1. Introduction
Pure Battery Electric Vehicles (BEVs) are now widely accepted in the market and have been adopted by Original Equipment Manufacturers (OEMs) owing to their simple power system structure, high mechanical efficiency, and lack of pollutant emissions when driving. However, even after many years of battery technology development [
1], range anxiety is still the major concern for BEV drivers, and this anxiety is further increased by the long charging time and insufficient charging stations [
2,
3]. Compared with BEVs, REVs are regarded as a potential solution for range anxiety. A range extender is an auxiliary power unit (APU) that provides the vehicle with additional energy. The APU does not power the wheels directly, but is used to generate electricity. There are several different types of range extenders, including internal combustion engines (ICEs), fuel cells, free-piston linear generators (FPLGs), and micro gas turbines (MGTs) [
4]. Compared with other types of range extenders, ICE range-extended EVs are currently widely adopted by manufactures, such as in the Chevrolet Volt and the BMW i3. An REV is often equipped with a larger battery compared to Hybrid Electric Vehicles (HEVs) and Plug-in Hybrid Electric Vehicles (PHEVs), which allows for a greater pure electrical range. As a result, REVs are able to produce zero emission while driving in urban areas, and travel long distances with the combustion engine as the power source. Similar to PHEVs, REVs reduce the consumption of fuel and energy originating from several energy sources compared with ICE vehicles, via their appropriate energy management strategy (EMS) [
5,
6].
The goal of energy management is to fulfill tractive power demand with the best possible fuel economy, and the battery state of charge (SoC) should be kept in the desired range under different driving conditions. To tackle these issues, numerous studies have been conducted into EMS for REVs as well as PHEVs. The EMS strategies are mainly placed into two categories: rule-based and online optimization-based [
7,
8,
9,
10]. For rule-based strategies, the energy-saving policies are determined by the operation conditions. In the meantime, battery SoC is managed according to the CD-CS method. Rule-based strategies are broadly used in the automotive industry for mass production. Their benefits in prolonging battery lifetime and reducing engine vibration have been well demonstrated [
11]. However, although the rule-based strategies are efficient and robust when implemented in vehicles, it is very difficult to achieve optimal energy-saving levels in the real world. The rules are normally obtained under ideal operating conditions. Not surprisingly, this causes poor energy-saving performance under real-world driving conditions. Wang et al. [
12] extracted the control rules from optimal algorithms, and the control parameters were optimized offline and corrected online. A better fuel economy performance was observed than under the original logic threshold rules. However, a more complex algorithm is required for the rule extraction and parameter optimization, which limits the application of this approach in massive production.
On the other hand, the optimization-based strategies are capable of adapting their control parameters to real-world conditions. Therefore, they have greater potential in terms offuel-saving. Typical optimization-based strategies include the Equivalent Fuel Consumption Minimization Strategy (ECMS) and Dynamic Programming (DP). The ECMS is a semi-analytical algorithm for optimizing hybrid power systems based on the Pontryagain minimization principle [
13]. Owing to its fast computation speed, the ECMS algorithm is able to be fully realized in real-time. Furthermore, it can not only be universally applied to hybrid power systems [
14,
15,
16], but requires no knowledge of the global operating conditions. This makes it more practical compared to DP. Though the DP algorithm can provide a theoretically optimum solution, it requires an accurate knowledge of global operation conditions, which are very difficult to get.
The key to the ECMS design is to determine an equivalent factor between fuel and electrical energy based on the available vehicle information. Then, an Adaptive-ECMS (or A-ECMS in short) strategy is proposed. Attention has been paid to the relationship between the equivalent factor and battery SoC [
17,
18,
19]. In many studies, this factor is optimized for a given typical driving cycle [
20,
21,
22]. The results are promising for cycles close to the pre-given ones, whereas they are less positive for complex real driving cycles. This is because the driving conditions in real cycles are more complicated than typical cycles due to different driver behaviors and traffic conditions. Driver behavior refers to the acceleration, aerodynamic drag force, braking frequency, energy recuperation, and the frequency of periodic and aperiodic faults, which significantly influence the energy consumption [
6,
23]. Liu et al. [
24] combined driver behavior recognition and Intelligent Transportation System (ITS) information to determine the optimum equivalent factor for the planned trip. Furthermore, the strategy was adjusted to deal with different levels of future ITS information. The result is satisfactory, but the problem is that the equivalent factor is fixed after the optimization, and it would not be dynamically adjusted during the rest of the trip. In order to achieve better optimization results, studies have recently been focused on adjusting the equivalent factor dynamically, based on driver behavior and vehicle operation conditions in the planned route. Khayyer et al. [
25] proposed a semi-empirical function to dynamically calculate the equivalent factor, based on the previous equivalent factor, the current SoC and the distance to the destination provided by ITS. A 9% fuel economy improvement can be achieved under the Urban Dynamometer Driving Schedule (UDDS) cycle. With velocity prediction up to 60 s available, the performance can be further improved. Although the results are promising, the parameters for calculating the equivalent factor rely on additional tuning, and the global optimization result has yet to be improved. A similar semi-empirical method has been adopted by Sun et al. [
26] and Zhang et al. [
27] to adjust the equivalent factor.
The above studies have shown the effectiveness of utilizing ITS information to determine the equivalent factor. However, semi-empirical methods are commonly used in most studies, and additional parameter tuning is inevitable. A better global dynamic optimization method for the equivalent factor is still yet to be developed. On the other hand, the strategy should be optimized and simplified for application in mass production vehicles. In this paper, A-ECMS is applied as the solution to the REV energy management problem. According to the traffic information provided by ITS, a practical dynamic optimization method of the equivalent factor during the planned route is proposed. Furthermore, the real-time start–stop control of the range extender is optimized by velocity forecasting to achieve better fuel economy.
This paper is organized as follows.
Section 2 deals with the system configuration and modeling of the studied REV. In
Section 3, the research method and procedure are presented.
Section 4 is focused on the energy management problem, in which the optimization of the equivalent factor based on ITS is demonstrated in detail.
Section 5 presents the further improvement of the A-ECMS strategy, wherein the start–stop optimization of the range extender is optimized through velocity forecasting to achieve better power split performance. Finally, the summary and conclusions are given in
Section 6.
6. Summary and Conclusions
A proper energy management strategy is an effective means to improve fuel economy in hybrid vehicles. This study presents a practical method to realize the global optimization of energy management in REVs, whereby the equivalent factor is dynamically optimized by the traffic information provided by ITS, and the control of the start–stopping of the range extender is further optimized through the utilization of NARX network-based velocity prediction. The studied vehicle operation conditions are restricted to a case in which the vehicle is not able to finish the planned trip when driven only by the battery, and range extender must be switched on to achieve the target SoC. On the other hand, traffic information in the planned route is a necessary input for the proposed strategy. Although cases where all needed traffic information is must be studied, these are not included in the scope of this research. Based on the established model and the proposed strategy, the vehicle performance under WLTC and real-world driving cycles is investigated via the simulation method. The following conclusions can be drawn:
- (1)
The proposed A-ECMS strategy is able to adjust the equivalent factor dynamically in relation to different power demands and battery SoCs. Then, the operation of the range extender can be optimized. Fuel consumption savings up to 9% can be achieved compared to the base CD-CS strategy under the studied real-world driving cycles. Its effectiveness is clearly shown;
- (2)
The proposed A-ECMS strategy achieves better fuel-saving performance under real-world driving cycles compared to WLTC. The fuel consumption saving under WLTC cycles is 6.6%, while it reaches up to 9% for real-world driving cycles. This is due to the fact that a more concentrated high-power region, and less range extender start–stop activity, are presented in the latter. Considering the variance in the values of battery SoC at the destination, the total energy savings are 3.7% and 4.1% for WLTC and real-world driving cycles, respectively;
- (3)
The predicted velocity of the NARX network effectively adheres to the real velocity. The maximum error is well under 3.5 m/s, which is significantly lower than that arising in the traffic flow speed provided by ITS;
- (4)
The energy saving performance is further improved by implementing a velocity prediction-based penalty function in the A-ECMS strategy. Control of the range extender’s start–stopping can then be optimized, with the fuel saving rate improving by 6.7% to 18.2% compared to the base A-ECMS strategy. This updated strategy is more effective when used in WLTC cycles, where the power distribution of the range extender is significantly shifted towards the higher-power region, with higher efficiency.
Although the effectiveness of the proposed strategies in this paper is well demonstrated, further improvements are still required. Firstly, the determination of the optimal equivalent factor relies on the traffic information provided by ITS, but in some cases, complete traffic information may not be available. The strategy has to be updated to cover these scenarios. Secondly, the proposed penalty function in the A-ECMS strategy was less efficient under the studied real-world driving cycles, where the high-power demand region was more concentrated. Further improvements should be carried out to broaden the effective range of this strategy. Thirdly, the utilization of the proposed strategy in REVs with other types of power source (such as hydrogen fuel cells) should be investigated.