Rui Xiong

Multivariate and Probabilistic Analyses of Sensory Science Problems

Rui Xiong

Sensory scientists are often faced with making business decisions based on the results of complex sensory tests involving a multitude of variables. Multivariate and Probabilistic Analyses of Sensory Science Problems explains the multivariate and probabilistic methods available to sensory scientists involved in product development or maintenance. The techniques discussed address sensory problems such as panel performance, product profiling, and exploration of consumer data, including segmentation and identifying drivers of liking. Applied in approach and written for non-statisticians, the text is aimed at sensory scientists who deal mostly with descriptive analysis and consumer studies. Multivariate and Probabilistic Analyses of Sensory Science Problems offers simple, easy-to-understand explanations of difficult statistical concepts and provides an extensive list of case studies with step-by-step instructions for performing analyses and interpreting the results. Coverage includes a refresher on basic multivariate statistical concepts; use of common data sets throughout the text; summary tables presenting the pros and cons of specific methods and the conclusions that may be drawn from using various methods; and sample program codes to perform the analyses and sample outputs. As the latest member of the IFT Press series, Multivariate and Probabilistic Analyses of Sensory Science Problems will be welcomed by sensory scientists in the food industry and other industries using similar testing methodologies, as well as by faculty teaching advanced sensory courses, and professionals conducting and participating in workshops addressing multivariate analysis of sensory and consumer data.

Advanced Battery Management Technologies for Electric Vehicles

Rui Xiong

A comprehensive examination of advanced battery management technologies and practices in modern electric vehicles Policies surrounding energy sustainability and environmental impact have become of increasing interest to governments, industries, and the general public worldwide. Policies embracing strategies that reduce fossil fuel dependency and greenhouse gas emissions have driven the widespread adoption of electric vehicles (EVs), including hybrid electric vehicles (HEVs), pure electric vehicles (PEVs) and plug-in electric vehicles (PHEVs). Battery management systems (BMSs) are crucial components of such vehicles, protecting a battery system from operating outside its Safe Operating Area (SOA), monitoring its working conditions, calculating and reporting its states, and charging and balancing the battery system. Advanced Battery Management Technologies for Electric Vehicles is a compilation of contemporary model-based state estimation methods and battery charging and balancing techniques, providing readers with practical knowledge of both fundamental concepts and practical applications. This timely and highly-relevant text covers essential areas such as battery modeling and battery state of charge, energy, health and power estimation methods. Clear and accurate background information, relevant case studies, chapter summaries, and reference citations help readers to fully comprehend each topic in a practical context. Offers up-to-date coverage of modern battery management technology and practice Provides case studies of real-world engineering applications Guides readers from electric vehicle fundamentals to advanced battery management topics Includes chapter introductions and summaries, case studies, and color charts, graphs, and illustrations Suitable for advanced undergraduate and graduate coursework, Advanced Battery Management Technologies for Electric Vehicles is equally valuable as a reference for professional researchers and engineers.