**1. Introduction**

In recent years, hybrid electric vehicle (HEV) technology has been rapidly developed. As a new type of power transmission technology, the HEV is recognized as one of the best solutions for energy saving and emission reduction in the world [1]. Following the success of HEV technology in the automotive field [2–4], the world's major manufacturers and related research institutions tried to apply it in the field of construction machinery. In the research and development of hybrid electric construction machinery (HECM), hybrid technology of the loader and excavator has been previously achieved [5,6]. It was not until 2008 that the Caterpillar company developed a diesel-electric driven bulldozer based on HEV technology, named D7E, which attracted extensive attention worldwide due to its excellent performance of 20% energy saving, 10% productivity improvement, and 50% service life extension [7]. After that, a special project was set up to develop the first hybrid electric bulldozer (HEB) of China in 2011 [8]. Since then, research on the HEB, as a heavy-duty off-road hybrid electric tracked vehicle, has received increasing attention [9–11].

Control strategies, especially energy management strategies, represent a hot topic in HEV technology research. The core purpose of a control strategy is to optimize the fuel economy and emission of the power system, while satisfying the driving purpose, by reasonably controlling the power distribution among multiple energy sources. The technology of the HECM or hybrid special vehicle should be based on the application, transfer, and expansion of HEV technology. Such control strategies are also research hotspots in their fields. Even with the development and introduction of autonomous vehicles, the vehicle control strategy is the most attractive technical research field [12–14]. HEBs mainly adopt the series hybrid electric powertrain due to their violently fluctuating operating resistance. As the amount of research on the control strategy of series HEB is still small, in order to fully draw lessons from related research results, we have reviewed the control strategy of series HEV, the control strategy of other types of HECMs which also face the problem of HEV technology transfer, and some of the recent research on the HEB control strategy.

Research on the control strategy of the series hybrid power system can be simply divided into three types, based on the characteristics: rule-based control [15], optimization-based control [16,17], and intelligent control [18,19]. As shown in Table 1, it can be seen that the three types of control strategies have advantages and challenges in practical engineering applications. The rule-based control strategy is the most commonly used strategy of the series HEV in practical applications. The on-off (thermostat) strategy is the simplest, and permits the genset to always work at the best efficiency point, but the engine is often switched on and off, and the energy loss in start-stop and charge-discharge is large. The power following strategy requires frequent changes in the engine operating points to follow the changing load. However, the engine efficiency is relatively low, the dynamic energy loss is large, and the charge and discharge energy loss of the energy storage unit (ESU) is small [20]. The on-off and power following strategy also are called deterministic rules. The optimization-based control makes use of various optimization algorithms to solve objective functions and obtain the optimal control law. It can be divided to instantaneous and global optimization, according to the time scale of the optimal solution. Two typical instantaneous optimization strategies are the equivalent consumption minimization strategy (ECMS) and its derivative adaptive-ECMS, which calculate the minimum objective function at every moment [16]. Dynamic programming (DP) is a classic and accepted global optimization algorithm used to measure the maximum potential of fuel saving in the whole drive cycle time. Stochastic dynamic programming (SDP) is then formed by considering some uncertainty factors in the load. Between the moment and duration of the optimal time scale, the model predictive control (MPC) converts the solution problem over the entire drive cycle time into a value over a short future period by using sensing devices and methods [21]. An indirect and analytical algorithm employed to solve the global optimal control problem is Pontryagin's minimum principle (PMP), which is also widely adopted and offers an optimal solution close to the DP result by solving a Hamiltonian minimization problem [22]. These optimization-based controls are often segmented into online and offline optimization-based strategies, depending on whether the controls can be applied in real vehicles [1,20]. Fuzzy logic is also mentioned as a kind of rule-based control for fuzzy rules [23], while it is categorized as intelligent control due to its intelligence features of non-model and nonlinear systems in this paper. Intelligent control approaches, including the back propagation neural network (BPNN), genetic algorithm (GA), and machine learning [4,24], which rely on engineering experience and engineering databases, have excellent properties in dealing with uncertain mathematical models, high nonlinearity, and complex task requirements [25,26]. They are also widely adopted in series HEVs and have a good adaptability.


**Table 1.** Classification and comparison of control strategies of the series hybrid electric powertrain.

The control strategies of different types of HECMs have also been studied in terms of the above three aspects. The rule-based control strategies of HECMs have been researched the most. Unlike HEVs, the control strategies of HECMs mainly require the characteristics of operating conditions to be combined. In research on the control strategy of a hybrid excavator, the pressure of the hydraulic pump was often used to identify the working load, and the control rules were made according to the load level [27]. The working process of the excavator and the moving process of each part were analyzed in detail, and the pressure of the operating system was measured in real time to obtain an estimate of the required and recoverable energy [28]. The key point of energy saving control for excavators lies in the design of energy recovery control for moving parts, such as boom and swing systems [29]. Research on the hybrid loader control strategy has also paid attention to the discrimination of operating conditions and to making rules according to the characteristics of the load. The power consumed by a loader's hydraulic system and the impact on the powertrain's dynamic response have often been taken into account to design the strategy [30]. In most studies, the load of the operating system was incorporated into the energy management of the hybrid loader by measuring the outlet pressure of its hydraulic pump [31]. The characteristics of the high transiency and periodicity of construction machinery are both common and specific in control strategy design. The complexity of the working environment of construction machinery, as well as the significant and frequent changes in operating loads, bring new difficulties to energy management and control [6,32]. Research on the HECM control strategy based on an optimization algorithm has been increasing in recent studies. For instance, Nilsson et al. proposed a predictive control approach using SDP under severe disturbances and uncertainties, according to the repetitive pattern of operation of the wheel loaders [33]. SDP control based on prediction also achieved an energy consumption optimization effect on the hybrid excavator [34]. A comparative assessment of ECMS, DP, and thermostat controllers [35]; joint control and parameter optimization by adopting DP control and GA [36]; and a comparison of DP, PMP, and MPC [37], for a hybrid excavator have been conducted. Since intelligent control approaches have a good robustness for nonlinear systems, and HECMs have complex operating conditions and uncertain model parameters, intelligent controls have also been widely studied in this field. Intelligent algorithms such as reinforcement learning [38,39], fuzzy logic [40,41], and the particle swarm optimization (PSO) algorithm [42] have been applied to the energy saving control of HECMs. Therefore, research on other types of HECM control strategies developed according to the characteristics of their operating conditions can provide enlightenment for us to control the HEB: the characteristics of HECMs, such as the periodicity of work, short idle time, and high fluctuation [43], especially the power supply and recovery of the hydraulic actuators, should be taken into account in the control.

At present, only a few institutions have actually developed HEBs, but research articles on control and simulation of the HEB have been increasing in recent years [7,8,44]. In [45,46], the fuel-saving control concept of D7E was briefly introduced; that is, engine control in the series system was not affected by the load torque so that the speed of the generator could be controlled within a narrow range to improve the efficiency, but the detailed control strategy was not given. Song et al. proposed a power following control strategy based on the minimum fuel consumption curve of the engine

for an HEB [9]. Wang et al. also proposed a load power following control strategy and adopted three engine speed control methods for a comparative study [11]. However, these studies did not take the efficiency of the generator into account when calculating the optimal efficiency. Wang et al. proposed applying MPC to an HEB, compared the MPC to DP and a rule-based control strategy, and artificially added white noise interference to test the robustness of MPC, which indicated that the energy consumption and robustness of the HEB under the MPC strategy are better than those of the rule-based strategy [10,47–49]. Wang et al. then proposed an improved MPC strategy for an HEB without future driving information [50], and applied the MPC to the analysis of the HEB's energy storage unit [51].

Although various types of algorithms have been applied to the control of the HEB in the existing literature, these studies have basically treated the HEB as a tracked vehicle similar to [52,53], which did not pay enough attention to the difference between HEVs and HEBs. Specifically, in most of these HEB control strategies, the influence of the torque consumed by the hydraulic pump of the working system on the genset operating point has not been considered, even though instantaneous disturbance of the hydraulic pump would increase the fuel consumption and emission [45]. In addition, there has been little discussion about the fact that the transient fuel consumption caused by frequent changes of the engine operating points is higher, in which case the load power of the bulldozer fluctuates violently. Additionally, very little attention has been paid to the fact that the frequent adjustment of the HEB's engine operating points causes an insufficient instantaneous response of the engine and then leads to the points deviating from the optimal target trajectory, before finally resulting in an engine efficiency decline. Therefore, it is necessary to carry out in-depth research on these problems for developing the control strategy of HEBs.

Therefore, the purpose of this paper is to propose an innovative Adaptive Smoothing Power Following (ASPF) control strategy to solve the above mentioned problems. In general, the problems include frequent fluctuations of the engine working points, deviation of the genset working points from the pre-set target trajectory due to an insufficient response, and interference of the hydraulic pump consumed torque, all of which result in the fuel consumption increasing. The control strategy takes the impact of the drastic fluctuation in the bulldozer's load and the abrupt demand torque of the hydraulic pump on the working points of the HEB's genset into consideration. The adaptive smoothing algorithm is used to automatically reduce the transient fuel consumption and the working points' deviation from the high efficiency zone. In addition, the algorithm is combined with an optimal efficiency map of the genset considering the correction for interference of the operating system demand torque. A novel transient fuel consumption model embedded in the HEB model and a hardware in loop (HIL) platform is developed to test the proposed approach. The methodology of the proposed control strategy is a combination of fuzzy control theory, real-based control, and the optimization method. The verification method is based on adopting a validated simulation model and an HIL test platform. The test indicates that the proposed approach could solve the above problems and feature an excellent on-line real-time robustness and adaptability for energy saving of the HEB.

The rest of this article is organized as follows: the HEB model with a novel transient fuel consumption model based on BPNN is developed in Section 2; in Section 3, the ASPF based on an optimal efficiency map strategy is proposed and described; the HEB HIL platform is built in Section 4; Section 5 presents the simulation results and a discussion of the proposed approach; and the last section summarizes the major conclusions.

## **2. HEB Simulation Model**

A simulation model is the basis for recognizing the characteristics of each part of the HEB and analyzing the control strategy. The control strategy performance can be simulated and analyzed after being incorporated into the simulation model. Additionally, the simulation model is essential and primary for the HIL platform test. Therefore, the simulation model of HEB should first be established. The modeling process is based on an analysis of the HEB architecture and component characteristics. The detailed modeling and validation process information can be found in our previous study [54]. The theoretical foundation of the model is derived from the bulldozer's dynamics and kinetics, the component characteristics, and the modeling method of Advanced Vehicle Simulator (ADVISOR) software (Version 2002, National Renewable Energy Laboratory, Golden, CO, USA) [55].
