## 1 Introduction
To efficiently distribute the energy between oil and electricity, rule-based (RB) strategies select operating modes based on predefined rules, which are subdivided into deterministic rule-based strategies and fuzzy logic-based strategies [10,12]. Although rule-based strategies are the most basic strategies, and are widely applied due to their simplicity and practicality, they usually lead to local optimization such that they cannot obtain the globally optimal solution. On the other hand, it does not take the actual changes in driving conditions into account and its robustness cannot be guaranteed. To achieve a better performance in terms of fuel economy in HEVs, numerous efforts been made in the last decade in the area of optimization-based energy management control strategies, mainly with respect to instantaneous optimization and global information driven optimization [13, 14, 15]. Instantaneous optimization strategies, such as Equivalent consumption minimization strategies (ECMS), and global information-driven optimization, such as Pontryagin's Minimum Principle (PMP) [16, 17, 18, 19] and dynamic programming (DP) [20, 21, 22, 23], are able to obtain near-optimal or globally optimal control strategies, however, they should acquire entire driving cycle information in advance. The objective function can be optimized in a global sense to obtain the global optimal fuel economy for global information-driven optimization strategies. Theoretically, deterministic dynamic programming strategies can obtain the global optimal fuel economy for HEVs, providing a benchmark for assessing the optimality of other energy management strategies. However, they suffer from difficulties in practical application because they are time consuming, and tremendous memory is required for the calculation process, while they also need to acquire the driving information in advance. To obtain an applicable strategy, some researchers have attempted to design the EMS via model predictive control (MPC) [24, 25] and stochastic dynamic programming (SDP) [26], which can be directly applied online. The MPC strategy can apply various optimization methods to design the control strategy based on a period of the predicted velocity, which can be predicted via Markov Chain or artificial neural network. The optimization methods could be PMP algorithms, DDP algorithms, or some intelligent algorithms, including particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), and quadratic programming (QA). On the basis of DDP-based strategy and for the purpose of practical application, the SDP-based strategy constructs a stochastic transition probability matrix of the driver demand by Markov chain and captures the statistical characteristics in traffic information with a stochastic model. Therefore, the SDP-based strategy is a good choice for a bus driving cycle with a fixed route, due to its high regularity. However, whether it is SDP-based or MPC-based strategy, speed prediction is required, which influences the computational accuracy for EMS. With the rapid development of artificial intelligence algorithms and the large data processing technology, optimal data-driven control strategies show superior performance because of their on-line implementation and approximation to DP results. Data-driven strategies use neural networks to approximate the control sequence and cost function or the prediction of future driving patterns. At present, data-driven strategies are roughly composed of neural network-dynamic programming (NN-DP) [27], reinforcement learning (RL) [28; 29], and adaptive dynamic programming (ADP) [28; 29; 30; 31]. Among these, the ADP-based strategy can not only significantly reduce computational time and memory storage, but can also obtain similar results to DP results in achieving real-time optimization based on updated driving information.