Plug-in Hybrid Electric Vehicles Design Research Articles

Improvement of PHEV Equivalent Fuel Economy and Battery Life by Applying Traffic-Based SOC Management

Improvement of plug-in hybrid electric vehicle (PHEV) equivalent fuel consumption (EFC) and battery life are significant issues in the design of PHEVs. This research aims to develop an adaptive traffic-based controller to improve fuel economy and battery life. The optimal state of charge (SOC) trajectories in real-world and standard cycles are extracted using the modified dynamic programming (MDP) approach. The optimal data is clustered based on the driving features, under the light of fuzzy clustering. Then, a control map is formed based on the clustered data to estimate SOC trajectories as a reference in an adaptive control strategy. A battery life model is employed to investigate the effect of the proposed method on battery aging. This study reveals that traffic-based SOC management simultaneously reduces the operating and maintenance costs since it improves the equivalent fuel economy and battery life in various driving cycles. Moreover, the suggested energy management strategy is implemented on an embedded system by applying the model-based design (MBD) method. The comparison of hardware-in-the-loop (HIL) and model-in-the-loop (MIL) simulation shows that the suggested strategy is implementable and effective for real applications.

New clutch oil-pressure establishing method design of PHEVs during mode transition process for transient torsional vibration suppression of planetary power-split system

For plug-in hybrid electric vehicles (PHEVs), mode transition might be the most effective way of energy conservation. However, the transient torsional vibration (TTV) during mode transition process, which is unavoidable, usually affect the drive comfort. This paper presents an innovative planetary hybrid power-split system (PHPS), which comprised of an epicyclic gear system, two compact clutches and a wedge brake device, and will been applied Chery PHEVs. First, powertrain vibration during mode transition process, as influenced by dynamic transfer friction torque of clutch, angle-varying mesh stiffness of epicyclic gear and external loads of multi-power sources, are investigated through a nonlinear TTV model. Then, the dynamic equations of mode transition process are established and numerically solved to get TTV response as well as vehicle jerk, and further new clutch oil-pressure establishing method design of PHEVs during mode transition process for relative torsional oscillation suppression of PHPS is presented. At last, bench tests are carried out, and results indicated that the proposed optimization model effectively suppressed PHPS torsional oscillation, vehicle longitudinal jerk, and improved driving smoothness during mode transition process.

Power management optimization in plug-in hybrid electric vehicles subject to uncertain driving cycles

Abstract Optimization of power management in plug-in hybrid electric vehicles (PHEVs) with dual-power-source plays a critical role in achieving higher fuel economy and less pollutant emissions. In this study, power management and optimal control strategies in PHEVs have been investigated subject to uncertain driving cycles of individual drivers for particular trips. First, a stochastic driving cycle is constructed to more accurately model the dynamic characteristics of the uncertain driving cycles, derived from the historic record of individual drivers. Finite-horizon stochastic dynamic programming is adapted to globally optimize the vehicle performance in stochastic sense. Simulation results show that the proposed strategy significantly improves fuel economy, indicating the present optimization approach is very effective in exploring the potential of the hybridization of power train. A higher discretization of (that is, with smaller step sizes in) vehicle dynamics state variables (vehicle velocity, power demand and battery state of charge) has a positive impact on the fuel economy while the limitation of driving operability actually degrades the fuel economy. The commuting time with doubly truncated normal distribution slightly enhances the fuel economy in comparison with uniform distribution. In addition, there exists a tradeoff between the fuel economy and the pollutant emissions. These results could be utilized as a guideline for the design of PHEVs with different objectives.

Metamodelling-based Product Family Design of Plug-in Hybrid Electric Vehicles

Development of plug-in hybrid electric vehicles (PHEVs) is stimulated by the need to replace non-renewable energies with sustainable and more environment-friendly new energy types. PHEVs benefit from a combination of an internal combustion engine and an electric motor with a rechargeable and larger battery pack than conventional hybrid vehicles. In order to maximise customer satisfaction and motivate replacement of the conventional vehicles with this new technology, the design of PHEVs requires sufficient differentiation in the product specifications for diverse market segments. The added set-up time, processes, costs and longer lead time for designing and manufacturing a diverse range of such vehicles is a hurdle towards increasing their diversity. This study proposes an efficient product family design (PFD) method for mass customisation of the PHEV powertrains. The methodology is less dependent on expensive simulations due to use of metamodelling and non-conventional sensitivity analysis. The PFD concept and its implications to a family of five PHEVs are investigated, and benefits as well as limitations of a sustainable development for this complex product are discussed.

Application of Parallel Chaos Optimization Algorithm for Plug-in Hybrid Electric Vehicle Design

Plug-in hybrid electric vehicles (PHEVs) have been offered as alternatives that could greatly reduce fuel consumption relative to conventional vehicles. A successful PHEV design requires not only optimal component sizes but also proper control strategy. In this paper, a global optimization method, called parallel chaos optimization algorithm (PCOA), is used to optimize simultaneously the PHEV component sizes and control strategy. In order to minimize the cost, energy consumption (EC), and emissions, a multiobjective nonlinear optimization problem is formulated and recast as a single objective optimization problem by weighted aggregation. The driving performance requirements of the PHEV are considered as the constraints. In addition, to evaluate the objective function, the optimization process is performed over three typical driving cycles including Urban Dynamometer Driving Schedule (UDDS), Highway Fuel Economy Test (HWFET), and New European Driving Cycle (NEDC). The simulation results show the effectiveness of the proposed approach for reducing the fuel cost, EC and emissions while ensuring that the vehicle performance has not been sacrificed.

Evaluation of energy requirements for all-electric range of plug-in hybrid electric two-wheeler

Recently plug-in hybrid electric vehicles (PHEVs) are emerging as one of the promising alternative to improve the sustainability of transportation energy and air quality especially in urban areas. The all-electric range in PHEV design plays a significant role in sizing of battery pack and cost. This paper presents the evaluation of battery energy and power requirements for a plug-in hybrid electric two-wheeler for different all-electric ranges. An analytical vehicle model and MATLAB simulation analysis has been discussed. The MATLAB simulation results estimate the impact of driving cycle and all-electric range on energy capacity, additional mass and initial cost of lead-acid, nickel-metal hydride and lithium-ion batteries. This paper also focuses on influence of cycle life on annual cost of battery pack and recommended suitable battery pack for implementing in plug-in hybrid electric two-wheelers.

Optimal Plug-In Hybrid Electric Vehicle Design and Allocation for Minimum Life Cycle Cost, Petroleum Consumption, and Greenhouse Gas Emissions

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.

Electrification potential of the car – an estimate from a mid-size Swedish town

An electrification of vehicles can contribute to increased energy efficiency and decreased air pollution in urban environments. The high vehicle costs involved, especially for the batteries, means that careful considerations of the options are needed. We have investigated the optimal design and potential for plug-in hybrid electric vehicles (PHEV) under various viability conditions with the help of a data set for individual vehicle movements from a mid-size Swedish town. In the estimates each car is equipped with a battery cost-optimized in size with respect to the individual car movement pattern and charging options expressed as the minimum break time interval required for considering recharging. The resulting optimal battery sizes are relatively small for lower economic viability, but increase with raised charging options. For high economic competitiveness the optimal sizes are larger, but decrease with better recharging possibilities. The results point to a PHEV design strategy with a small battery in an introductory phase and then an increased size when the economic competitiveness is further enhanced. Still the resulting optimal battery size is highly dependent on the specific movement pattern of the individual car. It is now urgent for the continued development, planning, and estimates of proliferation and impact of electrified vehicles that further statistical data, today mostly lacking, for the movement patterns of individual vehicles in various regions are assembled and utilized.

Impact of battery weight and charging patterns on the economic and environmental benefits of plug-in hybrid vehicles

Plug-in hybrid electric vehicle (PHEV) technology is receiving attention as an approach to reducing US dependency on foreign oil and greenhouse gas (GHG) emissions from the transportation sector. PHEVs require large batteries for energy storage, which affect vehicle cost, weight, and performance. We construct PHEV simulation models to account for the effects of additional batteries on fuel consumption, cost, and GHG emissions over a range of charging frequencies (distance traveled between charges). We find that when charged frequently, every 20 miles or less, using average US electricity, small-capacity PHEVs are less expensive and release fewer GHGs than hybrid electric vehicles (HEVs) or conventional vehicles. For moderate charging intervals of 20–100 miles, PHEVs release fewer GHGs, but HEVs have lower lifetime costs. High fuel prices, low-cost batteries, or high carbon taxes combined with low-carbon electricity generation would make small-capacity PHEVs cost competitive for a wide range of drivers. In contrast, increased battery specific energy or carbon taxes without decarbonization of the electricity grid would have limited impact. Large-capacity PHEVs sized for 40 or more miles of electric-only travel do not offer the lowest lifetime cost in any scenario, although they could minimize GHG emissions for some drivers and provide potential to shift air pollutant emissions away from population centers. The tradeoffs identified in this analysis can provide a space for vehicle manufacturers, policymakers, and the public to identify optimal decisions for PHEV design, policy and use. Given the alignment of economic, environmental, and national security objectives, policies aimed at putting PHEVs on the road will likely be most effective if they focus on adoption of small-capacity PHEVs by urban drivers who can charge frequently.

Early U.S. Market for Plug-In Hybrid Electric Vehicles

Plug-in hybrid electric vehicles (PHEVs) are proposed as both a near-term technology to achieve energy and environmental goals and as a transitional step toward viable all-electric vehicles. To replace assumptions with observations of potential PHEV drivers’ behavior in market and impact analyses, an Internet-based survey of 2,373 new-car–buying households in the United States was conducted. The instrument required households to answer questions, complete a driving and parking diary, and then complete several PHEV design exercises. Three conclusions could be drawn from the resulting data. First, at least half of the target population is already equipped for at-home vehicle recharging but has little opportunity for recharging at the workplace or other locations. Second, the study found that the respondents had widely varied interests in four possible PHEV attributes: fuel economy (in both charge-depleting and charge-sustaining operations), blended versus all-electric operation, the distance over which the vehicle is in the charge-depleting mode, and the recharging speed. Nevertheless, the appeal of increased fuel economy appears to be the highest and that of faster recharging appears to be the lowest. Furthermore, there is little interest in all-electric operation. Third, given the previous two points, it was estimated that about a third of the target population has both the infrastructure to recharge a PHEV and interest in a vehicle with plug-in capabilities. Policy, technology, and energy providers may use this information to understand whether their plans, designs, and goals align with these present understandings or whether it would be collectively beneficial to foster new understandings of PHEVs among U.S. car buyers.