Browse Topic: Vehicle charging
Improving the energy efficiency of electrified vehicles remains a central objective in modern electric powertrains. Multi-level converters (MLCs) are widely recognised for lowering conversion losses relative to two-level inverters and improving total harmonic distortion (THD) in the sinusoidal supply to motors with a consequent reduction in motor losses. Despite this, sustained production-oriented validation at the integrated system level remains limited. This work introduces a multi-level converter architecture of the Battery Integrated Modular Multi-Level Converter (BIMMC) topology using Cascaded H-Bridge (CHB) architecture. It offers improvements in all key metrics of performance, cost, package size, mass and robustness compared to the current state-of-the-art two-level inverter system with distributed functions for charging available in the market today. The overall solution is highly functionally integrated. It supports four major functions required in electric vehicles without
Developing efficient fast-charging infrastructure along highway corridors is critical for reducing range anxiety and promoting long-distance electric travel. However, traditional static location approaches often fail to account for the stochastic interactions between continuous traffic flows and the stochastic variability of remaining driving ranges. To address these methodological gaps, this study develops a demand-driven optimization framework that integrates an improved Genetic Algorithm with the flow-capturing location-allocation model (GA-FCLM). Unlike static facility location approaches, the flow-capturing location-allocation component is specifically selected to maximize the interception of continuous traffic flows under strict range constraints, while the genetic algorithm efficiently navigates the high-dimensional discrete search space of simultaneous siting and sizing decisions. By synthesizing segment-level traffic flows with Monte Carlo simulations of state of charge (SOC
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