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.

Electric mobility is getting prominence in modern transportation as government policies aim to reduce greenhouse gas (GHG) emissions. In the context of real-time testing, numerical modelling and simulation of electric vehicle (EV) powertrains play a vital role in developing an efficient electric powertrain and charging infrastructure as it consumes less time and cost. Also, it enhances the overall performance by optimizing the size and configuration of the EV powertrain under different driving conditions. Thus, the review paper explores the different modelling approaches used for estimating the energy consumption (EC) and driving range (DR) initially. Further, the vehicle analytical model is discussed in detail with sub-models of powertrain components and vehicle dynamics, which have the mathematical correlation related to power losses and energy flow. Additionally, the necessity, development process, characterization and accuracy of localized driving cycles (DCs) and commonly used driver controller models for EVs are critically elaborated. Further, the impact of various influential input parameters such as vehicle parameters and driving conditions on EV performance characteristics is analyzed along with different improvisation methods utilized in the existing literature. From this extensive review, it can be concluded that simulation results by using an analytical vehicle model have good accuracy with chassis dynamometer-based testing and it can be used for optimizing the size and configuration of EV powertrain components under different scenarios. Finally, the present status and future research required in the field of EV powertrain development through modelling and simulation are summarized to extend the application of EVs in transportation sectors.

This paper proposes a less-disturbed ecological driving strategy for connected and automated vehicles (CAVs). The proposed strategy integrates the offline planning and the online tracking. In offline planning, an energy efficient reference speed is created based on traffic information (such as the average traffic speed) and characteristics of the vehicle (such as the engine efficiency map) via dynamic programming. The consideration of average traffic speed in speed planning avoids selfish optimisations. In online tracking, model predictive control is employed to update the vehicle speed in real-time to track the reference speed. A key challenge in applying ecological driving strategies is that the vehicle has to consider other traffic participants when tracking the reference speed. Therefore, this paper combines both longitudinal and lateral control to achieve better speed tracking by overtaking the preceding vehicle when necessary. The proposed less-disturbed ecological driving strategy has been evaluated in simulations in both single road segment scenario and real traffic environment. Comparisons of the proposed method with benchmark strategies and human drivers are made. The results demonstrate that the proposed strategy is more effective in energy saving. Compared to human drivers, the less-disturbed eco-driving strategy improves the fuel efficiency of CAVs by 4.53%.

The superior performance of an electric vehicles (EVs) is dependent on the related energy management controller (EMC). The current study devoted to develop various EMCs such PID, intelligent, hybrid and supervisory strategy to enhance the performance of EVs under real-time driving conditions. Also, the work integrates the various novel methodologies to develop EV model, efficiency maps and real-time driving cycle (DC). In this instance, a mathematical model of an EV with BLDC motor is developed using MATLAB/Simulink. Further, the efficiency maps for the motor and controller with different EMC’s are generated using the innovative experimental approach. Then, the developed efficiency maps are incorporated into model-in-loop (MIL)-based EV test platform to analyze the performance of various EMCs. Additionally, to validate the EV model, a real time DC has been developed for different types of road conditions, including urban, rural, and highway. Subsequently, the developed DC is integrated with MIL-based EV test platform for analysis of energy consumption and battery discharge behavior under real-time conditions. From the results, the proposed supervisory controller (68.4%) exhibits minimal SOC drop than the PID (21.5%), intelligent (44.9%) and hybrid (59.1%) controllers. As well, the energy consumption (EC) of the various EMCs is 85.63, 60.14, 44.67 and 33.4 Wh/km. In the case of regenerative efficiency of the developed EMCs under real-time driving conditions are -27.73, -41.64, -58.28 and -77.6 Wh respectively. The overall outcome of this work demonstrates that the proposed supervisory controller achieves a considerable improvement in battery consumption as well as a reduction in EC as compared to PID, intelligent, and hybrid controllers.

The electric turbocharger is a promising solution for engine downsizing. It provides great potential for vehicle fuel efficiency improvement. The electric turbocharger makes engines run as hybrid systems so critical challenges are raised in energy management and control. This paper proposes a real-time energy management strategy based on updating and tracking of the optimal exhaust pressure setpoint. Starting from the engine characterisation, the impacts of the electric turbocharger on engine response and exhaust emissions are analysed. A multi-variable explicit model predictive controller is designed to regulate the key variables in the engine air system, while the optimal setpoints of those variables are generated by a high level controller. The two-level controller works in a highly efficient way to fulfill the optimal energy management. This strategy has been validated in physical simulations and experimental testing. Excellent tracking performance and sustainable energy management demonstrate the effectiveness of the proposed method.