A hybrid power system energy management method based on dual-stack fuel cells

Abstract

This invention discloses an energy management method for a hybrid power system based on a dual-stacking fuel cell, belonging to the field of fuel cell hybrid power energy management. Specifically targeting large freight trucks, this invention, while ensuring truck power performance, couples various costs of the freight truck into an objective function using an equivalent hydrogen consumption method. By optimizing the fuel cell output power, the overall operating cost is reduced, and fuel cell efficiency is effectively improved. For a hybrid power system combining a dual-stacking fuel cell and a lithium battery, this invention establishes a vehicle hydrogen consumption model based on an ECMS strategy, using fuel cell output power as the control variable. It constructs an objective function based on optimal operating cost for the hybrid power system, solves for the minimum value of the objective function, and obtains the fuel cell output power and battery power under the state of lowest overall system loss, thereby reducing fuel cell and lithium battery losses and extending their lifespan.

Description

Technical Field

[0001] This invention belongs to the field of energy management for fuel cell hybrid electric vehicles, and particularly relates to an energy management method for a hybrid electric vehicle system based on a dual-stack fuel cell. Background Technology

[0002] Energy consumption and environmental pollution are pressing global issues that urgently need addressing. Road freight transport is a crucial component of the transportation industry's efforts to reduce carbon emissions. Heavy-duty trucks are primarily used in logistics and construction, and their increasing demand has led to a surge in their fleet. However, due to their high carrying capacity, the energy consumption and carbon emissions of heavy-duty trucks have long been a societal concern. Therefore, current attention is focused on upgrading and reforming heavy-duty trucks. Fuel cell vehicles, with their higher hydrogen energy density and rapid refueling capabilities, offer higher power output, meeting the extended range requirements of heavy-duty freight trucks.

[0003] Current research, due to the size limitations of fuel cells, cannot meet the high power output of a single fuel cell, which contradicts the high power demands of heavy-duty trucks. Furthermore, relying on a single energy source can lead to excessive performance degradation of the fuel cell. Therefore, the mainstream view on the energy configuration of fuel cell heavy-duty trucks is to adopt a multi-stack fuel cell system to meet their power requirements, while also improving overall efficiency.

[0004] Currently, commonly used energy management strategies for fuel cell hybrid power systems can be categorized into three main types: rule-based methods, optimization-based methods, and machine learning-based methods. Rule-based strategies, with their low computational cost and ease of implementation, are widely used in the industrial field of hybrid vehicle energy management. However, the formulation of relevant rules relies heavily on engineering experience, and their sensitivity to operating conditions leads to a lack of adaptability and poor robustness. Optimization-based strategies are mainly divided into global optimization and instantaneous optimization management strategies. These strategies aim to minimize a multi-objective value function. Global optimization methods can achieve the advantage of global optimum, but they depend on known operating conditions and have long computation times, making real-time control impossible. Instantaneous optimization methods employ single-step or multi-step optimization, which is faster than global optimization and more suitable for practical applications. Summary of the Invention

[0005] This invention provides an energy management method for a hybrid power system based on a dual-stack fuel cell. While ensuring vehicle power performance, it adopts an ECMS strategy and combines it with a multi-objective cost function to ensure that the SoC varies within a reasonable range while reducing the overall driving cost.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] An energy management method for a hybrid power system based on dual-stack fuel cells includes the following steps:

[0008] Step 1: Build a heavy-duty truck model with dual fuel cells;

[0009] Step 2: Establish the optimization objective function, obtain the instantaneous optimal operating point, allocate control variables to minimize instantaneous consumption, and build a fuel cell hybrid power energy management model based on the ECMS strategy;

[0010] Step 3: Obtain the relevant driving condition training dataset and perform simulation calculations for the driving conditions.

[0011] In the steps described above, the model in step 1 includes vehicle dynamics, fuel cell and lithium battery models, and system loss models;

[0012] The dynamic model of the fuel cell heavy-duty truck is as follows:

[0013]

[0014] Where P is the vehicle power demand, v is the vehicle's current speed, m is the vehicle's mass, μ is the rolling resistance coefficient, ρ is the air density, A is the frontal area, and C... d Here, θ is the air resistance coefficient, a is the current acceleration of the car, and θ is the angle between the road surface and the horizontal plane.

[0015] The fuel cell truck hybrid power system consists of two fuel cells with the same power and a lithium battery; the hydrogen consumption of the fuel cell is equivalent to the fuel cost, and the various losses of the fuel cell and lithium battery during operation are equivalent to the loss cost, and a system loss model is established.

[0016] Step 2 specifically includes the following steps:

[0017] Given the upper and lower power limits of fuel cell 1 and fuel cell 2;

[0018] Given the upper and lower limits of the lithium battery's output and input power;

[0019] Given the upper and lower limits of lithium battery charge;

[0020] Let the vehicle speed (Velocity), vehicle acceleration (Acceleration), and lithium battery SoC be represented as a state variable vector: S = [Velocity, Acceleration, SoC]. T The fuel cell output power is a control variable because the heavy-duty truck power system of this invention uses a dual-stack fuel cell, and the control variable vector is action = [P]. fc1 ,P fc2 ] T The objective function is shown in formula (2):

[0021] J=-{α[C cost (t)]+β[SoC ref -SoC(t)] 2} (2)

[0022] Among them, C cost (t) represents the total cost of the car's operation at the current moment, SoC ref Here, SoC(t) represents the expected SoC reference value for the lithium battery, α represents the battery SoC value at the current moment, β represents the total cost loss weight, and β represents the weight for maintaining the battery SoC.

[0023] The multi-objective cost consumption function C of the heavy truck cost (t), which contains four terms:

[0024] C cost =C fuel +C fcs +C bat +D SoC (3)

[0025] Among them, C fuel For the fuel consumption cost of a car, C fcs C represents the cost of fuel cell losses during vehicle operation. bat D SoC It is the penalty term for changes in SoC in the cost function;

[0026] The objective function is solved using the fmincon function:

[0027]

[0028] Here, c(x) and ceq(x) are functions that return vectors, b and beq are vector values, and J is determined by the relationship Aeq*x = beq. min The minimum x value of the objective function, lb and ub are used as its upper and lower limits.

[0029] The range of the state variables solved using the fmincon function is defined as follows:

[0030]

[0031] Among them, P fc1 (t) and P fc2 (t) represents the output power of fuel cell 1 and fuel cell 2, P batmin and P batmax Let P and P represent the minimum and maximum output power of the lithium battery, respectively. This design uses a fuel cell with the same output power, therefore P...fcmin and P fcmax I represents the minimum and maximum output power of the fuel cell, respectively. batcharge_min and I batdischarge_max This indicates the minimum and maximum current values ​​during the charging and discharging of a lithium battery.

[0032] Step 3 specifically includes the following steps:

[0033] Step (α): Calculate the power P required for the vehicle to run based on the driving conditions. load And set the initial SoC of the lithium battery for simulation calculation;

[0034] Step (β): Calculate the control variables [Pfc1, Pfc2] and the current lithium battery SoC using a model based on the ECMS strategy;

[0035] Step (γ): Return the state variable SoC to calculate the new control variable;

[0036] Step (δ): Repeat steps (α) to (γ) to obtain the fuel cell output power distribution at the lowest operating cost based on the setting of the multi-objective function.

[0037] Beneficial Effects: This invention provides an energy management method for a hybrid power system based on a dual-stall fuel cell heavy-duty truck. First, a fuel cell heavy-duty truck model is built. Second, relevant functions of the multi-objective cost function of the ECMS strategy are set, with the objective function including fuel cell loss cost, lithium battery health loss cost, hydrogen consumption, and SoC change penalty terms. Then, relevant training datasets are obtained for model training, and the model is used for energy management of the dual-stall fuel cell heavy-duty truck. While ensuring power performance, the various costs of the freight truck are coupled into an objective function through an equivalent hydrogen consumption method. By optimizing the fuel cell output power, the overall operating cost is reduced, and fuel cell efficiency is effectively improved. This invention targets the optimal function for the full-mileage operating cost of a dual-stall fuel cell heavy-duty truck, employing an ECMS strategy to optimize the allocation of fuel cell output power. This avoids the fuel cell operating time in the high-loss range, extends fuel cell lifespan, and reduces lithium battery health loss, thereby lowering the overall operating cost of the truck and improving transportation economy. Attached Figure Description

[0038] Figure 1 This is a structural diagram of the dual-stack fuel cell heavy-duty truck provided in the embodiments of the present invention;

[0039] Figure 2 This is a schematic diagram of the ECMS-based energy management strategy in the hybrid power system energy management method for a dual-stack fuel cell heavy-duty truck provided in this invention example;

[0040] Figure 3 This is an optimized fuel cell output power distribution diagram provided in the example of the present invention;

[0041] Figure 4 This is the optimized cost distribution diagram provided in the example of the present invention;

[0042] Figure 5 This is an optimized fuel cell power density distribution diagram provided in the example of the present invention. Detailed Implementation

[0043] The present invention will now be described in detail with reference to the accompanying drawings and specific examples:

[0044] like Figure 1 As shown, the dual-stack fuel cell heavy-duty truck mainly consists of two PEMFC systems (maximum output power 95kW), lithium batteries, motors, transmissions, and an energy management system controller. Each fuel cell is connected in parallel with the lithium battery to the DC bus via a DC / DC converter, and powers the motor via a DC / AC converter, which then transmits the power to the wheels via the transmission.

[0045] like Figure 2 As shown, an energy management method for a fuel cell hybrid power system based on an ECMS strategy is described. Its basic working principle is as follows: acquire vehicle speed (Velocity), vehicle acceleration (Acceleration), and lithium battery SoC (System-on-Chips). The state variable vector is S = [Velocity, Acceleration, SoC]. T The fuel cell output power is the action variable, because the heavy-duty truck power system of this invention uses a dual-stack fuel cell, and the action variable vector is action = [P]. fc1 ,P fc2 ] T The multi-objective function is shown in equation (1):

[0046] J=-{α[C cost (t)]+β[SoC ref -SoC(t)] 2} (1)

[0047] Among them, C cost (t) represents the total cost of the car's operation at the current moment, SoC ref Let be the expected SoC reference value for the lithium battery, SoC(t) be the battery SoC value at the current moment, α be the total cost loss weight, and β be the weight for maintaining the battery SoC. The total cost function is:

[0048] C cost =C fuel +C fcs +C bat +DSoC (2)

[0049] Among them, C fuel For the fuel consumption cost of a car, C fcs C represents the cost of fuel cell losses during vehicle operation. bat D SoC It is the penalty term for changes in SoC in the cost function;

[0050] The objective function is solved using the fmincon function:

[0051]

[0052] Here, c(x) and ceq(x) are functions that return vectors, b and beq are vector values, and J is determined by the relationship Aeq*x = beq. min The minimum x value of the objective function, lb and ub are used as its upper and lower limits.

[0053] The range of the state variables solved using the fmincon function is defined as follows:

[0054]

[0055] Among them, P batmin and P batmax Let P and P represent the minimum and maximum output power of the lithium battery, respectively. This design uses a fuel cell with the same output power, therefore P... fcmin and P fcmax I represents the minimum and maximum output power of the fuel cell, respectively. batcharge_min and I batdischarge_max This indicates the minimum and maximum current values ​​during the charging and discharging of a lithium battery.

[0056] The power P required for vehicle operation is calculated based on the driving conditions. load The calculation formula is as follows:

[0057]

[0058] Where v is the current vehicle speed, m is the vehicle mass, μ is the rolling resistance coefficient, ρ is the air density, A is the frontal area, and C... d Let θ be the air resistance coefficient, a be the current acceleration of the car, and θ be the angle between the road surface and the horizontal plane.

[0059] Simulations were performed by manually setting different initial SoCs for the lithium batteries; model calculations were conducted based on an ECMS strategy, ensuring that the dual-stack fuel cell and lithium battery could meet the truck's power requirements, using the fmincon function:

[0060] Here, c(x) and ceq(x) are functions that return vectors, b and beq are vector values, and J is determined by the relationship Aeq*x = beq. min The minimum x value of the objective function, lb and ub are used as its upper and lower limits.

[0061] And the boundary range is set:

[0062]

[0063] Among them, P batmin and P batmax Let P and P represent the minimum and maximum output power of the lithium battery, respectively. This design uses a fuel cell with the same output power, therefore P... fcmin and P fcmax I represents the minimum and maximum output power of the fuel cell, respectively. batcharge_min and I batdischarge_max This indicates the minimum and maximum current values ​​during the charging and discharging of a lithium battery.

[0064] The control variables [Pfc1, Pfc2] and the current lithium battery SoC are calculated; the state variable SoC is returned and substituted into the calculation to obtain a new control variable; the above calculation is repeated, and the fuel cell output power distribution under the lowest driving cost is obtained according to the setting of the multi-objective function.

[0065] Figure 3 This is a diagram showing the output power distribution of the fuel cell after optimization using the ECMS strategy in an example of the present invention. As can be seen from the diagram, the output power distribution of the fuel cell after optimization by the fuel cell hybrid power system energy management method based on the ECMS strategy is roughly distributed in the healthy operating range. The overall curve is relatively smooth, avoiding the time spent operating in the high load power and low load power ranges, and the number of start-stop cycles is also less, effectively reducing the loss of the fuel cell.

[0066] Figure 4 This is a cost distribution diagram after optimization using the ECMS strategy in an example of the present invention. The diagram shows that after optimizing the output power of the fuel cell, its loss cost is minimized, and the main cost is in hydrogen consumption. The cost of battery health status loss is also relatively small, achieving overall optimization of driving costs.

[0067] Figure 5This is a power density distribution diagram of the fuel cell optimized by the ECMS strategy in an example of the present invention. The right side is the efficiency curve of the fuel cell. As can be seen from the figure, in the power distribution based on the ECMS strategy, the power of fuel cell 1 is mostly distributed between 10 and 40 kW, while a small part is distributed between 70 and 80 kW. The output power of fuel cell 2 is the same as that of fuel cell 1. According to expert experience, 20% of the maximum output power is the low load range, and above 80% of the maximum output power is the high load range. A high-efficiency working range is defined, and the output power of the fuel cell is also roughly distributed within this range.

[0068] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0069] The above are merely preferred embodiments of the present invention. Those skilled in the art can make various modifications to these embodiments and apply the general principles described herein to other embodiments without creative effort. Therefore, the present invention is not limited to the above embodiments. Improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the present invention should be within the protection scope of the present invention.

Claims

1. An energy management method for a hybrid power system based on a dual-stack fuel cell, characterized in that, Includes the following steps: Step 1: Establish a dual-stack fuel cell vehicle model, wherein the fuel cell hybrid power system consists of two fuel cells with the same power output and a lithium battery; Step 2: Establish an optimization multi-objective function, obtain the instantaneous optimal operating point, allocate control variables to minimize instantaneous consumption, and build a fuel cell hybrid power energy management model based on the ECMS strategy; specifically, this includes the following steps: giving the upper and lower power limits of fuel cell 1 and fuel cell 2; Given the upper and lower limits of lithium battery output power and input power; given the upper and lower limits of lithium battery charge. Vehicle speed (Velocity), vehicle acceleration (Acceleration), and lithium battery SoC are treated as a state variable vector. The output power of the fuel cell is a control variable, and the control variable vector is... The objective function is as follows: ， in, This represents the total cost of driving the car at the current moment. This is a reference value for the expected SoC of lithium batteries. This represents the battery SoC value at the current moment. Weighted by total cost loss. It maintains the weight of the battery SoC; where the multi-objective cost consumption function It includes four items: ， in, For the fuel consumption cost of a car, The cost of fuel cell losses during vehicle operation. The degradation cost caused by changes in the state of harmonics (SOH) of lithium batteries. It is the penalty term for changes in SOC in the cost function; Step 3: Obtain the relevant driving condition training dataset and perform simulation calculations for the driving conditions.

2. The energy management method for a hybrid power system based on a dual-stack fuel cell according to claim 1, characterized in that, The model described in step 1 includes vehicle dynamics, fuel cell and lithium battery models, and system loss models. The hydrogen consumption of the fuel cell is equivalent to the fuel cost, and the various losses of the fuel cell and lithium battery during operation are equivalent to the loss cost, thus establishing a system loss model.

3. The energy management method for a hybrid power system based on a dual-stack fuel cell according to claim 2, characterized in that, The vehicle dynamics model is as follows: ， Where P is the vehicle power demand, v is the vehicle's current speed, m is the vehicle's mass, μ is the rolling resistance coefficient, ρ is the air density, A is the frontal area, and C... d Let θ be the air resistance coefficient, a be the current acceleration of the car, and θ be the angle between the road surface and the horizontal plane.

4. The energy management method for a hybrid power system based on a dual-stack fuel cell according to claim 1, characterized in that, The objective function is solved using the fmincon function: ， in, and It's a function that returns a vector, where b and beq are vector values, obtained through... Determine the relational expression The minimum x value of the objective function. As its upper and lower limits.

5. The energy management method for a hybrid power system based on a dual-stack fuel cell according to claim 4, characterized in that, The range of the state variables solved using the fmincon function is set as follows: ， Among them, P fc1 (t) and P fc2 (t) represents the output power of fuel cell 1 and fuel cell 2. and These represent the minimum and maximum output power of the lithium battery, respectively. This design uses a fuel cell with the same output power. and These represent the minimum and maximum output power of the fuel cell, respectively. and This indicates the minimum and maximum current values ​​during the charging and discharging of a lithium battery.

6. The energy management method for a hybrid power system based on a dual-stack fuel cell according to claim 1, characterized in that, Step 3 specifically includes the following steps: Step (α): Calculate the power required for the vehicle to run based on the driving conditions. And set the initial SoC of the lithium battery for simulation calculation; Step (β): Obtain the control quantity through model calculation based on ECMS strategy. and current lithium battery SoCs; Step (γ): Return the state variable SoC to calculate the new control variable; Step (δ): Repeat steps (α) to (γ) to obtain the fuel cell output power distribution at the lowest operating cost according to the setting of the multi-objective function.

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