Development of a simulation model of a hydrogen vehicle and a subsystem for managing power demand



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The efficiency of the hydrogen vehicle depends on the energy management algorithm, which ensures the distribution of power among different energy sources to meet the full power demand and supply the load. In this paper, a fuel cell power management strategy is developed. An algorithm is implemented that includes different types of strategies, such as: PID controller, state machine developed in MATLAB Simulink, equivalent consumption minimization algorithm and fuzzy logic controller.

In addition, the paper presents the requirements for the vehicle system and presents the development of a simulation model of a hybrid hydrogen vehicle using the Simscape library of the MATLAB Simulink package.

The results of the simulation model using different energy management strategies are presented, and the analysis of the simulation results is carried out. Based on the comparison of the operating parameters of the power distribution system, the most effective algorithm for managing the power demand is identified.

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In the production of passenger cars and vehicles, the use of power plants based on hydrogen fuel cells with a proton exchange membrane is becoming a modern trend. This is due to environmental and industry standards established within the framework of UNECE Regulation No. 83, as well as GOST R ISO 23828-2013 and SAE J2579.

For example, the use of fuel cells [1] on a 1.6 ton passenger car produces about 100 kW of power, with a consumption of 1.8 kg of hydrogen per 100 miles (161 km), which corresponds to 6.5 liters of gasoline in terms of specific energy. Fuel tanks for such cars are connected reservoirs that, under a pressure of 600-700 bar, contain 5-6 kg of hydrogen, which provides a range of 500-600 kilometers. At the same time, the emission products consist only of air and water, which makes them environmentally friendly [15].

When assessing the range of a hydrogen fuel cell vehicle according to the WLTC cycle, the best results are achieved through efficient management of the electric power generated by the hydrogen fuel cell and the traction battery, as well as the optimal distribution of this power at peak loads [2, 11], battery discharge and low temperatures. For example, the fuel cell of a Toyota Mirai passenger car generates about 114 kilowatts, while the power of the traction battery is 21 kilowatts, and the electric drive is 113 kilowatts (154 hp). This allows using the power of the battery at critical moments, which can provide a current exceeding the nominal by 5-10 times or more, which helps to compensate for the delay of the control system when requesting power from the fuel cell, and then charging the battery from it [12].

However, there are tasks where it is necessary to achieve maximum values of specific energy capacity and vehicle operating time to increase its mileage and maintain dynamic characteristics. To do this, it is necessary to develop algorithms that will effectively distribute power in a car using a hydrogen fuel cell with a proton exchange membrane.

The objective of this work is to develop mathematical models and power distribution algorithm for hydrogen fuel cell vehicles. This includes efficient control of the requested current while satisfying power requirements and increasing the autonomy of the vehicle by redistributing energy [2].

The object of the study is the electrical engineering complex of a vehicle power plant based on a hydrogen fuel cell with a proton-exchange membrane.

The subject of the research is the processes of energy distribution and implementation of power requests in an electrical complex based on fuel cells.

To achieve this goal, the following tasks were solved:

  1. Development of the functional structure of the electrical engineering complex and its implementation in a simulation environment, which includes the creation of a comprehensive numerical model that covers energy distribution models.
  2. Analysis of existing technical solutions in the field of power supply and power management systems for automotive power plants using hydrogen fuel cells.
  3. Experimental study of an electrical engineering complex in the context of numerical simulation modeling with the aim of identifying a more efficient operating algorithm.

 

Development of a simulation model of a hydrogen vehicle

There are several common methods for modeling (TS) vehicles, including the inverse and forward approaches. In the inverse approach, the model is based on a predetermined driving cycle that is strictly followed, and the vehicle speed does not change dynamically. In contrast, in the forward modeling approach, the vehicle is controlled based on a driver model that follows a given driving cycle, and therefore the speed becomes a dynamic parameter.

In the direct approach, the driving cycle defines a target speed, which serves as an input variable for the driver model. Depending on the difference between the set speed and the actual vehicle speed, the driver subsystem converts the desired speed and acceleration into accelerator pedal setpoint values. These values, in turn, generate a torque request to maintain the desired speed [6, 7].

In the direct approach, information is passed in both directions, allowing the actual output data to be fed back to the input via a feedback system. This provides a more complete understanding of the physical system and its real-world application, and allows transient states to be captured. This makes the model suitable for control system design and testing using semi-naturalistic real-time simulation. The only significant drawback of this approach is the slow computational speed of the model, as more realistic results are achieved at the expense of model complexity, which requires shorter time intervals for numerical solution [8].

To analyze and test the developed strategies for controlling the power [4] generated by the fuel cell, a simulation model of a hydrogen vehicle was created. The topology of the model using a direct approach to modeling is shown in Figure 1.

A mathematical model of a fuel cell vehicle equipped with a motor-generator, battery, direct drive transmission and associated control algorithms was developed using a direct approach in the MATLAB Simulink environment.

Fig. 1 . Topology of the hydrogen TS model using the direct modeling approach.

Fig. 1. Topology of the hydrogen TS model, using the direct modeling approach.

 

Figure 2 shows a mathematical model of a hybrid hydrogen vehicle created in the same environment [10, 13]. It includes such subsystems as “Drive Cycle”, “Driver”, “Control Unit”, “Vehicle Power Unit”, and “Visualization”.

 

Subsystem "Driving Cycle"

This subsystem includes a driving cycle model based on speed-time dependence tables. The choice of a specific cycle mode in the model is performed using the "Drive Cycle Source" block from the built-in MATLAB Simulink library, which reproduces a given vehicle route. The output parameter of this block is the signal of the reference longitudinal speed of the vehicle, which depends on time.

As part of the work, the WLTC driving cycle model was selected, which stands for Worldwide Harmonized Light-duty Car Test Cycle.

The WLTC cycle, as presented in the model (see Figure 3), includes four phases, differing in driving speed and separated by short stops. In the first phase, Low, the vehicle accelerates to a maximum speed of 56.5 km/h. In the second phase, Medium, it reaches 76.6 km/h. In the third phase, High, the car accelerates to a maximum of 97.4 km/h, and in the last phase, Extra High, to 131.6 km/h. The first two phases constitute the urban part of the WLTC cycle, while the last two constitute the suburban part.

 

Fig. 2. Mathematical model of a hybrid hydrogen vehicle.

Fig. 2. Mathematical model of a hybrid hydrogen vehicle.

 

Fig. 3. WLTC driving cycle used in the model.

Fig. 3. WLTC driving cycle used in the model.

 

Subsystem "Driver"

In the Driver subsystem, the speed calculated in the drive cycle subsystem acts as an input parameter. The output parameters of this subsystem are normalized acceleration and braking commands based on the reference and actual speed obtained through feedback from the vehicle dynamic model.

Fig. 4. Subsystem "Driver".

Fig. 4. Subsystem "Driver".

 

The subsystem is implemented using the Simulink block “Longitudinal Driver” (see Figure 4). The block settings are made in accordance with the task at hand: the control type is set to “Predictive” and the gear shift type is “Reverse, Neutral, Drive”. In this configuration, the subsystem models the behavior of the steering control when moving along a trajectory and avoiding obstacles. The logic of operation is implemented using direct modeling, which allows following a predetermined route. A logical state diagram in Stateflow was developed to model the shifting of reverse gears, neutral gear, and forward gear.

 

Subsystem "Power unit"

The subsystem is a simulation model of the power unit of a hydrogen vehicle, including an electrical installation and a transmission (see Figure 5).

In this subsystem, developed in MATLAB Simulink using the Simscape library, the fuel cell and battery are connected to the engine via a DC electrical network. The control system determines how much energy should be extracted from the battery and fuel cell. During braking, energy is recuperated to charge the battery. The cooling system is implemented as a closed loop with a cooling liquid and provides the required temperature regime for the battery, DC converters and engine.

Fig. 5. Electric power plant subsystem.

Fig. 5. Electric power plant subsystem.

"Electric Motor "

In operation, the electric motor is a brushless permanent magnet synchronous motor (PMSM) that operates within a certain range of torque and rotor speed, also known as an electric machine [9].

It operates in torque control mode, receiving the set torque value from the control unit and determining the possible operating parameters by interpolating the angular speed of the engine. Electrical losses are calculated based on tabular data that take into account efficiency depending on speed and torque.

The power consumption of an electric machine (Preq) can be calculated based on the engine speed (ω_m) and the required torque (T_m), which is determined by the value obtained from pressing the accelerator pedal (Equation 1).

                                                                                                             

The efficiency of an electric motor ( ) depends on the torque ( ) and the rotation speed ( ) and is described by equation 2.

                                                                                                                                 

The efficiency graph of the electric machine is shown in Figure 6.

 

"Battery System "

The traction battery is modeled using the Simulink library block, which is a battery model. This block simulates the open-circuit voltage depending on the state of charge (SOC) and temperature (T), based on reference tables. The equivalent circuit of the battery is shown in Figure 7 and includes the battery model, self-discharge resistor (R_sd), dynamic charging subsystem, and series resistor (R_0).

Fig. 6. Electric machine efficiency chart .

Fig. 6. Graph of the efficiency of an electric machine.

Fig. 7. Equivalent circuit of a battery.

Fig. 7. Equivalent circuit of the battery.

 

The battery model generates the open circuit voltage or battery voltage using interpolation methods according to Equation 3.

                                                                                                                                        

where is the open circuit voltage of the battery.

The battery state of charge (SOC) is defined as the ratio of the current charge to the battery's nominal capacity (

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作者简介

Evgenii Rakhmatullin

FUSE "NAMI"

Email: evgenii.rahmatullin@nami.ru

Engineer of the Software Center, Hybrid Vehicle Calibration Sector

俄罗斯联邦, 2 Avtomotornaya str., Moscow

Vladimir Debelov

FUSE "NAMI"

编辑信件的主要联系方式.
Email: vladimir.debelov@nami.ru
ORCID iD: 0000-0001-6050-0419

Head of Software technology department

俄罗斯联邦

参考

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