With growing problems of energy consumption and environmental pollution, the energy crisis and climate change have attracted more and more attention from all over the world. The large consumption of fuel and emission of exhaust gas have a seriously negative impact on these issues. Consequently, there is an urgent need for countries to develop energy-efficient vehicles to cope with the global energy crisis and climate change [
1]. Hybrid electric vehicles (HEVs), with their low energy consumption and extended driving range, are widely recognized as a public transportation solution with significant potential for further development [
2,
3]. HEVs, as distinguished from conventional vehicles, are equipped with new types of energy storage devices such as batteries or supercapacitors. As sustainable energy storage technologies, they possess advantages such as long cycle life and less pollution [
4].
HEVs can be classified into series, parallel, and series–parallel types based on system configuration. However, the most representative series–parallel configuration is the power-split configuration. Power-split hybrid electric vehicles (PSHEVs) have received widespread attention and application due to their excellent fuel economy [
5]. Numerous automobile manufacturers worldwide are actively investing in the development of HEVs, and currently, PSHEVs have taken a dominant position in the market [
6,
7]. However, the complexity of the structure of power-split hybrid heavy-duty trucks leads to two important issues that must be considered during the study of their fuel consumption. Firstly, the impact of key parameters of the power system on vehicle fuel consumption must be considered, as their rational optimization can improve the vehicle’s fuel economy. Secondly, the development of efficient energy-management control strategies can further enhance the fuel economy of the vehicle. The focus of this paper is a hybrid heavy-duty truck, specifically a heavy-duty dump truck. Compared to general vehicles, heavy-duty dump trucks face more complex and variable operating conditions. Therefore, when facing such complex conditions, it is crucial to optimize key parameters of the powertrain system or control strategies for hybrid heavy-duty dump trucks in order to reduce fuel consumption.
1.1. Optimization of Key Parameters
To improve fuel economy, the optimization of key parameters of the power system in hybrid heavy-duty trucks has become research focus. Common optimization algorithms include simulated annealing (SA) [
8], particle swarm optimization (PSO) [
9,
10,
11], and the genetic algorithm (GA) [
12]. Yang et al. combined PSO and rapid dynamic programming (rapid-DP) to optimize the key parameters of the power system of power-split hybrid vehicles. Simulation analysis indicates that the fuel consumption of the vehicle was reduced by 6.56% and 3.15% under FTP72 and HWFET cycle conditions, respectively [
13]. Chu et al. conducted a parallel hybrid powertrain parameter optimization study and performed simulation analysis under urban driving cycle conditions in China; they achieved a higher fuel economy with the same state of charge (SOC) before and after optimization [
14]. Sheng et al. used the GA to optimize the gear ratio of a two-speed AMT in a pure electric vehicle. Simulation verification under the NEDC cycle showed that the driving range increased by 5.85% after optimization [
15]. Hao et al. optimized the key parameters of the energy-management strategy for parallel hybrid electric vehicles using the DIRECT algorithm. Seven key parameters, including engine torque and battery state of charge (SOC), were identified for optimization, and the effectiveness of the DIRECT algorithm was validated [
16]. Fu et al. conducted powertrain system parameter-matching optimization for parallel hybrid electric vehicles, with parameters including motor maximum torque and engine maximum torque. Their proposed parameter-matching optimization methods for hybrid electric vehicles significantly reduce fuel consumption compared to that of traditional methods [
17].
1.2. Optimization of Energy-Management Strategies
The energy-management control strategies are mainly divided into rule-based control strategies, optimization-based control strategies, and based-on-learning control strategies. Based-on-learning strategies for HEVs and other control problems have been investigated. For example, Nuchkrua et al. explored the application of pneumatic artificial muscle (PAM) based on metal hydride (MH) as a compact compliant actuator and applied a learning-based adaptive robust control to solve the problem of PAM actuator compliance control [
18]. Ma et al. conducted research on short-term household load forecasting in the context of home energy-management systems (HEMS). Firstly, they presented the application of deep-learning algorithms such as recurrent neural networks (RNN), long short-term memory (LSTM), and convolutional neural networks (CNN) in short-term household load forecasting. Meanwhile, they proposed that combining forecasts using multiple deep-learning methods can effectively improve model generalization [
19]. Learning-based control strategies such as deep learning are helpful for the research of fuel economy in hybrid heavy-duty trucks. However, based-on-learning strategies usually require heavy computational resources, which leads to difficult implementation in real-time applications with automobiles. The advantages of rule-based control strategies (RCSs) include low computation burden, good stability, and reliability. In engineering practice, in order to consider the implementation ability of energy-management strategies, engineers often adopt energy-management strategies based on logical threshold rules [
20,
21]. However, the formulation of traditional strategies based on logic-threshold rules relies on expert experience, so it is difficult to guarantee the optimality of fuel consumption. Optimization-based control strategies are mainly divided into global optimization and real-time optimization [
22]. A typical representative of global optimization methods is dynamic programming (DP) [
23,
24,
25,
26]. DP has the drawback of complex computation and is unsuitable for real-time control of real vehicles, but it has been recognized as the standard for energy optimization of HEVs due to its ability to obtain globally optimal solutions [
27]. More importantly, DP can use its optimal solution to extract the optimal control strategy based on RCSs, which can further improve the fuel economy of conventional RCSs.
The DP-based rule-control strategy (DP-RCS) has been increasingly attracting researchers’ attention due to its outstanding fuel economy and online applicability. Riccardo et al. designed an energy-management strategy for medium-sized parallel hybrid trucks by extracting rules from the DP algorithm [
28]. Domenico et al. extracted rules from the global optimal results of the DP and proposed a feasible RCS for series/parallel hybrid vehicles [
29]. Peng et al. proposed a recalibration method to improve the performance of the RCS by using the results calculated by the DP [
30]. Fan et al. proposed an offline parameter extraction method with the DP algorithm for plug-in P2 hybrid electric buses, aiming to improve driving performance. In this method, three typical driving cycles were considered and applied to extract the boundary conditions for engine start/stop state switching and gear shifting. Finally, it was concluded that these boundary conditions have a significant effect on driving performance [
31]. After determining the energy-management strategy for a hybrid heavy-duty truck, powertrain parameters and the selected control strategy interact with each other and collectively determine the fuel economy of a hybrid vehicle [
32].
However, in the aforementioned studies, it is evident that there is lack of attention towards the following topics. Firstly, the selection of the vehicle configuration has been studied for parallel configurations, with limited focus on complex configurations such as power-split configuration. Furthermore, the research on hybrid vehicles has paid less attention to powertrain parameters (e.g., final drive ratio, transmission gear ratio) optimization and has mainly focused on optimizing threshold parameters of certain components in the strategy. Finally, few studies consider both the optimization of powertrain parameters and control strategy at the same time. Therefore, the objective of this study is to investigate the fuel economy of hybrid heavy-duty trucks employing power-split configurations. Special attention is given to the optimization of powertrain parameters for hybrid heavy-duty trucks, in conjunction with the DP-RCS, to provide an effective optimization approach to enhance the fuel efficiency of hybrid heavy-duty trucks.
The contributions of this paper mainly include the following two aspects: (1) Using the improved particle swarm optimization (IPSO) algorithm and the DP algorithm to optimize powertrain parameters of hybrid heavy-duty trucks. (2) Designing the DP-RCS and obtaining the optimal DP-RCS scheme to further improve the fuel economy of hybrid heavy-duty trucks based on optimal powertrain parameters.
The rest of this paper is organized as follows:
Section 2 introduces the model of a hybrid heavy-duty truck and the modeling of the whole-vehicle components.
Section 3 introduces the IPSO and DP algorithms and presents the optimization simulation of powertrain parameters.
Section 4 first introduces the RCS and DP-RCS, and then designs the optimal DP-RCS.
Section 5 describes some results and analyses. Conclusions are drawn in
Section 6.