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Plug-in hybrid electric vehicles (PHEVs) are currently recognized as a promising solution for reducing fuel consumption and emissions due to the ability of storing energy through direct connection to the electric grid. Such benefits can be achieved only with a supervisory energy management strategy that optimizes the energy utilization of the vehicle. This control problem is particularly challenging for PHEVs due to the possibility of depleting the battery during usage and the vehicle-to-grid interaction during recharge. This paper proposes a model-based control approach for PHEV energy management that is based on minimizing the overall CO2 emissions produced-directly and indirectly-from vehicle utilization. A supervisory energy manager is formulated as a global optimal control problem and then cast into a local problem by applying the Pontryagin's minimum principle. The proposed controller is implemented in an energy-based simulator of a prototype PHEV and validated on experimental data. A simulation study is conducted to calibrate the control parameters and to investigate the influence of vehicle usage conditions, environmental factors, and geographic scenarios on the PHEV performance using a large database of regulatory and “real-world” driving profiles

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A real-time quantification of Li transport using a nondestructive neutron method to measure the Li distribution upon charge and discharge in a Li-ion cell is reported. By using in situ neutron depth profiling (NDP), we probed the onset of lithiation in a high-capacity Sn anode and visualized the enrichment of Li atoms on the surface followed by their propagation into the bulk. The delithiation process shows the removal of Li near the surface, which leads to a decreased coulombic efficiency, likely because of trapped Li within the intermetallic material. The developed in situ NDP provides exceptional sensitivity in the temporal and spatial measurement of Li transport within the battery material. This diagnostic tool opens up possibilities to understand rates of Li transport and their distribution to guide materials development for efficient storage mechanisms. Our observations provide important mechanistic insights for the design of advanced battery materials.

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The starter/alternator technology is considered an easily realizable hybrid electric vehicle (HEV) configuration to achieve significant fuel economy without compromising consumer acceptability. Several examples can be found in production or near-production vehicles, with implementation based on a spark ignition (SI) engine coupled with either a belted starter/alternator (BSA) or an integrated starter/alternator (ISA). One of the many challenges in successfully developing a starter/alternator HEV is to achieve engine start and stop operations with minimum passenger discomfort. This requires control of the electric motor to start and stop the engine quickly and smoothly, without compromising the vehicle noise, vibration, and harshness signature. The issue becomes more critical in the case of diesel hybrids, as the peak compression torque is much larger than in SI engines. This paper documents the results of a research activity focused on the control of the start and stop dynamics of a HEV with a belted starter/alternator. The work was conducted on a production 1.9 l common-rail diesel engine coupled to a 10.6 kW permanent magnet motor. The system is part of a series/parallel HEV powertrain, designed to fit a midsize prototype sport utility vehicle. A preliminary experimental investigation was done to assess the feasibility of the concept and to partially characterize the system. This facilitated the design of a control-oriented nonlinear model of the system dynamics and its validation on the complete HEV hardware. Model-based control techniques were then applied to design a controller for the belted starter/alternator, ensuring quick and smooth engine start operations. The final control design has been implemented on the vehicle. The research outcomes demonstrated that the BSA is effective in starting the diesel engine quickly and with very limited vibration and noise.

In response to the current and future energy and environment challenges, the automotive industry is strongly focusing on improving the fuel efficiency of vehicles. Although the electrification of automotive powertrains is clearly the principal path towards sustainable transportation, many opportunities still exist to improve the fuel economy of conventional vehicles. However, some of the technical solutions representing the state of the art in research and advanced development are difficult to benchmark in terms of their potential benefits for fuel consumption improvement. A greater understanding of the fuel energy utilization on the vehicle (here intended as a ‘system’ ) is therefore necessary in order to identify the readily available opportunities for efficiency improvements and, ultimately, to develop automobiles which are more fuel efficient. To this extent, this paper presents a review of the state of the art and technology trends in the field of energy management and recovery for automotive systems, with the primary focus on conventional powertrains. An understanding of the fuel energy utilization and dissipation associated with the vehicle subsystems (the engine, transmission, and chassis) is provided, as well as an overview of the opportunities and potential challenges in improving the fuel economy through system-level energy management, recovery, and harvesting. Finally, an overview of the most important solutions for managing energy dissipation, energy recovery, and harvesting is presented, discussing their potential for fuel economy improvement, technical readiness, and challenges. Wherever possible, projections on fuel economy improvements, based on either experimental data or simulations, are reported to provide opportunity for the assessment and comparison of current and future technologies.

Evaluation of Li-ion cell performance and life requires the ability to predict behavior at extreme conditions, such as low temperatures and high C-rates. Most electrochemical models assuming constant electrolyte diffusion properties fail to accurately predict the electrode and electrolyte potential at such conditions. This study presents a physics-based Extended Single Particle Model (ESPM) designed specifically for accurately predicting the behavior of a Li-ion cell at extreme conditions, incorporating concentrationdependent properties in the electrolyte diffusion dynamics. Since the proposed model aims at supporting long-term simulation, virtual design and optimization studies, minimization of the computational complexity is achieved through analytical Model Order Reduction (MOR) based on a Galerkin projection method. Results show that the implementation of the concentration-dependent diffusion properties leads to significant improvement of model accuracy at extreme conditions. The Reduced Order Model (ROM) can be simulated significantly faster than numerical methods with no loss of accuracy, supporting simulation of long-term usage cycles (10-year) and remaining useful life calculations. Lithium ion batteries are considered the state of the art for energy storage in electric and hybrid vehicles. When batteries operates at low temperature conditions, for instance during a cold start of an electric vehicle, the highly reduced ionic conductivity and diffusivity lead to the formation of large gradients in the electrolyte concentration within the cell domain.1,2 Large concentration gradients can also be established at high C-rate conditions, which typically occur when the battery is subject to fast charge/discharge current loads in cold weather. Therefore, it is important to accurately predict the cell electrochemical behavior and terminal voltage at such conditions, as performing fast charging procedures at low temperatures could severely shorten the cycle life due to lithium deposition on the anode. 1,3,4 Electrochemical battery models based on first principles have shown the ability to accurately predict the concentration dynamics and terminal voltage. [5][6][7][8][9][10][11] In particular, the pseudo two-dimensional model (or the P2D model) based on Porous Electrode Theory 5 and Single Particle Model 12 (SPM) have been widely used for modeling the dynamic response of Lithium ion cells to variable input current profiles. Recently, Extended Single Particle Models (ESPM) [13][14][15][16][17] incorporating the electrolyte dynamics have been developed to capture the cell behavior under high C-rates conditions, albeit with simpler mathematical structure than P2D models.While electrochemical models generally predict well the cell terminal voltage as function of current and temperature, they are characterized by the presence of coupled PDEs and nonlinear algebraic equations, increasing the mathematical complexity and leading to computational challenges when applied to fast simulation and to estimation or control desig...