NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Applying the Battery Ownership Model in 

Pursuit of Optimal Battery Use Strategies

Jeremy Neubauer

National Renewable Energy Laboratory

May 15, 2012

Contributors: Ahmad Pesaran, Aaron Brooker, Eric Wood, 

Kandler Smith, Caley Johnson, and Michael Mendelsohn

Project ID # ES123

This presentation does not contain any proprietary, confidential, or otherwise restricted information.
2

Overview

Project Start Date: 2009

Project End Date: 2015

Percent Complete: 35%

• Battery cost

• Battery life / durability

• Public acceptance of electric 

drive as central vehicle choice

Funding Received in FY09: $200k

Funding Received in FY10: $200k

Funding Received in FY11: $200k

Anticipated Funding in FY12: $250k

Funding provided by Dave Howell of the DOE 

Vehicle Technologies office.

Activity managed by Brian Cunningham of 

the DOE Vehicle Technologies office.

Timeline

Budget

Barriers

• United States Advanced Battery 

Consortium (USABC)

• Better Place

• NREL internal teams:

– Vehicle Systems Analysis

– Transportation Secure Data Center

– Electric Vehicle (EV) Grid Integration

Partners
3

Relevance/Objectives

VTP Light-Duty Vehicle Goals:

• 50 percent reduction in petroleum-based consumption by 2015

• 80 percent of energy from carbon-neutral sources by 2030

VTP Energy Storage Goals:

• Cost Targets: $270/kWh in 2015, $125/kWh in 2020

• Life Targets: 10+ years, 3,000-5,000 cycles

Battery Ownership Model Project Objectives:

• Quantify the total cost of ownership (TCO) and petroleum-based consumption of EV 

technologies under traditional and advanced operational strategies

• Identify optimal battery use strategies to meet VTP-light duty vehicle goals

Battery Ownership Model FY12 Objectives:

• Assess the effect of electric range, charge strategies, and drive patterns on the TCO and 

petroleum-based consumption of plug-in electric vehicles (PEVs) 

• Investigate the economics of battery swapping services for battery electric vehicles (BEVs)
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Milestones

Date Milestone Status

8/31/2011 Complete technical and economic 

evaluation of various EV battery

ownership concepts

Completed. Results highlighted 

the need for better battery 

degradation modeling and drive 

patterns.

3/31/2012 Complete analysis on the impact 

of driving patterns, electric range, 

and charge strategies

Completed.
5

?

Approach

Optimize vehicle to 

spec, calculate cost 

and fuel consumption

Apply drive pattern, 

calculate battery 

degradation through 

replacement period

Calculate total fuel 

consumption and 

cost
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Approach

• Semi-empirical, physically justified model based on 

laboratory life test data for a nickel cobalt aluminum (NCA) 

lithium (Li)-ion battery chemistry

– Calculates capacity loss and resistance growth in both operational and 

storage conditions

– Considers effects of time, number of cycles, state of charge (SOC), 

voltage, temperature, and depth of discharge
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Approach

• Differentiators:

– High-fidelity battery degradation model

– Cost-optimal battery replacement schedules

– Analysis of hundreds of real-world longitudinal drive patterns

– Study trends of cost and fuel use with different vehicle types, configurations, and use strategies

Increment vehicle 

spec

Increment drive 

pattern

Optimize vehicle to spec, 

calculate cost and fuel 

consumption

Increment battery 

replacement period

Apply drive pattern, calculate 

battery degradation through 

replacement period

Calculate total fuel 

consumption and cost
8

Approach

• Differentiators:

– High-fidelity battery degradation model

– Cost-optimal battery replacement schedules

– Analysis of hundreds of real-world longitudinal drive patterns

– Study trends of cost and fuel use with different vehicle types, configurations, and use strategies

– Accounting for the added utility, battery wear, and infrastructure costs of range-extension 

techniques (battery swap, fast charge, public charging, etc.)

– Allow creative ownership and financing options that mitigate risks to consumers

Increment service 

plan

Calculate 

infrastructure 

requirements 

and service 

provider fees

Identify service usage patterns 

from likely subscribers

Calculate total cost 

of ownership (TCO) 

of individual 

subscribers
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Collaboration and Coordination

• Working with industry and heavily leveraging other DOEfunded projects

– Work directly with USABC on battery electric vehicle (BEV) target 

analyses

– Input from Better Place on battery swapping analyses

– Utilize NREL Vehicle Systems Analysis group’s FASTSim for vehicle 

simulation and optimization

– Drive patterns and processing from NREL’s Transportation Secure 

Data Center

– Battery degradation model provided by NREL’s Energy Storage 

team

– Employing ANL’s GREET model for greenhouse gas accounting

– Development of unique capabilities has been supported by NREL 

battery second use, EV grid integration, and Clean Cities projects.
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FY11 Accomplishments

• Identified the need for more advanced modeling 

capabilities and began integrating them

– Performed BEV target analyses, battery swapping 

investigations, and lithium availability studies using 

version 1.0 software

– Identified a strong sensitivity to battery degradation and 

the potential for strong sensitivity to drive patterns

– Began version 2.0 development, restructuring software 

architecture for new battery degradation model and 

large numbers of longitudinal drive patterns

– Incorporated and tested NREL’s high-fidelity battery 

degradation algorithm
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FY12 Accomplishments

• Major software upgrades in FY12

– Incorporated 398 longitudinal drive patterns from Puget Sound 

Regional Council Travel Choices Study (TCS)

– Accounted for a BEV’s unachievable vehicle miles travelled 

(VMT) by two different means

– Developed a cost-optimal battery replacement algorithm

– Developed a battery SOC window expansion control algorithm

– Made significant updates to service provider submodel

– Added battery second use and vehicle-to-grid (V2G) capability
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FY12 Accomplishments

• There’s a lot of variability between drive patterns

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Probability Distribution Function (%)

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Daily Vehicle Miles Traveled (DVMT)

Probability Distribution Function (%)

National Highway Traffic Safety (NHTS) national 

average drive pattern: 

Heavy black line

TCS real-world, vehicle-specific drive patterns:

Light colored lines
13

FY12 Accomplishments

• Drive pattern variability impacts cost, life, and other factors

*Results assume a mid-size sedan vehicle platform, 2012 start year, 15-year vehicle life & analysis period, availability of current federal purchase incentives, 

achievement of DOE battery cost targets ($500/kWh in 2012 to $125/kWh by 2020), 2010 Energy Information Administration (EIA) high-oil-price scenario 

gasoline and electricity cost projections, a 75-mile range, just-in-time charging from home, 100% maximum SOC, high cost of unachievable VMT, and several 

other important factors not listed here.
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FY12 Accomplishments

• Large quantities of data require new ways to 

present results

– BEV study of range, charge strategy, and drive patterns: 

42,876 cases simulated

– Plug-in hybrid electric vehicle (PHEV) study of range, 

charge strategy, and drive patterns: 76,416 cases 

simulated

– We propose the 75th Percentile Cost Ratio, which bounds 

the top 25% of the most cost-effective drive patterns.
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FY12 Accomplishments

• Unachievable VMT has a big effect on cost, 

optimal vehicle configuration, and use strategies

*Results assume a mid-size sedan vehicle platform, 2012 start year, 15-year vehicle life & analysis period, availability of current federal 

purchase incentives, achievement of DOE battery cost targets ($500/kWh in 2012 to $125/kWh by 2020), 2010 EIA high-oil-price scenario 

gasoline and electricity cost projections, 100% max SOC, just-in-time charging, and several other important factors not listed here.
16

FY12 Accomplishments

• Plug-in electric vehicle battery replacements are rarely 

financially justified

– Cost-optimal battery replacement algorithm trades the cost of a 

replacement battery against the savings due to increased electric VMT

– Only BEVs under the high cost of unachievable VMT produce significant 

numbers of financially motivated battery replacements

Increment battery 

replacement period
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Proposed Future Work

• FY12

– Complete battery swapping study

– Perform BEV target analysis for USABC

• FY13

– Scope and complete fast charge study

– Scope and complete V2G analysis

– Scope and complete vehicle-to-building analysis

– Scope and complete battery second use analysis

– Compare all study results to identify cost- and fuel-optimal 

implementation strategies for EVs and future research 

avenues

– Add degradation models for other battery chemistries

– Integrate ANL’s battery cost model (BatPaC)
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Summary

• The Battery Ownership Model is an advanced TCO tool for EVs 

that:

– Uses a unique approach to enable analysis of vehicle and use strategy 

combinations that other calculators cannot

– Integrates modeling tools funded by other DOE-funded projects

– Is being applied to identify pathways to the VTP’s short- and long-term 

goals of reducing petroleum use and carbon footprint by optimizing the 

use of energy storage systems

• FY12 findings show us that:

– The use of real-world, vehicle-specific drive patterns in place of multi- vehicle “averaged” drive patterns is very important

– Accounting for the cost of unachievable travel in range-limited BEV 

analyses has a strong impact on cost-optimal vehicle specification and 

use strategies

– PHEV and BEV battery replacements are rarely financially justifiable

• FY13 work will begin identifying cost- and fuel-optimal EV 

configurations, use strategies, and business models