Accelerated Testing of Advanced Battery Technologies in PHEV Applications

Plug-in hybrid electric vehicle (PHEV) presents the new trend of clean energy vehicle development due to their advantage of all electric driving, allowable external charging, lower emissions, and lower petroleum fuel consumption, comparing to the maturing HEV. Applications of advanced plug-in hybrid powertrain system technology to different type of vehicles lead to the need to identify the key characteristics of these different vehicular applications, and to develop more systematic and effective powertrain design methods. In this work two different types of PHEV powertrain architecture were investigated: a) pre- trans series-parallel multi-regime plug-in hybrid electric commercial vehicle (SPMR-PHEV), and b) post-trans parallel plug-in hybrid electric formula racing car. Model-based design (MBD) methods were used for powertrain system modelling of the two PHEV applications. With the powertrain system models developed using MATLAB Simulink and dSPACE Automotive Simulation Models (ASM), the powertrain configurations, control strategies and key features were investigated. The simulation results on standard driving cycles for each model using different rule-based control strategies were compared. The differences and key features of two types of vehicle in design and calibration were presented and analyzed.

Plug-in hybrid electric vehicle (PHEV) technology has the potential to reduce operating cost, greenhouse gas (GHG) emissions, and petroleum consumption in the transportation sector. However, the net effects of PHEVs depend critically on vehicle design, battery technology, and charging frequency. To examine these implications, we develop an optimization model integrating vehicle physics simulation, battery degradation data, and U.S. driving data. The model identifies optimal vehicle designs and allocation of vehicles to drivers for minimum net life cycle cost, GHG emissions, and petroleum consumption under a range of scenarios. We compare conventional and hybrid electric vehicles (HEVs) to PHEVs with equivalent size and performance (similar to a Toyota Prius) under urban driving conditions. We find that while PHEVs with large battery packs minimize petroleum consumption, a mix of PHEVs with packs sized for ∼25–50 miles of electric travel under the average U.S. grid mix (or ∼35–60 miles under decarbonized grid scenarios) produces the greatest reduction in life cycle GHG emissions. Life cycle cost and GHG emissions are minimized using high battery swing and replacing batteries as needed, rather than designing underutilized capacity into the vehicle with corresponding production, weight, and cost implications. At 2008 average U.S. energy prices, Li-ion battery pack costs must fall below $590/kW h at a 5% discount rate or below $410/kW h at a 10% rate for PHEVs to be cost competitive with HEVs. Carbon allowance prices offer little leverage for improving cost competitiveness of PHEVs. PHEV life cycle costs must fall to within a few percent of HEVs in order to offer a cost-effective approach to GHG reduction.

Plug-in hybrid electric vehicles (PHEVs) are being considered as an alternative to conventional medium-duty (MD) and heavy-duty (HD) commercial vehicles to reduce fuel consumption and tailpipe emissions. Lithium ion batteries, which are used in PHEVs due to their high energy density, are expensive. The battery contributes significantly towards the life-cycle cost of MD/HD PHEVs, as these vehicles, due to high mass and aggressive battery usage, require multiple battery replacements over their lifetime. Smaller batteries increase the fuel consumption and need more replacements, while bigger batteries increase the initial system cost. Powertrain design from a life-cycle cost perspective is required to explore this trade-off and maximize the economic gains obtained from PHEVs. Powertrain design entails component sizing, control strategy selection as well as architecture selection. Different powertrain designs yield different lifetime economic gains. A variety of applications exist for MD/HD vehicles, which differ in their ways of powertrain usage, due to variations in required acceleration, available braking, and average and maximum speeds. Therefore, different powertrain designs are needed depending on the application and usage scenario. The powertrain design space needs to be explored, and solutions that maximize the economic gains within the specified constraints need to be chosen.This dissertation compares the economic viability of two PHEV applications (MD Truck and HD Transit Bus), with options of series and parallel hybrid architectures, over multiple drivecycles, for four economic scenarios (years 2015, 2020, 2025 and 2030). It is shown that hybridizing the transit bus achieves payback sooner than hybridizing the truck. Further, the results for the transit bus application, over the Manhattan drivecycle, show that implementation of the parallel architecture is economically viable in the 2015(present) scenario, while the series architecture becomes viable in 2020, due to significantly lower initial costs involved in the parallel architecture.A methodology to select a solution out of the explored design space that maximizes the economic gains is demonstrated. Variations in the economic and vehicle usage conditions for which this solution is designed, can be expected. It is therefore necessary to check the robustness of this solution to change in external factors such as vehicle mass, annual vehicle miles travelled (AVMT), component and fuel costs. It is shown that the economic gains are affected by the battery cost, fuel cost, AVMT and vehicle mass, while the number of battery replacements are affected by AVMT and vehicle mass. A probability-based approach is demonstrated to obtain confidence in the economic and battery life predictions. Specifically, probability-based variations are provided to variables such as miles traveled between recharge, recharge C-rate and battery temperature. It is shown that battery life is affected the most by battery temperature.A battery heating/cooling system is required to maintain constant battery temperature of operation during all seasons, but these systems incur additional fuel costs. A framework that utilizes just the Coefficient of Performance (COP) of the heating/cooling system to calculate the excess fuel cost is proposed and demonstrated. An increase of 0.9-1.8\% in fuel consumption is shown, depending on the drivecycle and ambient temperature.Further, the well-to-wheel (WTW) fuel-cycle emissions from conventional and PHEV transit buses operating in Indiana and California are assessed using the ``Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation'' (GREET) Model 2017, developed by Argonne National Labs. It is shown that 59% and 63% greenhouse gas (GHG) reductions can be achieved in Indiana and California respectively, along with reduction in carbon monoxide (CO), nitrogen oxides NOx, particulate matter with diameter less than 2.5 microns PM2.5 and volatile organic compounds (VOC) emissions for both the states. However, an increase in sulfur oxides SOx emissions for both the states, and particulate matter with diameter less than 10 microns PM10 increase for Indiana, are observed.

This paper presents a new approach to life estimation for lithium-ion batteries used in plug-in hybrid electric vehicles (PHEVs) applications. A new framework for battery life estimation is developed which investigates the effects of two primary factors of battery life reduction in PHEVs applications, namely, depth of discharge (DOD) and temperature (Tbatt), under typical driving conditions, driving habits, and average commute time of typical user over a year. This framework, whose development is built upon a weighted ampere-hour throughput model of the battery, is based on the novel concept of severity factor map which captures and quantifies the battery damage caused by different operating conditions. The proposed methodology can be a suitable tool to estimate battery life in terms of miles/year on-board of the vehicle.

Plug-in hybrid electric vehicles (PHEVs) have potential to reduce greenhouse gas (GHG) emissions in the U.S. light-duty vehicle fleet. GHG emissions from PHEVs and other vehicles depend on both vehicle design and driver behavior. We pose a twice-differentiable, factorable mixed-integer nonlinear programming model utilizing vehicle physics simulation, battery degradation data, and U.S. driving data to determine optimal vehicle design and allocation for minimizing lifecycle greenhouse gas (GHG) emissions. The resulting nonconvex optimization problem is solved using a convexification-based branch-and-reduce algorithm, which achieves global solutions. In contrast, a randomized multistart approach with local search algorithms finds global solutions in 59% of trials for the two-vehicle case and 18% of trials for the three-vehicle case. Results indicate that minimum GHG emissions is achieved with a mix of PHEVs sized for around 35 miles of electric travel. Larger battery packs allow longer travel on electric power, but additional battery production and weight result in higher GHG emissions, unless significant grid decarbonization is achieved. PHEVs offer a nearly 50% reduction in life cycle GHG emissions relative to equivalent conventional vehicles and about 5% improvement over ordinary hybrid electric vehicles. Optimal allocation of different vehicles to different drivers turns out to be of second order importance for minimizing net life cycle GHGs.

The rising concerns over global climate change and depleting fossil fuel reserves are two of the main reasons for the ongoing efforts towards the electrification of the transportation sector. While greenhouse gases (GHGs) emissions from other sectors are generally falling, emissions from the road transport have increased over the past few decades, with both full electric vehicles (FEVs) and plug-in hybrid electric vehicles (PHEVs) being recognized as potential alternatives to combat climate change and reduce GHG emissions. However, wide-spread integration of FEVs and PHEVs will substantially increase the load on the power system which will eventually affect the reliability of existing power systems. In this paper, a probabilistic model for integrating FEVs and PHEVs with existing power grids is proposed that incorporates important FEV and PHEV characteristics, such as battery capacity, charge depleting distance, and charging rates. In addition, user behavior is taken into account through time of recharging, arrival and departure times, and daily miles driven. Furthermore, different charging strategies, i.e., opportunistic charging and controlled charging with and without vehicle-to-grid (V2G) scheme have been considered to evaluate the impact of FEVs and PHEVs on the composite power system. IEEE-RTS-79 system is used to examine the proposed probabilistic technique considering different FEV and PHEV penetration levels as well as charging strategies. Simulation results show that even a relatively low penetration level of FEVs or PHEVs might have a significant impact on the system reliability unless a proper charging and/or discharging schemes are utilized.

A key issue associated with the wider adoption of hybrid-electric vehicles (HEV) and plug in hybrid-electric vehicles (PHEV) is the implementation of the power electronic systems that are required in these products. One of the primary industry goals is the reduction in the price of these vehicles relative to the cost of traditional gasoline powered vehicles. Today these systems, such as the Prius, utilize one coolant loop for the engine at approximately 100 C coolant temperatures, and a second coolant loop for the inverter at 65 C. One way in which significant cost reduction of these systems could be achieved is through the use of a single coolant loop for both the power electronics as well as the internal combustion engine (ICE). This change in coolant temperature significantly increases the junction temperatures of the devices and creates a number of challenges for both device fabrication and the assembly of these devices into inverters and converters for HEV and PHEV applications. Traditional power modules and the state-of-the-art inverters in the current HEV products, are based on chip and wire assembly and direct bond copper (DBC) on ceramic substrates. While a shift to silicon carbide (SiC) devices from silicon (Si) devices would allow the higher operating temperatures required for a single coolant loop, it also creates a number of challenges for the assembly of these devices into power inverters. While this traditional packaging technology can be extended to higher temperatures, the key issues are the substrate material and conductor stability, die bonding material, wire bonds, and bond metallurgy reliability as well as encapsulation materials that are stable at high operating temperatures. The larger temperature differential during power cycling, which would be created by higher coolant temperatures, places tremendous stress on traditional aluminum wire bonds that are used to interconnect power devices. Selection of the bond metallurgy and wire bond geometry can play a key role in mitigating this stress. An alternative solution would be to eliminate the wire bonds completely through a fundamentally different method of forming a reliable top side interconnect. Similarly, the solders used in most power modules exhibit too low of a liquidus to be viable solutions for maximum junction temperatures of 200 C. Commonly used encapsulation materials, such as silicone gels, also suffer from an inability to operate at 200 C for extended periods of time. Possible solutions to these problems exist in most cases but require changes to the traditional manufacturing process used in these modules. In addition, a number of emerging technologies such as Si nitride, flip-chip assembly methods, and the elimination of base-plates would allow reliable module development for operation of HEV and PHEV inverters at elevated junction temperatures.

Plug-in hybrid electric vehicles (PHEVs) are specialized hybrid electric vehicles that have the potential to obtain enough energy for average daily commuting from batteries. These batteries would be charged from the power grid and would thus allow for a reduction in the overall petroleum consumption. To implement the plug-in function, a single phase bidirectional AC-DC converter interfacing with the grid is essential. The implementation of a bidirectional AC-DC converter can allow for battery recharge from the grid, battery energy injection to the AC grid, and battery energy for AC power stabilization. In this paper, the basic requirements and specifications for PHEV bidirectional AC-DC converter designs are presented. Generally, there are two types of topologies used for PHEVs: an independent topology and a combination topology that utilizes the drive motorpsilas inverter. Evaluations of the two converter topologies are analyzed in detail. The combination topology analysis is emphasized because it has more advantages in PHEVs, in respect to savings in cost, volume and weight.

Plug-in Hybrid Electric Vehicles (PHEVs) bear great promises for increasing fuel economy and decreasing greenhouse gas emissions by the use of advanced battery technologies and green energy resources. The design of a PHEV highly depends on several factors such as the selected powertrain configuration, control strategy, sizes of drivetrain components, expected range for propulsion purely by electric energy, known as AER, and the assumed driving conditions. Accordingly, design of PHEV powertrains for diverse customer segments requires thorough consideration of the market needs and the specific performance expectations of each segment. From the manufacturing perspective, these parameters provide the opportunity of mass customization because of the high degree of freedom, especially when the component sizes and control parameters are simultaneously assessed. Based on a nonconventional sensitivity and correlation analysis performed on a simulation model for power-split PHEVs in this study, the product family design (PFD) concept and its implications will be investigated, and limitations of PFD for such a complex product along with directions for efficient family design of PHEVs will be discussed.

Optimal design and allocation of electrified vehicles and dedicated charging infrastructure for minimum life cycle greenhouse gas emissions and cost

Hybrid whale optimization and pattern search algorithm for day-ahead operation of a microgrid in the presence of electric vehicles and renewable energies

This study employed the Life Cycle Assessment (LCA) method to evaluate the greenhouse gas (GHG) emissions of passenger vehicles across 31 provinces in China. It examined the potential of Battery Electric Vehicles (BEVs), Hybrid Electric Vehicles (HEVs), and Plug-in Hybrid Electric Vehicles (PHEVs) in significantly reducing emissions compared to Internal Combustion Engine Vehicles (ICEVs). The findings revealed that HEVs, PHEVs, and BEVs substantially lower emissions under the national average electricity GHG emissions coefficient, with PHEVs being the most effective, reducing emissions by over 30%. However, disparities exist at the provincial level due to varying electricity structures, with BEVs not fully effective in reducing emissions in about ten provinces, including Liaoning. The study further categorized provinces into six groups using the K-means method to guide the promotion of different types of New Energy Vehicles (NEVs). Sensitivity analysis highlights the critical role of reducing electricity emission coefficients and adjusting PHEV's utility factor in achieving emission reductions. The study also notes that battery replacements in PHEVs and BEVs during their lifecycle could impede emission reduction in most provinces. To maximize the environmental benefits of NEVs, there is a need to move to a greener electricity mix. At the same time, strategies for promoting NEVs should vary for different provinces.

High efficiency two stroke opposed piston engine for plug-in hybrid electric vehicle applications: Evaluation under homologation and real driving conditions

Among various Hybrid Electric Vehicles (HEVs), Plug-in Hybrid Electric Vehicles (PHEVs) charged from the grid are seen as the most advanced ones, as they can drive dozens of kilometers using only the electric engine and thus producing less tailpipe greenhouse gas emissions than vehicles with internal combustion engines or other HEVs. The proportion of PHEVs among all vehicles is still relatively low but increasing rapidly in many countries. However, the real-world emissions from these novel hybrid technologies are not straightforward to estimate. This study investigates multiple properties of the particle emissions of a PHEV, with gasoline direct injection, GDI, compared to two conventional gasoline vehicles, one with port fuel injection, PFI and one with GDI. Distance-based emission factors (EFs) for each vehicle in various driving modes, including battery-hold and battery-charge driving modes for the PHEV, were analyzed. The results showed that the PHEV produced smaller particles in size, resulting that particle mass (PM) and black carbon (BC) were lower by factor of ten in comparison to EFs from the vehicles with PFI and GDI engines. The PHEV consistently emitted lower distance-based EFs than the PFI and GDI vehicles in all driving modes, though EF for particle number (PN) in battery-charge mode was close to the EFs from the other two vehicles. The study also found that the vehicle cold start effect was present in the case of the shorter driving route but not as significant in the longer one. Overall, the study demonstrated that PHEVs could produce lower particle and BC emissions compared to traditional gasoline-powered vehicles. The vehicle cold start and systematic combustion engine restart effects still can have significant impacts on particle emissions, especially in shorter trips.

Power system operation risk analysis considering charging load self-management of plug-in hybrid electric vehicles