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In this paper we apply a model of technological diffusion, Future Technology Transformations in the Transport Sector (FTT: Transport), linked to the E3ME macroeconomic model, to study possible future technological transitions in personal passenger transport in four East Asian countries. We assess how targeted policies could impact on these transitions by defining four scenarios based on policies that aim to reduce emissions from transport. For each country we find that an integrated approach of tax incentives, subsidies, regulations (fuel economy efficiency), kick-start programs and biofuel programs yield the most significant emission reductions because, when combined, they accelerate effectively the diffusion of electric vehicles in the region.

Regarding the issue of individual purchasing behavior in the rapid growth of electric vehicles, this article studies the diffusion model of electric vehicles considering individual choices and social effects from the perspective of the scale and quantity changes of electric vehicles. First, the neural network was used to predict the charging data of electric vehicles, and the economic effects of purchasing electric vehicles were calculated by combining the purchase cost and government subsidies. Then, the utility function for owners to purchase electric or traditional fuel vehicles was created by considering economic effects, cognitive attitudes, and social effects as factors that individuals need to consider when purchasing electric or traditional fuel vehicles. Finally, the discrete choice model was used to calculate the probability of users choosing to purchase electric or traditional fuel vehicles, and the number of electric vehicles was statistically calculated. Analysis of simulation examples shows that the growth rate of fuel vehicles decreases year by year during the simulation period, and the trend of electric vehicle growth follows an S-curve.

Electric vehicles are expanding significantly in recent years. Policies have been critical in stimulating the growth of electric vehicle market. This paper focuses on subsidy policies for electric vehicle adoption in a horizontally differentiated goods market. Using a representative consumer model and assuming the duopoly firms compete in a Cournot fashion, we find that the optimal level of subsidies might not fall as a result of the decreasing production cost of electric vehicles. Instead, the subsidy might phase down when the government starts to bring more competition into the electric vehicle industry. This main result goes through irrespective of whether the subsidy is sales volume-based or sales revenue-based. Our numerical findings further suggest that welfare maximizing subsidy declines with an increasing competition among car manufacturers, and sales volume-based subsidy policy is more efficient than sales revenue-based one. In addition, we also find that the subsidy cut would reduce electric vehicle sales, and subsidy policy is responsive to the government’s objective function.

The market share of electric vehicles is growing and the interest in these vehicles is rapidly increasing in industrialized countries. In the light of these circumstances, this study provides an integrated policy-making package, which includes key features for electric vehicle diffusion and its impact on the Korean power market. This research is based on a quantitative analysis with the following steps: (1) it analyzes drivers’ preferences for electric or traditional internal combustion engine (ICE) vehicles with respect to key automobile attributes and these key attributes indicate what policy makers should focus on; (2) it forecasts the achievable level of market share of electric vehicles in relation to improvements in their key attributes; and (3) it evaluates the impact of electric vehicle diffusion on the Korean power market based on an achievable level of market share with different charging demand profiles. Our results reveal the market share of electric vehicles can increase to around 40% of the total market share if the key features of electric vehicles reach a similar level to those of traditional vehicles. In this estimation, an increase in the power market’s system generation costs will reach around 10% of the cost in the baseline scenario, which differs slightly depending on charging demand profiles.

Batteries directly contribute to the advancement of technologies ranging from portable electronics to fuel-efficient vehicles. In high power applications such as hybrid electric vehicles (HEVs), monitoring algorithms use current and voltage measurements to estimate battery state of charge (SOC) and available power. Despite increased cost, these systems commonly employ conservative, oversized batteries due to poor prediction of current/voltage dynamics and imprecise real-time estimation. This dissertation introduces a general, electrochemical model-based approach for safe and efficient integration of Li-ion batteries into transient, pulse power-type systems. A transient solid-state diffusion model is incorporated into a previously developed 1D electrochemical model. The nonlinear model, solving 4 coupled partial differential equations by a computational fluid dynamics (CFD) technique, is validated against low rate constant current, pulse power, and transient driving cycle data sets from a 6 Ah Li-ion HEV battery. Solid-state Li transport (diffusion) significantly limits high rate performance, and end of discharge at the 2.7 V minimum limit is caused by depleted/saturated active material surface concentrations in the negative/positive electrodes for pulses lasting longer than around 10 seconds. The 3.9 V maximum limit, meant to protect the negative electrode from side reactions such as lithium plating, is overly conservative for pulse charging. Increased power capability may be realized by using a real-time electrochemical model to estimate internal states and control the battery within appropriate limits. Development of a fast, stable, and accurate model is difficult however, given the infinite-dimensional, distributed nonlinear processes governing battery dynamics. Here, an impedance model is derived from the electrochemical kinetic, species and charge transport equations and, using a model order reduction technique developed herein, the high order transfer functions/matrices are numerically reduced to an observable/controllable state variable model in modal form. Open circuit potential and electrode surface concentration nonlinearities are explicitly approximated in the model output equation on a local and electrode-averaged basis, respectively. Validated against the 313th order CFD model, a 12th order state variable model with 0-10 Hz bandwidth predicts terminal voltage to within 25 mV (<1%) for pulse and constant current profiles at rates up to 50C. The modeling methodology is valid for all types of porous electrode Li-ion batteries not operating under severe electrolyte transport limitations. A linear Kalman filter is designed for real-time estimation of internal potentials, concentration gradients, and SOC. A reference current governor predicts operating margin with respect to electrode side reactions and surface depletion/saturation conditions responsible for damage and sudden loss of power. Estimates are compared with the nonlinear CFD model. The linear filter gives to within ∼2% performance in the 30-70% SOC range except in the case of severe current pulses that drive electrode surface concentrations near saturation and depletion, although the estimates recover as concentration gradients relax. With 4 to 7 states, the filter has low order comparable to equivalent circuit methods presently employed for battery management but, unlike those empirical methods, enables pulse charging/discharging beyond conservative voltage limits. For the 6 Ah HEV battery, the method increases power density by 22% and streamlines the systems integration process for families of battery/vehicle designs.

We analyze the effect of four determinants of electric vehicle diffusion in China for a panel of 31 regions for the period 2010–2016. We analyze diffusion of four different electric vehicle types, namely battery electric cars and buses as well as plug-in hybrid electric cars and buses. System GMM panel estimation results show that total monetary subsidies have a positive effect only on the diffusion of battery electric cars. A closer look reveals that subsidies provided by regional governments are decisive for all types of vehicles but the subsidy provided by the central government and its degression over time dilute the overall effect of subsidies and is partly detrimental. Non-monetary ownership policies, such as license-plate lotteries, show a positive effect only for battery electric cars. Availability of public charging infrastructure increases diffusion of all vehicle types. Charging points are relevant for cars, while charging stations are especially decisive for the diffusion of electric buses. Using local environmental conditions as a novel determinant for the diffusion of electric vehicles reveals that the local air pollution influences the diffusion of buses, but not of cars.

China is a major energy-consuming country and is under great pressure to improve its energy efficiency as well as reduce its carbon emissions. Hybrid electric vehicles (HEVs), as an energy-efficient transport innovation, have the potential to reduce gasoline consumption, carbon emissions and alleviate environmental problems. Diffusion of HEVs’ adoption is a significant initiative. A sample of 433 respondents has been collected in China to predict the customers’ intention to adopt HEVs, using an extended model of the theory of planned behavior (TPB). The empirical results show that the attitude toward HEVs, subjective norm, perceived behavioral control (the three primary elements of the TPB model) and personal moral norm partially mediate the effect of consumers’ environmental concern on their intention to adopt HEVs. Consumers’ environmental concern affects the adoption intention indirectly and is significantly positively related to the attitude toward HEVs, subjective norm, perceived behavioral control and personal moral norm, which in turn influence the adoption intention positively. The results confirm the appropriateness of the TPB model and verify that the extended TPB model has good explanatory power in predicting consumers’ intention to adopt HEVs. Based on the empirical results, we discuss the implications for promoting the adoption of HEVs and provide suggestions for future study.

Owing to the increasing demand of energy and shifting to the renewable energy resources, lithium ion batteries (LIBs) have been considered as the most promising alternative and green technology for energy storage applied in hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and other electric utilities. Owing to its environmental benignity, availability, and stable structure, titanium dioxide (TiO 2 ) is one of the most attractive anode materials of LIBs with high capability, long cycling life, high safety, and low cost. However, the poor electrical conductivity and low diffusion coefficient of Li-ions in TiO 2 hamper the advancement of TiO 2 as anode materials of LIBs. Therefore, intensive research study has been focused on designing the nanostructures of TiO 2 and its composites to reduce the diffusion length of Li-ion insertion/extraction and improve the electrical conductivity of the electrode materials. In this article, the development of TiO 2 and its composites in nano-scales including fabrication, characterization of TiO 2 nanomaterials, TiO 2 /carbon composite, and TiO 2 /metal oxide composites to improve their properties (capacity, cycling performance, and energy density) for LIBs are reviewed. Meanwhile, the mechanisms for influences of the structure, surface morphology, and additives to TiO 2 composites on the related properties of TiO 2 and TiO 2 composites to LIBs are discussed. The new directions of research on this field are proposed.

Forecasting the potential demand for electric vehicles is a challenging task. Because most studies for new technologies rely on stated preference (SP) data, market share predictions will reflect shares in the SP data and not in the real market. Moreover, typical disaggregate demand models are suitable to forecast demand in relatively stable markets, but show limitations in the case of innovations. When predicting the market for new products it is crucial to account for the role played by innovation and how it penetrates the new market over time through a diffusion process. However, typical diffusion models in marketing research use fairly simple demand models. In this paper we discuss the problem of predicting market shares for new products and suggest a method that combines advanced choice models with a diffusion model to take into account that new products often need time to gain a significant market share. We have the advantage of a relatively unique databank where respondents were submitted to the same stated choice experiment before and after experiencing an electric vehicle. Results show that typical choice models forecast a demand that is too restrictive in the long period. Accounting for the diffusion effect, instead allows predicting the usual slow penetration of a new product in the initial years after product launch and a faster market share increase after diffusion takes place.

The rapid evolution of portable electronics and electric vehicles necessitates batteries with high energy density, robust cycling stability, and fast charging capabilities. High-voltage cathodes, like LiNi 0.8 Co 0.1 Mn 0.1 O 2 (NCM-811), promise enhanced energy density but are hampered by poor stability and sluggish lithium-ion diffusion in conventional electrolytes. We introduce a metal-organic framework (MOF) liquid-infusion technique to fully integrate MOF liquid into the grain boundaries of NCM-811, creating a thoroughly coated cathode with a thin, rigid MOF Glass layer. The surface electrically non-conductive MOF Glass layer with 2.9 Å pore windows facilitating Li-ion pre-desolvation and enabling highly aggregative electrolyte formation inside the Glass channels, suppressing solvated Li-ion co-insertion and solvent decomposition. While the inner Glass layer composes of Li-ion conducting components and enhancing fast Li-ion diffusion. This functional structure effectively shields the cathode from particle cracking, CEI rupture, oxygen loss, and transition metal migration. As a result, Li | |Glass@NCM-811 cells demonstrate good rate capability and cycling stability even under high-charge rates and elevated voltages. Furthermore, we also achieve a 385 Wh kg -1 pouch-cell (19.579 g, for pouch-cell), showcasing the practical potential of this method. This straightforward and versatile strategy can be applied to other high-voltage cathodes like Li-rich manganese oxides and LiCoO 2 . Li-ion batteries based on high-voltage Ni-rich layered oxides are hampered by stability and ion diffusion issues. Here, authors develop a metal-organic-framework liquid-infusion technique to create a rigid glass layer on the oxide particles, improving both Li + diffusion and battery stability.