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Introduction to PV Systems
Published in Roger Messenger, Homayoon “Amir” Abtahi, Photovoltaic Systems Engineering, 2017
Roger Messenger, Homayoon “Amir” Abtahi
The nickel–zinc battery is a combination of the Ni–Cd system and the Cu–Zn system, which provides some attractive features, including long life and a capacity advantage. The specific energy of the Ni–Zn system is double the specific energy of the Ni–Cd system. The overall discharge chemical reaction is given by 2NiOOH+Zn+2H2O→2Ni(OH)2+Zn(OH)2. If the battery is allowed to overcharge, it dissociates the water in the KOH electrolyte, resulting in gassing and the release of hydrogen and oxygen. Hence, charging must be controlled to avoid overcharging. Fully charged cell voltage is approximately 1.73 V.
Modeling green vehicle adoption: An integrated approach for policy evaluation
Published in International Journal of Sustainable Transportation, 2018
To forecast the market penetration of GVs, in the early 1980s, Train (1980) calculated the market share of several nongasoline-powered automobiles for the years 2000 and 2025 if no large changes occurred in fuel prices, taxes, consumers’ attitudes, or regulations affecting the automobile market. He predicted that hybrid and aluminum-reaction vehicles would capture a significant share, while nickel-zinc battery vehicle would occupy very small market share. Numerous studies use consumer choice models to forecast vehicle purchase and holding decisions. These studies have incorporated discrete choice models of consumer preference to vehicle technology, class, make, and characteristics (Al-Alawi & Bradley, 2013). Different model structures have been used in the automotive consumer preference literature: logit models (Boyd & Mellman, 1980; Daziano, Sarrias, & Leard, 2017), nested logit model (Brownstone, Bunch, & Train, 2000), mixed logit model (Maness & Cirillo, 2012; Rasouli & Harry Timmermans, 2016), hybrid choice model (Valeri & Cherchi, 2016), and dynamic choice model with diffusion process (Jensen, Cherchi, Mabit, & Ortúzar, 2016; Liu & Cirillo, 2017). Traditional attributes used to characterize consumer preference over new vehicle technologies include the sensitivity to technology incremental cost, battery replacement, refueling or charging infrastructure availability, refueling or recharging time, maintenance cost, and driving range (Cao, 2006). More recent studies have investigated the impact of other attributes on GV adoption; these innovative attributes include information on vehicle emissions (Daziano et al., 2017), social networks (Rasouli & Harry Timmermans, 2016), and habitual behavior (Valeri & Cherchi, 2016). In spite of the benefits of GVs, several obstacles need to be overcome before they will be widely adopted (Egbue & Long, 2012). A major barrier is that consumers tend to resist new technologies that are considered alien. Other factors such as features of vehicles currently on the market; family and work responsibilities, residential choices, routines, and preferences all act as constraints to buy a GV (Flamm & Agrawal, 2012).