## 1 Introduction  
Electric and hybrid vehicles (HV) are becoming increasingly popular due to their environmental benefits. Energy management strategies (EMSs) include different methods or techniques that aim to manage multiple aspects of optimal energy consumption to improve the performance of electric vehicles (EVs) [1]. The integration of EMS with EVs as a part of the eco-driving protocol may enable the smart management of driving speed profiles and help in achieving long-term sustainability goals. The main significance of optimized energy management lies in the achievement of better performance of vehicles with the desired set of characteristics, as defined by the user. An optimal EMS is an important requirement to be implemented to produce a final product which is not only smart and automated but also energy efficient. In the past [2; 3; 4; 5; 6], different types of methods have been adopted for energy management in EVs or hybrid electric vehicles (HEVs). Depending on the mode of operation, the strategies can be classified as online or offline. In Figure 1, a broad classification of EMSs is listed based on online and offline approaches. Online approaches are generally real-time-based algorithms such as predictive and instantaneous optimization techniques, whereas offline approaches are non-real-time and can provide a globally optimal solution. Predictive (prediction-based) and cognitive (recognition-based) energy management can enhance the performance of electric or HVs immensely [7]. In [8], a dynamic programming-based energy management strategy (EMS) is presented along with a general regression neural network (NN) for improving the system efficiency of a dual-motor battery electric vehicle (BEV). In [9], authors have developed an energy management system for the minimization of the total distribution costs of BEV batteries, which is considered along with the maximization of average utilization of batteries. In [10], a plug-in hybrid vehicle has been optimized for better fuel efficiency by using an adaptive and hierarchical EMS and classification based on fuzzy logic control. In [11], an adaptive wavelet transform-fuzzy logic control EMS has been combined with cluster analysis-based classification to divide power among the hybrid energy sources in an optimal manner. In [12], the fuel cell lifetime of HEVs is  
Figure 1: Classification of EMSs for electric and HVs [2; 3; 4; 5; 6]. classification that works with a genetic-algorithm-based EMS. Similarly, [13] achieved an improvement in the fuel efficiency of HEVs by optimizing power management using fuzzy logic-based strategies. In [14], the authors present a fuzzy-based EMS for fuel cell vehicles (FCV) by employing a particle swarm optimization algorithm for the better recognition of traffic conditions. Further, in [15], a multi-mode EMS has been achieved for a fuel cell electric vehicle (FCEV). A Markov chain-based DPR classification has been employed for the improvement of fuel economy. In [16], authors minimized the fuel consumption of HEVs using the Euclid approach degree-based classification and simulated annealing-based EMS. In [17], the fuel consumption of HEVs has been minimized by using heuristic and optimal control strategies. Similarly, in [18], fuel consumption and emission improvements of an HEV have been analyzed using clustering and torque division by employing classification and optimization, respectively. Further, in [19], a DPR-based optimization technique has been presented with a learning vector quantization classification system to improve the fuel economy of HEVs. In the case of an EV, a prior estimate of the load can be made depending upon the driving pattern of the driver, and information thus obtained can be used to extend the life and range of the battery [20]. As concluded from the literature, regardless of the type of vehicle, combining DPR with energy management may lead to significant improvements in fuel economy, energy consumption, battery life, performance, and efficiency [21; 22].