both strategies at various points in time. For charge sustaining, the battery’s SOC is set such that the average rate of electrical energy consumption is the same as the rate of energy return. This strategy is common where grid charging is not available. The prime mover is sized in order to supply average power demands, with the battery available to load level the peak power demands. Over extended periods, the prime mover is able to maintain the battery’s SOC [6]. A Charge Sustaining strategy would normally be managed by the vehicle (Prius, Tino). An et al.[7], showed a 90% saving in fuel efficiency of a charge-sustaining, power hybrid US delivery truck over the conventional. Charge depleting strategies can be subdivided into three categories, but in all cases the rate of electrical energy consumption is greater than the rate of energy return [8]. This strategy is also referred to as “range extender” [9]. • Maximum Zero Emission Vehicle (ZEV) Mileage Operating Strategy, which will run the HEV in EV mode for as great a distance as possible. • Greenzone Operating Strategy, which utilises the EV mode in Greenzones (regions in which emissions/noise pollution is required to be low, typically urbanised areas). • Mixed Mode Charge-Depletion Operating Strategy, in which the vehicle is governed by an operating strategy that does not require ZEV operation at any point in time. Certain hybrid configurations allow the hybrids to operate in full ZEV/EV mode, either controlled by the vehicle powertrain management control or by the driver. Driver selection of the ZEV mode may be required for the Greenzone Operating Strategy over distances less than or equal to the EV range (Renault Scenic, Fiat Multipla, Peugeot SA Dynactive, Renault Kangoo). In this case the driver will require information about the state of the ESS to determine his decision. Some vehicle configurations mean that a pure EV mode is not possible, for instance where the electric motor is permanently coupled with the combustion engine. (Honda Insight, Peugeot SA Dynalto). Mild hybrids fall into this category unless (as in the project being discussed here) the engine is able to be decoupled.

1.3 Grid Charging As the prime mover supplies less than the average power demand for a charge-depleting HEV, battery charging from the electrical transmission network is required, which leads to the common term; Plug-in Hybrids. Considerations for this type of charging are the available charging infrastructure, the vehicle architecture and customer acceptance [10]. 1.4 Urban Delivery Vehicle Application Heavy goods vehicles making deliveries in urban areas undergo heavy start/stop cycling where the conventional diesel powertrain operates inefficiently. Prototype heavy duty hybrids, such as produced by Navistar (charge sustaining, 25x180Ah), Eaton (charge sustaining Li-Ion), or Mercedes Benz 1217 Atego (charge depleting 33x180Ah) and others [11] have demonstrated combinations of improved fuel efficiency and emission reductions that have raised awareness on the part of commercial operators. Research in this area at Imperial College London focuses on a 7.5 tonne delivery vehicle operating predominantly in urban areas [12] . Figure 2 shows the powertrain layout for this vehicle.

Figure 2 Powertrain Layout

This powertrain meets the operating criteria that require EV mode below a defined speed. Above this speed the diesel engine provides vehicle power and some battery recharging. 1.5 Vehicle Specifications While operating electrically, the vehicle has a rated 40kW power requirement, provided via a 288V DC bus. For the power electronic switching device, peak current is limited to 320Amps. The batteries are situated in two locations of 1500mm x 600mm x 400mm shown in Figure 3 either side of the vehicle. The maximum battery mass is 1500kg. Above this, it becomes unfeasible to operate the vehicle due to reduction in cargo capacity.

Figure 3 Battery Pack Position

1.6 Vehicle Battery Control Strategy The vehicle operates with a charge-depleting battery control strategy, to accommodate ZEV operation in urban areas. This is facilitated by charging from the electrical transmission network when the vehicle returns to the depot each evening. The specific strategies that accommodate these requirements are the Greenzone Operating Strategy and the Mixed mode charge-depletion Operating Strategy. For the Mixed mode charge-depletion Operating Strategy, EV operation is based on the vehicle speed. The speed at which the engine starts operating (defined as the engine cut-in speed) has initially been set to 25km/h. 1.7 Battery Technology Selection For reasons of cost, proven technology and the available expertise in the modelling of the batteries, Lead Acid has been selected as an appropriate chemistry.

2. Drive Cycle and Simulation The drive cycle has been collated from approximately fifty hours of vehicle monitoring data, using a combined accelerometer and GPS system (Figure 4) in London [13].

Figure 4 GPS antenna on vehicle roof, IMU in cabin [13]

The data collected was bound by engine-off periods and separated into microtrips by periods of zero speed. The microtrips were statistically compared over nine parameters which are representative of driving patterns. The average variance based on each of these parameters was calculated, such that microtrips within 10% of the mean were used to generate an artificial cycle (NL-ART). Weighting factors were used to yield a cycle with characteristics relevant to this study.

Table 1 Statistical comparison of the NL-ART [13] cycle with a week’s data Total Average Speed (m/s) Average Running Speed (m/s) Average Acceleration (m/s2) Average Deacceleration (m/s2) Acceleration mode (%) Cruise mode (%) Decceleration mode (%) Idle mode (%) Avg. Num. of Acc-Decc Changes Avg Distance per Microtrip (m) Avg Microtrip Duration (s) Acceleration RMS (m/s2) Maximum Speed (km/h)

4.69 6.36 0.95 -0.97 30% 13% 27% 30% 8.66 459.37 72.26 0.87 71.71

NL-ART 4.44 6.01 0.91 -0.93 30% 13% 27% 30% 8 517.69 86.12 0.83 56.12

The resulting drive cycle is 32 minutes covering a distance of 8.8km, as shown in Figure 5. One day of driving was simulated as five consecutive cycles, representing ~30% of the time that the vehicle is driven, covering a distance of 44km. Table 2 London Delivery Truck Statistical Data

Vehicle stops, where the engine was switched off (for breaks / deliveries) were found to average just over 9 minutes. Battery recovery during these periods was neglected, and engine recharging of the battery was not possible with the engine off since the driver is required to switch off the engine whilst making a delivery. With grid recharging assumed to take place at the depot each night (taking the battery back to an assumed full SOC), simulation was used to determine the extent of battery discharge experienced during one day.

Figure 5 Statistically Generated North London Delivery Drive Cycle (NL-ART) [13]

Software developed at Imperial College London [14] was used to simulate the vehicle operation. Aerodynamic, inertial, rolling resistance, mechanical transmission and motor efficiency losses were taken into account, for the vehicle powertrain layout shown in Figure 6.

Engine

Arrows Indicating Power Flow

Chassis

Wheels

Transmission

MotorGenerator

Controller

Battery Pack

Figure 6 Simulation Powertrain Layout

The battery model is based on a hydrodynamic analogy model developed at Imperial College London [15], and takes into account power degradation with lowering SOC, charging efficiency and internal resistance. This relates well to other modelling methodologies [16]. Capacity (and degradation of capacity through memory effect and cycling) also affects the DOD versus cycles curve [3]. The effects of this were not included in the model for this study.

3. Analysis of the Greenzone Operating Strategy The driver controlled Greenzone Operating Strategy will allow the vehicle to function as a ZEV while operating in urban areas. Factors which affect the ability to achieve this are battery capacity and cargo mass. There is no operating criteria requirement for the EV range of the vehicle, although the intention is for it to operate in EV mode during the core of the day in dense urban areas. 3.1 Useful EV Range The useful EV range of the vehicle can be taken as the vehicle being able to meet the significant requirements of the drive cycle (90-100%). EV ranges for meeting at least 90%, 95% and 100% of the drive cycle are shown for one string and two strings in parallel (Figures 7-9). Battery capacities from 50Ah to 200Ah were considered (being within the cost/size requirements). Vehicle mass includes cargo and battery pack mass, as well as a 3.5 tonne chassis.

Figure 7 EV Range for 90% Drive Cycle Agreement for One String (left) and Two Strings (right) in Parallel



In comparison the Mercedes Benz 1217 Atego has an EV range of 30km fully laden [17].

4. Analysis of the Mixed Mode Charge-Depletion Operating Strategy The cargo mass transported and loss of capacity are key factors in the sizing of a battery pack, together with providing a similar performance to the conventional, diesel powertrain vehicle. Larger cargo mass will put additional strain on the battery pack with higher discharge rates for a given cycle, reducing battery life. 4.1 Sensitivity of the Mixed Mode Charge-Depletion Operating Strategy The charge-depleting control strategy chosen is more likely put the hybrid through deeper cycling of the batteries and will ultimately lead to a shorter battery pack life. The speed-based strategy is intended to allow the slow traffic-dictated urban operation to be pure EV and the faster ‘highway’ drive to be predominantly diesel. A single string of batteries was determined to be unable to sustain the power requirements for the duration of the day. Using one string, the engine cut-in speed became extremely sensitive, with a small increase changing the batteries from being overcharged to depleting or failing quickly. This marginality was heavily dependent on cargo mass, and as a result a single string of 24 batteries is not considered for this strategy.

4.2 Battery Capacity Battery capacities from 50Ah to 200Ah were considered (in two parallel strings of 24), and vehicle mass indicated includes cargo and battery pack mass. Figure 10 illustrates battery SOC for various battery capacities and vehicle masses.

Increasing SOC

Figure 10 Simulated DOD During One Day versus Vehicle Mass for Specific Battery Pack.

4.3 Life Prediction There is very little published data available for the modelling of battery life, other than from manufacturers or analytical models [18]. Figure 11 shows battery life for an AGM VRLA battery, based on DOD.

Figure 11 AGM VRLA Cycle Life versus DOD, adapted from [19]

Combining the data from figures 10 and 11, the life of the battery pack can be predicted. Assuming 250 days of operation a year, and a 50% payload, the battery pack is predicted to have a life of 10-16 years

depending on chosen battery capacity. Figure 12 illustrates the relation between mass, battery pack capacity and battery cycle life.

Increasing Cycle Life

Figure 12 Simulated Capacity versus Cycle Life

5. Discussion A charge-depleting strategy inherently causes more severe demands on the battery pack, but the ready availability of grid recharging at the depot, makes this an ideal strategy, and allows operation in a pure EV mode, which is not available under a charge sustaining strategy. Higher capacity or battery pack size also means a reduced cargo capacity, which equates to lost earnings, although higher EV range. Higher capacity / pack size also means lower DOD and therefore longer battery pack life and lower associated costs. Higher battery capacity will lead to reduced fuel consumption / lower emissions, weighted against higher grid usage. A full cost analysis would be necessary to evaluate this, but is beyond the scope of this study which is primarily a hardware evaluation. For overall efficiency, fuel consumption and network energy consumption would need to be evaluated. Final assessment may determine that overall efficiency is less important than the ability of EV operation in Greenzones.

6. References 1. Madeka, F.C., G.W. Davis, and G.L. Hodges. The selection of lead-acid batteries for use in hybrid electric vehicles, SAE technical paper number 940338. in SAE International Congress and Exposition. 1994. Detroit, Michigan: Society of Automotive Engineers (SAE). 2. Nelson, R.F., Power requirements for batteries in hybrid electric vehicles. Journal of Power Sources, 2000. 91(1): p. 2-26. 3.

Dhameja, S., Electric Vehicle Battery Systems. 2002: Newnes.

4. G. Rizzoni, Y.G., Computer Control Makes For Better Hybrid Electric Vehicles, in Ohio State University Website http://www.osu.edu/researchnews/archive/hev.htm. 5. System Controller Architecture for a Hybrid Electric Truck, Center for Environmental Research and Technology, Bourns College of Engineering Website: http://www.cert.ucr.edu/research/. 6. Energy Management and System http://www.ott.doe.gov/hev/energy_management.html.

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US

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Website:

7. An, F., Stodolsky, J, Eberhardt, J. Fuel Emission Impacts of Heavy Hybrid Vehicles. in 32nd International Symposium on Automobile Technology and Automation. 14 to 18 June 1999. Vienna, Austria. 8. Graham, R., Thomas, B., Duvall, M. Development of Plug-in Hybrid Electric Light and Medium-Duty Commercial Vehicles. in EVS20. 2003. Califormia. 9. Riley, R.Q., DIFFERENT ROADS: Personal Mobility in the 21st Century, Robert Q Riley Enterprises: http://www.rqriley.com/sld007t.htm. 10. Badin, F., Jeanneret, B., Trigui, R. Harel, F. Hybrid Vehicles, should we plug them to the grid or not? in The 18th International Electric Vehicle Symposium. 2001. Berlin, Germany. 11. Eudy, L., Heavy-Duty Hybrid Vehicle Projects. 13th November 2003, National Renewable Energy Lab: http://www.ott.doe.gov/otu/field_ops/excel/hd_hev.xls. 12.

Mihpow Project, http://www.hybridtruck.org. 2003.

13. Marquis, I.R., Investigation into the Driving Cycles for a Delivery Van around London to Assess Environmental Benefits and Assist Performance Simulation of a Hybrid-Electric Drivetrain, in Mechanical Engineering. 2003, Imperial College: London. 14. Wilkins, S., Lampérth, M U. The Development of an Object-Oriented Tool for the Modeling and Simulation of Hybrid Powertrains for Vehicular Applications. in SAE International Future Transportation Technology Conference. 2003. Costa Mesa USA. 15. Lampérth, M.U., Pullen, K R, Etemad, M R. Development of an Analysis Software to Allow Electromechanical Battery Modelling for Electric and Hybrid Vehicles. in 30th ISATA Electric, Hybrid and Alternative Fuel Vehicles. 1997. Florence. 16. Inc., T., Battery Modeling, http://www.thermoanalytics.com/support/publications/batterymodelsdoc.html. 17.

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Concorde, http://www.concordebattery.com/. 2003.

7. Authors

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Steven Wilkins: Researcher in hybrid powertrain modeling focusing on HEVs, simulation software techniques and hardware-the-loop simulation. Has performed studies on onboard energy storage devices and electric braking, heavy vehicle subsystems. E-mail: [email protected] Hybrid Power Research Group, C.A.S.E., Department of Mechanical Engineering, Imperial College of Science, Technology & Medicine, London, Tel: +44-20-7594-7024 Fax: +4420-7584-7127 URL: http://www.me.ic.ac.uk/hybridpower/

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Sejul Shah: Researcher in Flywheel Energy storage, and powertrain design for hybrid vehicles. E-mail: [email protected] Hybrid Power Research Group, C.A.S.E., Department of Mechanical Engineering, Imperial College of Science, Technology & Medicine, London, Tel: +44-20-7594-7024 Fax: +4420-7584-7127 URL: http://www.me.ic.ac.uk/hybridpower/

No photo avail

Alan Walker: Researcher in electric machines for traction and generation applications, and powertrain design for hybrid vehicles. E-mail: [email protected] Hybrid Power Research Group, C.A.S.E., Department of Mechanical Engineering, Imperial College of Science, Technology & Medicine, London, Tel: +44-20-7594-7024 Fax: +4420-7584-7127 URL: http://www.me.ic.ac.uk/hybridpower/

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Michael Lampérth: Lecturer of Design, CAD, Electronics and Instrumentation in the Department of Mechanical Engineering of Imperial College, London. He is head of the hybrid power research group which focuses on sustainable energy technologies, hybrid electric vehicles, including simulation, and permanent magnet machines. E-mail: [email protected] Hybrid Power Research Group, C.A.S.E., Department of Mechanical Engineering, Imperial College of Science, Technology & Medicine, London, Tel: +44-20-7594-7020 Fax: +44-207584-7239 URL: http://www.me.ic.ac.uk/hybridpower/