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As the battery provides the entire propulsion power in electric vehicles (EVs), the utmost importance should be ascribed to the battery management system (BMS) which controls all the activities associated with the battery. This review article seeks to provide readers with an overview of prominent BMS subsystems and their influence on vehicle performance, along with their architectures. Moreover, it collates many recent research activities and critically reviews various control strategies and execution topologies implied in different aspects of BMSs, including battery modeling, states estimation, cell-balancing, and thermal management. The internal architecture of a BMS, along with the architectures of the control modules, is examined to demonstrate the working of an entire BMS control module. Moreover, a critical review of different battery models, control approaches for state estimation, cell-balancing, and thermal management is presented in terms of their salient features and merits and demerits allowing readers to analyze and understand them. The review also throws light on modern technologies implied in BMS, such as IoT (Internet of Things) and cloud-based BMS, to address issues of battery safety. Towards the end of the review, some challenges associated with the design and development of efficient BMSs for E-mobility applications are discussed and the article concludes with recommendations to tackle these challenges.

This research offers a maximum power point tracking (MPPT) algorithm for photovoltaic (PV) systems based on neural networks (NNs) and a rapid step-up converter configuration. An improved variable step size-radial basis function network (RBFN) in the NN algorithm is accomplished in the proposed system to track the maximum power point (MPP) with high convergence speed and obtain maximum power with reduced oscillations. Under various irradiance and temperature conditions, the performance of the recommended algorithm was compared to that of particle swarm optimization (PSO), modified perturb and observe (P&O) MPPT technique, artificial neural network (ANN), and multilayer perceptron feed-forward (MPF) NN-based MPPT method. In this system, a new interleaved non-isolated large step-up converter with the coupled inductor technique is suggested to compensate for the discord in PV devices to enable a continuous and independent power flow. The proposed PV-fed converter system is validated under partial shading conditions (PSCs) and uniform solar PV, and the results are experimentally verified with the use of a programmable direct current (DC) source. The obtained results indicate that the proposed converter produces output with high gain, continuous input current, low voltage stress on switches, minimal ripple, high power density, and extensive input and output operations. Finally, a prototype has been implemented to verify the functionality of the presented converter in continuous conduction mode operation with an input voltage range of 20 V and an output voltage of 200 V.

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