Battery state of charge as an effective operational indicator is expected to play a crucial role in the advancement of electric vehicles, improving the battery capacity and energy utilization, avoiding battery overcharging and over-discharging, extending the battery’s useful lifespan, and extending the autonomy of electric vehicles. In context, this article presents a computationally efficient battery state-of-charge estimator based on the Coulomb counting technique with constant and variable discharging current profiles for an actual battery pack in real time. A dedicated experimental bench is developed for validation purposes, where pivotal measurements such as current, voltage, and temperature are initially measured during the charging/discharging cycle. The state of charge thus obtained via these measurements is then compared with the value estimated through the battery generic model. Detailed analysis with conclusive outcomes is finally presented to exhibit the flexible nature of the proposed method in terms of the precise state-of-charge estimation for a variety of batteries, ranging from lead–acid batteries for domestic applications to Li-ion batteries inside electric vehicles.

The systematic transition of conventional automobiles to their electrified counterparts is an imperative step toward successful decarbonization. Crucial advances in battery storage systems (BSS) and related technologies will enable this transition to proceed smoothly. This requires equivalent developments in several interconnected areas, such as complete battery cycles and battery management systems (BMS). In this context, this article critically examines state-of-the-art battery technologies from the perspective of automakers, provides insightful discussions, and poses open questions with possible answers. The generations of BSS (traditional, current, and futuristic) are first reviewed and analyzed via two distinct qualitative factors (DQFs): key design markers and performance indicators. Based on the introduced DQFs, major development trends and probable evolutions are forecasted. Thereafter, recent modeling and state estimation methods are comprehensively reviewed in relation to high-performance BMS. Accordingly, promising modeling methods are identified as futuristic solutions, leading to an accurate and timely decision for reliable and safer user experience. This article is concluded by presenting a techno-economic assessment of what to expect, as well as highlighting future challenges and opportunities for industry, academia, and policy makers.

Being a renewable influenced utility grid, the wind integrated hydrothermal system (WHTS) is considered as a future‐proof and contemporary energy system to reduce the dependency on depleting fossil fuels. However, due to intermittent nature of wind energy and complex operational constraints, offering the day‐ahead operation schedule of WHTS with joint awareness of fuel economy and emissions is classified as a complicated multi‐objective optimization problem. The major challenge is to maximize the utilization of clean hydro power to accommodate uncertain wind energy together with reduced dependency on fossils based thermal power. In this context, the paper devises an efficient control and multi‐criteria energy scheduling method, essentially comprised of adaptive repair and volume restriction to maximize the utilization of available hydro power. The pareto‐metaheuristic optimizer based on non‐dominated sorting is exploited for the day‐ahead scheduling of WHTS based utility grid. The most appropriate solution from pareto‐optimal archive is selected using fuzzy‐ranking index. The expected reductions in fuel cost and emissions are reflected using a significant probability scheme. The effectiveness of the proposed approach based on pareto‐metaheuristic is appraised by various case studies. The simulation results provide a suitable day‐ahead generation schedule of WHTS based grid, considering both economics and environmental aspects.