Electric vehicles (EVs) have been garnering wide attention over conventional fossil fuel-based vehicles due to the serious concerns of environmental pollution and crude oil depletion. In this article, we have conducted a systematic literature survey to explore the battery raw material supply chain, material processing, and the economy behind the commodity price appreciation. We present significant areas of concern, including resource reserves, supply, demand, geographical distribution, battery reuse, and recycling industries. Furthermore, details of the battery supply chain and its associated steps are illustrated. The authors believe the presented study will be an information cornerstone in boosting manufacturing and understanding the key components and materials required to facilitate EV battery production. Further, this study discusses the major industries, and their policies and global market share in each material category.

This paper presents the real-time implementation of a feature fusion based learning using multi-domain discriminant correlation analysis (MDCA) for accurate diagnosis of nonlinear processes. The algorithm is also implemented in 8-bit PIC-microcontroller (PIC18F45k22) from the perspective of online applications. In the MDCA, a set of multiple features are evaluated in direct and wavelet domain for each process employing large-volume available signals. Features are subjected for correlation analysis to demonstrate the variation of features and to extract low order statistics which represent underlying phenomena. The locally evaluated statistics are fused using MDCA and obtained discriminant features are subjected to linear transformation. Finally, a set of efficient key statistics are derived for accurate characterization of various processes. The algorithm is validated statistically and integrated with decision models-k-nearest neighbor, discriminant analysis and neural network for real-time process diagnosis. The method achieves accuracy in the range of 98.75-100%. Results and comparison analysis show the effectiveness and reliability of the proposed model.

Light-Duty Electric Vehicle (LDEV) with a distributed drive powertrain provides several potential advantages in terms of flexibility, controllability and responsiveness over conventional powertrains. The precise distribution of driving and braking torque of such configuration is crucially vital for improving the overall performance and efficiency of the vehicles. This paper proposes a new torque allocation (TA) model emphasizing the wheel load variation due to the passenger occupancy payload. A light-duty vehicle model is developed along with the occupancy payload arrangement and a dynamic tire-road friction estimation method for the control system for wheel slip. This proposed TA algorithm uses offline optimization to derive the necessary transmissible torque to the driving wheels. Unlike conventional optimization, it adopts a set of predefined distribution coefficients. Therefore it can execute in a real time platform without high computation power and additional hardware requirements. The efficiency of the model is analyzed using Indian Urban Drive Cycle (IN-UDC). A comparative analysis using a traditional torque allocation model highlights the contributions of this novel torque allocation. The results obtained from various simulations demonstrate the effectiveness of the proposed new TA algorithm.