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The Battery Performance and Cost Model (BatPaC), developed by Argonne National Laboratory, is a versatile tool designed for lithium-ion battery (LIB) pack engineering. It accommodates user-defined specifications, generating detailed bill-of-materials calculations and insights into cell dimensions and pack characteristics. Pre-loaded with default data sets, BatPaC aids in estimating production costs for battery packs produced at scale (5 to 50 GWh annually). Acknowledging inherent uncertainties in parameters, the tool remains accessible and valuable for designers and engineers. BatPaC plays a crucial role in National Highway Transportation Traffic Safety Administration (NHTSA) regulatory assessments, providing estimated battery pack manufacturing costs and weight metrics for electric vehicles. Integrated with Argonne's Autonomie simulations, BatPaC streamlines large-scale processes, replacing traditional models with lookup tables.

Improvements in vehicle technology impact the purchase price of a vehicle and its operating cost. In this study, the monetary benefit of a technology improvement includes the potential reduction in vehicle price from using cheaper or smaller components, as well as the discounted value of the fuel cost savings. As technology progresses over time, the value and benefit of improving technology varies as well. In this study, the value of improving a few selected technologies (battery energy density, electric drive efficiency, tire rolling resistance, aerodynamics, light weighting) is studied and the value of the associated cost saving is quantified. The change in saving as a function of time, powertrain selection and vehicle type is also quantified. For example, a 10% reduction in aerodynamic losses is worth $24,222 today but only $8,810 in 2030 in an electric long haul truck. The decrease in value is primarily due to expected battery cost reduction over time.

As vehicles are getting electrified and more intelligent, the energy consumption of the auxiliary system increases rapidly. The auxiliary battery acts as the backbone of the system to support the proper operation of the vehicle. It is important to ensure the auxiliary battery has enough energy to meet the basic loads regardless the vehicle is in park or running. However, the existing methods only focus on auxiliary energy management when the vehicle is in a dynamic event. To fulfill the gap, we propose an intelligent strategy that detects the low state of charge (SOC) condition, temporarily turns down the auxiliary loads based on their priorities and charges the auxiliary battery at the maximum efficiency of the auxiliary power unit. In addition, the proposed strategy allows the vehicle to get the park duration update and make intelligent decisions on charging the auxiliary battery.

The objective of this study was to evaluate the fuel saving potential of various hybrid powertrain architectures for medium and heavy duty vehicles. The relative benefit of each powertrain was analyzed, and the observed fuel savings was explained in terms of operational efficiency gains, regenerative braking benefits from powertrain electrification and differences in vehicle curb weight. Vehicles designed for various purposes, namely urban delivery, utility, transit, refuse, drayage, regional and long haul were included in this work. Fuel consumption was measured in regulatory cycles and various real world representative cycles. A diesel-powered conventional powertrain variant was first developed for each case, based on vehicle technical specifications for each type of truck. Autonomie, a simulation tool developed by Argonne National Laboratory, was used for carrying out the vehicle modeling, sizing and fuel economy evaluation.

The adoption of lithium-ion batteries in vehicle electrification is fast growing due to high power and energy demand on hybrid and electric vehicles. However, the battery overall performance changes with time through the vehicle life. This paper investigates the electric vehicle battery cell aging under different usages. Battery cell experimental data including open circuit voltage and internal resistance is utilized to build a typical electric vehicle model in the AVL-Cruise platform. Four driving cycles (WLTP, UDDS, HWFET, and US06) with different ambient temperatures are simulated to acquire the battery cell terminal currents. These battery cell terminal current data are inputs to the MATLAB/Simulink battery aging model. Simulation results show that battery degrades quickly in high ambient temperatures. After 15,000 hours usage in 50 degrees Celsius ambient temperature, the usable cell capacity is reduced up to 25%.

An increase in spark-ignition engine efficiency can be gained by increasing the engine compression ratio, which requires fuels with higher knock resistance. Oxygenated fuel components, such as methanol, ethanol, isopropanol, or iso-butanol, all have a Research Octane Number (RON) higher than 100. The octane numbers (ON) of fuels are rated on the CFR F1/F2 engine by comparing the knock intensity of a sample fuel relative to that of bracketing primary reference fuels (PRF). The PRFs are a binary blend of iso-octane, which is defined to an ON of 100, and n-heptane, which represents an ON of 0. Above 100 ON, the PRF scale continues by adding diluted tetraethyl lead (TEL) to iso-octane. However, TEL is banned from use in commercial gasoline because of its toxicity. The ASTM octane number test methods have a “Fit for Use” test that validate the CFR engine’s compliance with the octane testing method by verifying the defined ON of toluene standardization fuels (TSF).

Lithium-ion polymer battery has been widely used for vehicle onboard electric energy storage ranging from 12V SLI (Starting, Lighting, and Ignition), 48V mild hybrid electric, to 300V battery electric vehicle. Formulation on cell parameters acquired from minimum numbers of experiments, the modeling and simulation could be an effective approach in predicting battery performance, thermal effectiveness, and degradation. This paper describes the modeling, simulation, and validation of Lithium-Nickel-Manganese-Cobalt-Oxide (LiNiMnCoO2) based cell with 3.6V nominal voltage and 20Ah capacity. Constant current 20A, 40A, 60A, and 80A discharge tests are conducted in the computer-controlled cycler and temperature chamber. Discharging voltage curves and cell surface temperature distributions are recorded in each discharging test. A three-dimensional cell model is constructed in the COMSOL multi-physics platform based on the cell parameters.

With the mass movement toward electrification and renewable technologies, the scope of innovation of electrification has gone beyond the automotive industry into areas such as electric motorcycle applications. This paper provides a discussion of the methodology and complexities of converting an internal combustion motorcycle to an electric motorcycle. In developing this methodology, performance goals including, speed limits, range, weight, charge times, as well as riding styles will be examined and discussed. Based on the goals of this paper, parts capable of reaching the performance targets are selected accordingly. Documentation of the build process will be presented along with the constraints, pitfalls, and difficulties associated with the process of the project. The step-by-step process that is developed can be used as a guideline for future build and should be used as necessary.

As emerging automated vehicle technology is making advances in safety and reliability, engineers are also exploring improvements in energy efficiency with this new paradigm. Powertrain efficiency receives due attention, but also impactful is finding ways to reduce driving losses in coordinated-driving scenarios. Efforts focused on simulation to quantify road load improvements require a sufficient amount of background validation work to support them. This study uses a practical approach to directly quantify road load changes by testing the coordinated driving of two vehicles on a test track at various speeds (64, 88, 113 km/h) and vehicle time gaps (0.3 to 1.3 s). Axle torque sensors were used to directly measure the load required to maintain steady-state speeds while following a lead vehicle at various gap distances.

The Toyota Prius Prime is a new generation of Toyota Prius plug-in hybrid electric vehicle, the electric drive range of which is 25 miles. This version is improved from the previous version by the addition of a one-way clutch between the engine and the planetary gear-set, which enables the generator to add electric propulsive force. The vehicle was analyzed, developed and validated based on test data from Argonne National Laboratory’s Advanced Powertrain Research Facility, where chassis dynamometer set temperature can be controlled in a thermal chamber. First, we analyzed and developed components such as engine, battery, motors, wheels and chassis, including thermal aspects based on test data. By developing models considering thermal aspects, it is possible to simulate the vehicle driving not only in normal temperatures but also in hot, cold, or warmed-up conditions.

Lead-acid batteries have dominated the automotive conventional electric system, particularly in the functions of starting (S), lighting (L) and ignition (I) for decades. However, the low energy-to-weight ratio and the low energy-to-volume ratio makes the lead-acid SLI battery relatively heavy, large, and shallow Depth of Discharge (DOD). This could be improved by replacing the lead-acid battery by the lithium-ion polymer battery. The lithium-ion polymer battery can provide the same power with lightweight, compact volume, and deep DOD for engine idle elimination using start-stop function that is a basic feature in electric-drive vehicles. This paper presents the modeling and validation of a lithium-ion battery for SLI application. A lithium-metal-oxide based cell with 3.6 nominal voltage and 20Ah capacity is used in the study. A simulation model of lithium-ion polymer battery pack (14.4V, 80Ah) with battery management system is built in the MATLAB/Simulink environment.

A new type of approval procedure for light-duty vehicles, the Worldwide harmonized Light vehicles Test Procedure (WLTP), developed by an initiative of the United Nations Economic Commission for Europe, will come into force by the end of 2017. The current European type-approval procedure for energy consumption and CO2 emissions of cars, the New European Driving Cycle (NEDC), includes a number of tolerances and flexibilities that no longer accurately reflect state-of-the-art technologies. Indeed, on the basis of an analysis of real-world driving data from the German website spritmonitor.de, the ICCT concluded that the differences between official laboratory and real-world fuel consumption and CO2 values were around 7% in 2001. This discrepancy has been increasing continuously since then to around 30% in 2013, with notable differences found between individual manufacturers and vehicle models.

Reductions in vehicle drive losses are as important to improving fuel economy as increases in powertrain efficiencies. In order to measure vehicle fuel economy, chassis dynamometer testing relies on accurate road load determinations. Road load is currently determined (with some exceptions) using established test track coastdown testing procedures. Because new vehicle technologies and usage cases challenge the accuracy and applicability of these procedures, on-road experiments were conducted using axle torque sensors to address the suitability of the test procedures in determining vehicle road loads in specific cases. Whereas coastdown testing can use vehicle deceleration to determine load, steady-state testing can offer advantages in validating road load coefficients for vehicles with no mechanical neutral gear (such as plug-in hybrid and electric vehicles).

Validation of a vehicle simulation model of the Chevrolet Volt 2016 was conducted. The Chevrolet Volt 2016 is equipped with the new “Voltec” extended-range propulsion system introduced into the market in 2016. The second generation Volt powertrain system operates in five modes, including two electric vehicle modes and three extended-range modes. Model development and validation were conducted using the test data performed on the chassis dynamometer set in a thermal chamber of Argonne National Laboratory’s Advanced Powertrain Research Facility. First, the components of the vehicle, such as the engine, motor, battery, wheels, and chassis, were modeled, including thermal aspects based on the test data. For example, engine efficiency changes dependent on the coolant temperature, or chassis heating or air-conditioning operations according to the ambient and cabin temperature, were applied.

Many leading companies in the automotive industry have been putting tremendous amount of efforts into developing new designs and technologies to make their products more energy efficient. It is straightforward to evaluate the fuel economy benefit of an individual technology in specific systems and components. However, when multiple technologies are combined and integrated into a whole vehicle, estimating the impact without building and testing an actual vehicle becomes very complex, because the efficiency gains from individual components do not simply add up. In an early concept phase, a projection of fuel efficiency benefits from new technologies will be extremely useful; but in many cases, the outlook has to rely on engineer’s insight since it is impractical to run tests for all possible technology combinations.

The control analysis and model validation of a 2014 BMW i3-Range Extender (REX) was conducted based on the test data in this study. The vehicle testing was performed on a chassis dynamometer set within a thermal chamber at the Advanced Powertrain Research Facility at Argonne National Laboratory. The BMW i3-REX is a series-type plug-in hybrid range extended vehicle which consists of a 0.65L in-line 2-cylinder range-extending engine with a 26.6kW generator, 125kW permanent magnet synchronous AC motor, and 18.8kWh lithium-ion battery. Both component and vehicle model including thermal aspects, were developed based on the test data. For example, the engine fuel consumption rate, battery resistance, or cabin HVAC energy consumption are affected by the temperature. Second, the vehicle-level control strategy was analyzed at normal temperature conditions (22°C ambient temperature). The analysis focuses on the engine on/off strategy, battery SOC balancing, and engine operating conditions.

Today’s value proposition of plug-in hybrid electric vehicles (PHEV) and battery electric vehicles (BEV) remain expensive. While the cost of lithium batteries has significantly decreased over the past few years, more improvement is necessary for PHEV and BEV to penetrate the mass market. However, the technology and cost improvements of the primary components used in electrified vehicles such as batteries, electric machines and power electronics have far exceeded the improvements in the main components used in conventional vehicles and this trend is expected to continue for the foreseeable future. Today’s weight and cost structures of electrified vehicles differ substantially from that of conventional vehicles but that difference will shrink over time. This paper highlights how the weight and cost structures, both in absolute terms and in terms of split between glider and powertrain, converge over time.

It is widely understood that cold ambient temperatures negatively impact vehicle system efficiency. This is due to a combination of factors: increased friction (engine oil, transmission, and driveline viscous effects), cold start enrichment, heat transfer, and air density variations. Although the science of quantifying steady-state vehicle component efficiency is mature, transient component efficiencies over dynamic ambient real-world conditions is less understood and quantified. This work characterizes wheel assembly efficiencies of a conventional and electric vehicle over a wide range of ambient conditions. For this work, the wheel assembly is defined as the tire side axle spline, spline housing, bearings, brakes, and tires. Dynamometer testing over hot and cold ambient temperatures was conducted with a conventional and electric vehicle instrumented to determine the output energy losses of the wheel assembly in proportion to the input energy of the half-shafts.

The energy density and power density comparison of conventional fuels and batteries is often mentioned as an advantage of conventional vehicles over electric vehicles. Such an analysis often shows that the batteries are at least an order of magnitude behind fuels like gasoline. However this incomplete analysis ignores the impact of powertrain efficiency and mass of the powertrain itself. When we compare the potential of battery electric vehicles (BEVs) as an alternative for conventional vehicles, it is important to include the energy in the fuel and their storage as well as the eventual conversion to mechanical energy. For instance, useful work expected out of a conventional vehicle as well as a BEV is the same (to drive 300 miles with a payload of about 300 lb). However, the test weight of a Conventional vehicle and BEV will differ on the basis of what is needed to convert their respective stored energy to mechanical energy.

Present-day battery technologies support a battery electric vehicle with a 300mile range (BEV 300), but the cost of such a vehicle hinders its large-scale adoption by consumers. The U.S. Department of Energy (DOE) has set aggressive cost targets for battery technologies. At present, no single technology meets the cost, energy, and power requirements of a BEV 300, but a combination of multiple batteries with different capabilities might be able to lower the overall cost closer to the DOE target. This study looks at how such a combination can be implemented in vehicle simulation models and compares the vehicle manufacturing and operating costs to a baseline BEV 300. Preliminary analysis shows an opportunity to modestly reduce BEV 300 energy storage system cost by about 8% using a battery pack that combines an energy and power battery. The baseline vehicle considered in the study uses a single battery sized to meet both the power and energy requirements of a BEV 300.