The range-extended electric vehicle is proposed to improve the range anxiety drivers have of electric vehicles. Conventionally, a gasoline/diesel generator increases the range of an electric vehicle. Due to the zero-CO2 emission stipulations, utilizing fuel cells as generators raises concerns in society. This paper presents a novel charging strategy for fuel cell/battery electric vehicles. In comparison to the conventional switch control, a fuzzy control approach is employed to enhance the battery’s state of charge (SOC). This approach improves the quick loss problem of the system’s SOC and thus can achieve an extended driving range. Smooth steering experience and range extension are the main indexes for development of fuzzy rules, which are mainly based on the energy management in the urban driving model. Evaluation of the entire control system is performed by simulation, which demonstrates its effectiveness and feasibility.
In order to reduce our dependence on petrochemical resources and decrease the deterioration of the earth’s environment, the United Nations (UN) passed the “Kyoto Protocol” in December of 1997. Basically, the Kyoto Protocol progressively restricts the emissions of carbon dioxide (CO2) in every industrialized country. Consequently, this protocol has resulted in a massive impact on vehicle engineering. The result is a new challenge to the design of revolutionary vehicles for the future. Thus, the proposal of novel, low-pollution, and pollution-free vehicles has increased including concepts such as hybrid, ethanol, hydrogen fuel cell, and electric-power motor cars. In these next-generation automobiles, the fully electric motor vehicles have pollution-free benefits, which drive further research and development [ The battery charging requires ample time—several hours for common home chargers. Currently, the maximum range of a battery-powered car is less than its gasoline engine counterpart, which thus creates range anxiety.
In order to endow the electric vehicle with a similar driving experience as the gasoline engine car, the range-extended electric vehicle (REEV) [
Conventional REEVs use the switch regulation to control the fuel cell generator for charging. Basically, the generator initiates charging when the SOC drops to less than 20% (or in some studies, 30%) [ If the SOC is at a low level, the vehicle will fall into a stop-and-go period. Otherwise, the vehicle will stop for a long time, and the driver will have to wait for the SOC to recover. The battery cannot perform charging and discharging at the same time. In highway driving, due to the continuous pedal command requests, it is difficult for the generator to charge the battery. Consequently, the SOC will drop quickly under this scenario.
In order to solve these problems, Chen et al. [
This paper aims to make use of the advantages of electric vehicles driven by a fuel cell/battery to reveal a new concept on REEV. It is structured as follows. The system modeling is introduced in Section
The fuel cell system considered in the simulation study is based on the design manufactured by Asia Pacific Fuel Cell Technologies, Ltd. This system is powered by a proton exchange membrane fuel cell (PEMFC). The inputs of the system are hydrogen and air, while the outputs are cell voltage and current. The dynamics of the fuel cell system is nonlinear and time varying. It is influenced by many factors, including the diffusion dynamic, the Nernst equation, proton concentration dynamics, and cathode kinetics as illustrated in Figure diffusion equation: Nernst equation: proton concentration dynamics: cathode kinetics:
From the system point of view, the physical model of Figure
PEMFC dynamics.
Block diagrams of fuel cell system.
The battery model in the simulation study is simplified as an equivalent circuit with a voltage source and a resistance [
Equivalent circuit of simplified battery model.
According to the investigation results in [
Consider a four-wheeled vehicle in a longitudinal motion, as depicted in Figure
Vehicle model.
Figure
Schematics of presented FC-REEV system.
Driving cycle is a simulation pattern for evaluating the vehicle’s fuel economy in different scenarios. It is an important tool for design in driveline and control strategies. The main purpose in powertrain design is to minimize fuel consumption and component costs, while maximizing drivability. Since it is still cost-ineffective to build prototypes of FC-EV, the FC-REEV becomes an alternative in early stages of the development. The driving cycle is formed from numerous tests. Nowadays, it is a standard process in fuel economy evaluation. Basically, driving cycle is a speed profile where the most common scenarios of steering, such as rapid traction, braking, and coasting, are concerned. Its speed profile is defined as a function of time for a fair evaluation. Several driving cycles have been developed by governments around the world as tools for vehicle certification. The famous New European Drive Cycle (NEDC) in Europe and the Federal Test Procedure (FTP) in the USA are sufficient benchmarks. In this study, as shown in Figure
Driving pattern of NEDC.
In other words, one cycle of the NEDC is just 10.9314 km. It is not sufficient to represent all urban driving situations. Therefore, in this study, the driving pattern of NEDC is repeated ten times (i.e., 109.314 km) for a more realistic steering scenario. Under this assumption, the fuel economy and reliability of the FC-REEV can further reveal its major contribution to range extension.
As mentioned in the Introduction, because the battery cannot be charged and/or discharged simultaneously, the extended range of REEV is mainly relevant to the charging style to the battery. The conventional switch control strategy, which is also named the thermostat control strategy (TCS) in [
Generally, when controlling a process, human operators usually encounter complex patterns of qualitative conditions, which are not easy to quantify. For example, in many applications, the measurement data can be classified as fast, slow, high, low, and so on. Such linguistic variables are employed in describing inexact information. To represent such information, a new mathematical approach called fuzzy theory was proposed by Zadeh [ P0: status > 80%, P1: status = 70%, P2: status = 60%, P3: status = 50%, P4: status = 40%, P5: status = 30%, P6: status ≤ 20%.
Triangular membership functions.
The shape of the membership functions is quite arbitrary and is dependent on the user’s preference. For the sake of mathematical simplicity, the triangular shape is utilized due to practical considerations. Note that there are many membership functions that can be utilized. The triangular membership function is one of the candidates. Based on the research results from Pedrycz [
Due to the guarantee of expert experience, most commercial fuzzy products are rule-based systems. They receive the current states in the feedback loop and check the rules for control and operation. A basic fuzzy logic system can be found in Figure
Fuzzy controller block diagram.
Table
Fuzzy rules.
P6 of distance | P5 of distance | P4 of distance | P3 of distance | P2 of distance | P1 of distance | P0 of distance | |
|
|||||||
P6 of SOC | Case 1 | Case 1 | Case 2 | Case 2 | Case 2 | Case 3 | Case 3 |
P5 of SOC | Case 1 | Case 1 | Case 2 | Case 2 | Case 2 | Case 3 | Case 3 |
P4 of SOC | Case 1 | Case 1 | Case 2 | Case 2 | Case 2 | Case 3 | Case 3 |
P3 of SOC | Case 2 | Case 2 | Case 4 | Case 4 | Case 4 | Case 5 | Case 5 |
P2 of SOC | Case 2 | Case 2 | Case 4 | Case 4 | Case 4 | Case 5 | Case 5 |
P1 of SOC | Case 3 | Case 3 | Case 5 | Case 5 | Case 5 | Case 6 | Case 6 |
P0 of SOC | Case 3 | Case 3 | Case 5 | Case 5 | Case 5 | Case 6 | Case 6 |
Note that the percentage of the charging/pedal sharing ratio is adjustable according to a specific index. Additionally, the distance is strictly indexed by ten cycles of NEDC (i.e., 109.314 km). The fuzzy controller receives statuses of the range and SOC from feedback sensors and regulates the duty ratio between the SOC and pedal.
In this section, the presented fuzzy control approach is tested in simulation for performance verification. The whole system of Figure
Comparative simulations of SOC.
Figure
Speed profile of fuzzy control.
Considering the fuel efficiency issue, Figure
Vehicle traction/braking power losses.
Traction power losses
Braking power losses
This paper has investigated the novel range extension strategy for fuel cell/battery electric vehicles. The analysis was carried out by the simulation results done by MATLAB/Simulink. The presented charging sharing idea, which is regulated by a fuzzy rule table, has revealed that the quick loss of SOC can be remedied for high-speed steering. The battery’s lifetime and system’s fuel economy have confirmed an improvement under the proposed energy management. These evaluations have demonstrated its effectiveness and potential feasibility. The main findings from the simulation results are described below. Under the regulation of sufficient fuzzy control, the urban driving experience for FC-REEV can be the same as the internal combustion engine car. Hence, range anxiety can be fully managed in the proposed approach. Fuzzy strategy also improves the battery’s lifetime and fuel economy of a REEV system. A safe and continuous driving experience is guaranteed in the proposed FC-REEV. The operator can enjoy almost the same driving experience as the conventional engine car. FC-REEV has revealed a better fuel economy than the conventional REEV. Additionally, the FC-REEV is a purely zero-CO2 emission car. The energy consumption from the gearbox can be further saved when the powertrain design is changed. For example, the power decentralized electric vehicles that utilize the in-wheel motor to propel the vehicle’s motion can achieve less heat loss on gear wearing. Investigations of relevant issues are worth studying in future work.
This publication has been approved by all the authors and explicitly by the responsible authorities where the work was carried out. The authors declare that there is no conflict of interests regarding the publication of this paper.
This work was supported by the Ministry of Economic Affairs (MOEA) of Taiwan and Science Park Administration (SPA) of Taiwan under Projects of MOEA 102-D0621 and SPA 102A38, respectively.