A method and system for optimizing the power source of fuel cell hybrid electric heavy-duty trucks

Abstract

This invention discloses a method and system for optimizing the power source design of a fuel cell hybrid electric heavy-duty truck. The method includes: constructing an objective function for the total cost per unit mileage over the lifespan of the fuel cell, power battery, and supercapacitor, based on the fundamental parameters of the fuel cell, power battery, and supercapacitor, as well as the degradation parameters and hydrogen consumption of the fuel cell and power battery; establishing an energy management strategy for the fuel cell hybrid electric heavy-duty truck based on the vehicle's average power demand, the state of charge (SOC) of the power battery, and the supercapacitor; and employing a cancer cell competition and metastasis algorithm to find the objective function that minimizes the operating conditions under the energy management strategy, thereby achieving energy size optimization. This invention, by employing a cancer cell competition and metastasis algorithm for global optimization design of the fuel cell hybrid electric heavy-duty truck's power source, extends the lifespan of the power source while ensuring vehicle power performance, significantly reduces the manufacturing cost, degradation cost, and hydrogen consumption of the hybrid system, and improves the system's energy efficiency.

Description

Technical Field

[0001] This invention relates to the field of energy management, specifically to a method and system for optimizing the power source of a fuel cell hybrid electric heavy-duty truck. Background Technology

[0002] Electrification is a necessary path for decarbonization in the transportation industry. Currently, the electrification technologies for heavy-duty trucks mainly fall into two categories: lithium-ion batteries and fuel cells (FC). To achieve a 500km range, a 49-ton pure electric heavy-duty truck requires a 1000kWh lithium-ion battery, weighing approximately 5 tons, which significantly impacts the truck's cargo-carrying capacity. Compared to pure lithium-ion heavy-duty trucks, fuel cells can provide a longer range with a similar mass to an internal combustion engine, making them more suitable for long-distance transportation. Furthermore, fuel cell refueling takes relatively little time, only a few minutes, while lithium-ion batteries have a long charging time, affecting logistics efficiency. Therefore, fuel cell systems are more suitable as a power source for heavy-duty trucks. Compared to traditional gasoline vehicles, the energy density of hydrogen used in fuel cells is approximately three times that of gasoline, and the emissions are primarily water vapor, significantly reducing emissions of atmospheric gases and other pollutants, thus mitigating climate change.

[0003] Fuel cell vehicles can be broadly categorized into two types based on their energy source: all-fuel cell vehicles (AFCVs) and fuel cell hybrid vehicles (FCHVs). The former is powered entirely by the fuel cell, while the latter is powered by both the fuel cell and an energy storage system. Energy storage systems typically consist of a battery (B), a supercapacitor (SC), or a hybrid energy storage system (HESS) combining both. All-fuel cell vehicles have a slower response to instantaneous power demands, impacting vehicle start-up and acceleration performance. For heavy-duty trucks operating under high loads for extended periods, a single fuel cell system faces severe temperature management and lifespan degradation issues. Furthermore, fuel cell systems cannot recover braking energy, resulting in lower overall vehicle energy efficiency. Therefore, to improve the instantaneous power response, extend the lifespan, and recover braking energy in fuel cell vehicles, a fuel cell hybrid system is an excellent choice.

[0004] The size of the power source in a fuel cell hybrid electric vehicle (FC+B) has a significant impact on the initial cost, operating cost, power source lifespan, and energy management. Many scholars have conducted extensive research on the size design of FC+B FC+B vehicles, primarily categorizing them into two types: FC+B and FC+B+SC. Min-Joong Kim et al. were the first to study the optimal size of the FC+B power source, proposing a subsystem scaling model and a parameterizable power management strategy, which were incorporated as design variables into system optimization. Simulations showed that this method can provide good fuel economy. Furthermore, other researchers have proposed an optimal size method for plug-in FC+B driven buses. In this work, L. Xu et al. proposed a theoretical model to describe the relationship between component parameters and vehicle performance, employing a charge-depleting and charge-sustaining (CDCS) strategy for energy management, and using contour plots to optimize energy source size to reduce system and operating costs. None of the above studies on FC+B system size optimization considered system lifespan. Summary of the Invention

[0005] To address the technical problems mentioned above, this invention proposes a new energy size optimization method for fuel cell hybrid heavy-duty tractors based on the characteristics of three power sources.

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

[0007] A method for optimizing the power source design of a fuel cell hybrid electric heavy-duty truck, comprising the following steps:

[0008] Based on the fundamental parameters of fuel cells, power batteries, and supercapacitors, as well as the degradation parameters and hydrogen consumption of fuel cells and power batteries, an objective function for the total cost per unit mileage over the life cycle is constructed.

[0009] An energy management strategy for fuel cell hybrid electric heavy-duty trucks is established based on the vehicle's average power demand, the SOC of the power battery and supercapacitor.

[0010] Using a cancer cell competition and metastasis algorithm, under the energy management strategy, the objective function that minimizes the energy size under driving conditions is found, thus achieving energy size optimization.

[0011] Preferably, the constructed objective function includes:

[0012]

[0013] Cost in =N fc ·c fc +N bs ·Nbp ·c b +N scs ·N scp ·c sc

[0014] N cycle =(N fc ·c fc +N bs ·N bp ·c b ) / (cost fcdeg +cost bdeg )

[0015] Cost fcdeg =(D fc / 0.1)·N fc ·c fc

[0016] Cost bdeg =(Q b_loss / 0.2)·N bs ·N bp ·c b

[0017] X total =N cycle ·x cycle

[0018] Cost H2 =cost H2 ·N cycle

[0019] in, Indicates every 10 years within the lifespan. 4 Total cost consumed per km; Cost in Cost represents the initial cost. H2 Indicates the cost of hydrogen usage over its lifespan; X total N represents the total mileage during the vehicle's lifespan. fc Indicates the number of cells in a fuel cell; c fc N represents the cost per cell of a fuel cell; bs and N bp These represent the number of batteries connected in series and in parallel, respectively; c b Indicates the cost per cell; N scs and N scp These represent the number of supercapacitors connected in series and in parallel, respectively; c sc Cost indicates the cost of a single supercapacitor. fcdeg and cost bdeg N represents the degradation cost of the fuel cell and battery respectively in a single cycle; cycleThe number of cycles representing the total mileage traveled; x cycle Indicates the distance traveled in a single cycle; cost H2 This is the hydrogen consumption per cycle.

[0020] Preferably, the established energy management strategy includes the power demand of fuel cells and hybrid energy storage:

[0021] P fc =αP avg +βP fc_Eff +(1-α-β)P fc_Max

[0022]

[0023] Among them, P avg P represents the average power demand over the total operating time from vehicle start-up to the current moment. fc_Eff P represents the power at the point of maximum efficiency of a fuel cell. fx_Max This represents the rated power of the fuel cell, where α represents P. avg The preceding dynamic coefficient; β is determined by the SOC of the battery and supercapacitor; P HESS P represents the power demand of a hybrid energy storage system. fc Indicates the output power of the fuel cell; η DC / DC P represents the efficiency of a DC-DC converter. DC_dem This indicates the power requirement of the DC bus.

[0024] Preferably, the cancer cell competition and metastasis algorithm includes two stages: early stage of cancer and late stage of cancer;

[0025] Early signs of cancer include:

[0026]

[0027] D = ln(n) + b

[0028]

[0029] w i ~N(0, UL)

[0030] In the formula, a represents the pre-factor that decreases from 0.1 to 0 as the number of iterations increases; b represents the variable that decreases from 1 to 0 as the number of iterations increases. and Let $\frac{i}{i}$ represent the positions of the i-th healthy cell at iterations $it$ and $it+1$, respectively, with the value gradually decreasing as the number of iterations increases; $k$ represents a random number between $-1$ and $1$; $meancell_H$ represents the average position of the healthy cells; $D$ represents the spatial dimension influence coefficient; and $w$ represents the position of the healthy cell. iThe zero-mean Gaussian noise vector representing the i-th healthy cell; the covariance is represented by U-L, where U = (U1, U2,...U n ) represents the peak value of all healthy cells; L = (L1, L2,...L n ) represents the minimum boundary of all healthy cells.

[0031] The expression for the advanced stage of cancer includes:

[0032] R1 = rand, H = 0.5

[0033]

[0034] In the formula, and respectively represent the positions of the i-th central cancer cell at the it-th and it+1-th iterations; represents the division and mutation behavior of cancer cells; H represents the metastasis probability; R1 is a random number on [0, 1]; when R1 < H, the cancer cells continue to proliferate and divide, otherwise the cancer cells spread to other places through blood vessels; represents the moving speed of the i-th central cancer cell at the it+1-th iteration; c represents a pre-factor that decreases from 0.3 to 0 as the number of iterations increases; rand is a random number on [0, 1].

[0035] The present invention also provides a fuel cell hybrid electric heavy truck power source optimization design system, which is used to implement the above method and includes: an objective function construction module, a vehicle energy management module, and a solution module;

[0036] The objective function construction module is used to construct an objective function of the total cost per unit driving mileage within the life cycle based on the basic parameters of the fuel cell, power battery, and super capacitor, the degradation parameters of the fuel cell and power battery, and the hydrogen consumption;

[0037] The energy management module is used to allocate the power of the fuel cell and the energy storage system based on the average demand power of the vehicle, the SOC of the power battery and the super capacitor, and perform charging and discharging according to the SOC, maximum charging power, and maximum discharging power of the power battery and the super capacitor;

[0038] The solution module is used to adopt the cancer cell competition and metastasis algorithm to find the minimum objective function that meets the driving conditions under the energy management strategy, and complete the energy size optimization.

[0039] Preferably, the constructed objective function includes:

[0040]

[0041] Cost in = Nfc ·c fc +N bs ·N bp ·c b +N scs ·N scp ·c sc

[0042] N cycle =(N fc ·c fc +N bs ·N bp ·c b ) / (cost fcdeg +cost bdeg )

[0043] cost fcdeg =(D fc / 0.1)·N fc ·c fc

[0044] cost bdeg =(Q b_loss / 0.2)·N bs ·N bp ·c b

[0045] X total =N cycle ·x cycle

[0046] Cost H2 =cost H2 ·N cycle

[0047] in, Indicates every 10 years within the lifespan. 4 Total cost consumed per km; Cost in Cost represents the initial cost. H2 Indicates the cost of hydrogen usage over its lifespan; X total N represents the total mileage during the vehicle's lifespan. fc Indicates the number of cells in a fuel cell; c fc N represents the cost per cell of a fuel cell; bs and N bp These represent the number of batteries connected in series and in parallel, respectively; c b Indicates the cost per cell; N scs and N scp These represent the number of supercapacitors connected in series and in parallel, respectively; c sc Cost indicates the cost of a single supercapacitor. fcdeg and cost bdegN represents the degradation cost of the fuel cell and battery respectively in a single cycle; cycle The number of cycles representing the total mileage traveled; x cycle Indicates the distance traveled in a single cycle; cost H2 This is the hydrogen consumption per cycle.

[0048] Preferably, the established energy management strategy includes the power demand of fuel cells and hybrid energy storage:

[0049] P fc =αP avg +βP fc_Eff +(1-α-β)P fc_Max

[0050]

[0051] Among them, P avg P represents the average power demand over the total operating time from vehicle start-up to the current moment. fc_Eff P represents the power at the point of maximum efficiency of a fuel cell. fx_Max This represents the rated power of the fuel cell, where α represents P. avg The preceding dynamic coefficient; β is determined by the SOC of the battery and supercapacitor; P HEsS P represents the power demand of a hybrid energy storage system. fc Indicates the output power of the fuel cell; η DC / DC P represents the efficiency of a DC-DC converter. DC_dem This indicates the power requirement of the DC bus.

[0052] Preferably, the cancer cell competition and metastasis algorithm includes two stages: early stage of cancer and late stage of cancer;

[0053] Early signs of cancer include:

[0054]

[0055]

[0056] D = ln(n) + b

[0057]

[0058] w i ~N(0, UL)

[0059] In the formula, a represents the pre-factor that decreases from 0.1 to 0 as the number of iterations increases; b represents the variable that decreases from 1 to 0 as the number of iterations increases. and respectively represent the positions of the i-th healthy cell at the it-th and it+1-th iterations, and their values gradually decrease as the number of iterations increases; k represents a random number between [-1, 1]; meancell_H represents the average value of the positions of healthy cells; D represents the spatial dimension influence coefficient; w i represents the zero-mean Gaussian noise vector of the i-th healthy cell; the covariance represents U-L, where U = (U1, U2,... U n ) represents the peak values of all healthy cells; L = (L1, L2,... L n ) represents the minimum boundaries of all healthy cells.

[0060] The expression for the late stage of cancer includes:

[0061] R1 = rand, H = 0.5

[0062]

[0063] In the formula, and respectively represent the positions of the i-th central cancer cell at the it-th and it+1-th iterations; represents the division and mutation behavior of cancer cells; H represents the metastasis probability; R1 is a random number on [0, 1]; when R1 < H, the cancer cells continue to proliferate and divide, otherwise the cancer cells spread to other places through blood vessels; represents the moving speed of the i-th central cancer cell at the it+1-th iteration; c represents a pre-factor that decreases from 0.3 to 0 as the number of iterations increases; rand is a random number on [0, 1].

[0064] Compared with the existing technology, the beneficial effects of the present invention are as follows:

[0065] By adopting the cancer cell competition and metastasis algorithm to globally optimize the design of the power source of a fuel cell hybrid electric heavy truck, the present invention prolongs the service life of the power source while ensuring the vehicle's power performance, significantly reduces the manufacturing cost, degradation cost and hydrogen consumption of the hybrid power system, improves the energy efficiency of the system, and provides an economical, environmentally friendly and efficient solution for the electrification of heavy trucks. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the technical solutions of the present invention, the following briefly introduces the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0067] Figure 1 is a schematic diagram of the method flow of the present invention;

[0068] Figure 2 This is a schematic diagram of the operating condition curves of CHTC-TT-400 according to an embodiment of the present invention;

[0069] Figure 3 This is a schematic diagram of the vehicle's power demand under cyclic operating conditions according to an embodiment of the present invention; wherein, (a) represents the CHTC-TT-400 cyclic operating condition; (b) represents the power demand of a fully loaded vehicle; and (c) represents the power demand of an unloaded vehicle.

[0070] Figure 4 This is a schematic diagram of the energy management strategy according to an embodiment of the present invention;

[0071] Figure 5 This is a schematic diagram of the algorithm pseudocode in an embodiment of the present invention;

[0072] Figure 6 This is a schematic diagram of the simulation results of an embodiment of the present invention; wherein, (a) represents 10 4 (a) represents the total cost of system consumption; (b) represents the total mileage driven over the life cycle. Detailed Implementation

[0073] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0074] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0075] Before proceeding with the explanation, the technical background of this invention will be briefly described.

[0076] Currently, commercially available fuel cell tractor trucks primarily use the FC+B (fuel cell + battery) configuration. However, to meet the high power energy demands of vehicles and the need for instantaneous high-power braking energy recovery, the FC+B scheme requires more lithium batteries for compensation, increasing the weight of the powertrain, affecting vehicle power, increasing system costs, and limiting vehicle performance. While supercapacitors have lower energy density, they have higher power density, making them a good supplement to FC+B. This embodiment adopts a hybrid scheme of FC+B+SC (fuel cell + battery + supercapacitor).

[0077] Fuel cell hybrid power systems are divided into active and passive solutions. Compared to passive solutions, active solutions, while increasing system costs due to their DC / DC converters, can better handle complex and dynamic driving conditions, maintain system stability and performance, and achieve dynamic energy management, automatically adjusting the output of each energy source and optimizing energy distribution to meet changing load demands.

[0078] Example 1

[0079] like Figure 1 The diagram shown is a schematic representation of the method flow in this embodiment, and the steps include:

[0080] S1. Based on the fundamental parameters of fuel cells, power batteries, and supercapacitors, as well as the degradation parameters and hydrogen consumption of fuel cells and power batteries, construct an objective function for the total cost per unit mileage over the life cycle.

[0081] In this embodiment, the basic parameters of the hybrid power system include: the unit manufacturing cost of the fuel cell, the power battery and the supercapacitor, the life cost of the fuel cell and the power battery, the hydrogen consumption cost and the driving range per cycle.

[0082] Since heavy trucks often undertake long-distance transportation tasks, this embodiment, in order to simulate the long-distance transportation situation of heavy trucks, uses the CHTC-TT (China Semi-Trailer Tractor Cyclic Operating Condition) as a basis, repeating the portion above 80km / h to construct a cyclic operating condition with a total length of 400km, called CHTC-TT-400, with a total operating condition duration of 17750s. CHTC-TT includes two speed ranges, with a total operating condition duration of 1800s. The low-speed range lasts 473s, accounting for 26.3% of the total time, and the high-speed range lasts 1327s, accounting for 73.7% of the total time. The average vehicle speed is 46.6km / h, the maximum vehicle speed is 88.0km / h, and the idling rate is 8.6%. Figure 2 The CHTC-TT operating condition curves are shown.

[0083] Meanwhile, in order to indicate the operating status of the tractor unit when fully loaded and unloaded, the tractor unit will be operated in the CHTC-TT-400 state when fully loaded and unloaded respectively, with a total mass of 49,000 kg when fully loaded and a total mass of 11,500 kg when unloaded. Figure 3 (a) shows the velocity-time image of the CHTC-TT-400. Figure 3 (b) and (c) show the changes in DC bus power and average power demand over time when the vehicle is fully loaded and unloaded, respectively.

[0084] To address the issue of inconsistent scales and orders of magnitude differences among various optimization objectives (including initial cost, fuel cell degradation, battery degradation, and hydrogen consumption), this embodiment analyzes the optimal energy-scale Pareto front obtained under the same energy management and optimization algorithms. It finds that as the initial system cost increases, the system lifetime increases accordingly, and the cycle hydrogen consumption decreases accordingly. To simultaneously consider these optimization objectives, this embodiment unifies them to a unit mileage (10... 4 The comprehensive cost of km is shown in Equation (1), avoiding the setting of weights. For every 10 years of lifespan 4 Total cost consumed per km; Cost in Cost is the initial cost. H2 X represents the total cost of hydrogen consumed over its lifespan; total Total mileage driven over its lifespan. Cost in X is calculated from the energy dimensions, as shown in equation (2). total The number of cycles N within the lifespan cycle and the mileage of a single cycle x cycle The decision is made and the calculations are as shown in equations (3) to (6). Equation (3) calculates the number of cycles N of the system within its lifespan based on the degradation costs of FC and B under each cycle condition (calculated using equations (4) and (5)). cycle x cycle =800km. Cost H2 By N cycle and the cost of hydrogen consumption per cycle H2 The decision is made and the calculation is as shown in equation (7).

[0085]

[0086] Cost in =N fc ·c fc +N bs ·N bp ·c b +N scs ·N scp ·c sc (2)

[0087] N cycle =(N fc ·c fc +N bs ·N bp ·c b ) / (cost fcdeg +cost bdeg (3)

[0088] cost fcdeg=(D fc / 0.1)·N fc ·c fc (4)

[0089] cost bdeg =(Q b_loss / 0.2)·N bs ·N bp ·c b (5)

[0090] X total =N cycle ·x cycle (6)

[0091] Cost H2 =cost H2 ·N cycle (7)

[0092] Where, N fc Indicates the number of cells in a fuel cell; c fc N represents the cost per cell of a fuel cell; bs and N bp These represent the number of batteries connected in series and in parallel, respectively; c b Indicates the cost per cell; N scs and N scp These represent the number of supercapacitors connected in series and in parallel, respectively; c sc Cost indicates the cost of a single supercapacitor. fcdeg and cost bdeg These represent the degradation costs of fuel cells and batteries in a single cycle, respectively.

[0093] S2. Based on the average power demand of the vehicle, the SOC of the power battery and the supercapacitor, establish an energy management strategy (Average Power and SOC, APS) for fuel cell hybrid electric heavy-duty trucks.

[0094] The APS proposed in this embodiment takes into account the dynamic changes and rate of change of the output power of fuel cells and power batteries, avoids the waste of fuel cell power generation, and reduces the number of fuel cell start / stop cycles.

[0095] Specifically, this embodiment proposes a method for calculating fuel cell power based on average power demand and the SOC state of the energy storage system, as shown in equation (8). This method introduces the vehicle's average power demand P... avg The highest efficiency point power P of fuel cells fc_Eff and maximum power P fc_Max This can both avoid drastic changes in the output power of fuel cells and allow P to... fc_EffThe coefficient β increases with the increase of the SOC of the hybrid energy storage system. When the SOC of the energy storage system increases, the output power of the FC decreases and moves towards the point of maximum efficiency; conversely, when the SOC of the system decreases, the output power of the FC (fuel cell) increases and moves towards P. fc_Max The mobility of the fuel cell ensures that the energy storage system does not require additional charging, thus meeting the high power demands of the vehicle. Variations in fuel cell output power not only prevent the underutilization of fuel cell power when operating at a constant power, but also ensure a gradual change in fuel cell output power, reducing fuel cell degradation.

[0096] The output power P of the fuel cell fc The calculation is as follows:

[0097] P fc =aP avg +βP fc_Eff +(1-α-β)P fc_Max (8)

[0098] Among them, P avg P represents the vehicle's average power demand; Δt represents the total running time from when the vehicle started to the present; fc_Eff and P fc_Max These represent the power at maximum efficiency and the maximum power of the fuel cell, respectively; α represents P. avg The preceding dynamic coefficient; the value of β is determined by the SOC of the battery and supercapacitor, and is calculated as follows:

[0099]

[0100] Among them, E b and E sc These represent the maximum usable energy that batteries and supercapacitors can store, respectively. and Let B (battery) and SC (supercapacitor) represent the highest and lowest SOC values, respectively; σ represents the pre-correction factor to ensure α+β∈[0,1], calculated as follows:

[0101]

[0102] Therefore, the power demand P of the vehicle's hybrid energy storage system (HESS) HESS for:

[0103]

[0104] Where, η DC / DC This indicates the efficiency of the DC-DC converter, set to 0.95; if P HESS If the value is greater than 0, HESS discharges; otherwise, HESS charges. DC_demThis indicates the power requirement of the DC bus.

[0105] During discharge, HESS (Hypercapable Series-Emitting Capacitors) are primarily used to provide peak power due to their high power density and low energy density. When SOC (State of Charge) is reached... sc When the value is greater than 0.4, the supercapacitor prioritizes outputting electrical energy, and the maximum output power is P. sc_disMax The portion of the output power exceeding the maximum is supplied by the supercapacitor; otherwise, the battery prioritizes outputting electrical energy. The maximum output power is P. b_disMax The portion exceeding the maximum output power is supplied by the supercapacitor, maintaining its SOC value at 0.4 to ensure sufficient margin for absorbing the high-power energy generated during braking. During HESS charging, because the battery can store more energy than the supercapacitor, it is charged preferentially within the battery's permissible SOC range, with a maximum charging power of P. b_chaMax The portion of the power exceeding the battery's maximum charging power is allocated to the supercapacitor, ensuring that the system's power is not wasted.

[0106] In APS, the SOC value of a supercapacitor is... sc The range is set to [0.05, 1] ​​to avoid low voltage drop in the DC-DC converter. Overcharging, over-discharging, and fast charging often cause irreversible damage and severe lifespan degradation to batteries; therefore, the SOC value of the battery is important. b The range is set to [0.2, 0.9], and the maximum charging rate of the battery is set to 0.1C. Figure 4 Energy management strategies were demonstrated.

[0107] Therefore, during the operation of APS, the system needs to meet the following constraints:

[0108] P xmin ≤P x ≤P xmax (13)

[0109] V xmin ≤V x ≤V xmax (14)

[0110] I xmin ≤I x ≤I xmax (15)

[0111] SOC ymin ≤SOC y ≤SOC ymax (16)

[0112] ΔI fcmin ≤ΔI fc ≤ΔIfcmax (17)

[0113] V DCmin ≤V DC ≤V DCmax (18)

[0114] Where x represents any one of FC, B, or SC; y represents any one of B and SC; V DC This represents the voltage of the DC-DC converter; ΔI fc The current change rate of FC is represented by ΔI, which is specified in the embodiment. fcmin = -3A / s, ΔI fcmax =3A / s.

[0115] S3. Employing a cancer cell competition and metastasis algorithm, under an energy management strategy, we seek the objective function that minimizes the energy size under driving conditions.

[0116] Optimization algorithms are crucial in engineering problems requiring the search for optimal solutions, directly impacting the quality of the results. Researchers typically employ Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Genetic Algorithm (GA) to find optimal solutions. PSO simulates the predation behavior of bird flocks; while computationally fast and easy to implement, it is prone to getting trapped in local optima, unsuitable for high-dimensional problems, and prone to premature convergence. Grey Wolf Optimizer simulates the hunting behavior of a wolf pack; its ability to search for global optima is relatively weak, and it easily gets trapped in local optima when dealing with complex problems. While Genetic Algorithms have good search capabilities for high-dimensional problems, they are computationally intensive, require parameter tuning, and have slow convergence speeds. To address these issues, this embodiment proposes a novel population position update strategy to improve the ability to search for optimal solutions. This position update strategy is very similar to the competition between cancer cells and surrounding healthy cells during cancer evolution, as well as the metastasis and mutation behavior of cancer cells; therefore, the algorithm is named the Cancer Cell Competition and Metastasis Algorithm (C3TA).

[0117] The algorithm mainly consists of two stages: (1) In the early stage of cancer, the cancer cells at the primary site are dividing and proliferating and competing with the surrounding healthy cells for nutrient resources. As the healthy cells do not get enough nutrition, they gradually become disadvantaged and are engulfed by the cancer cells. The cancer cells occupy the positions of the healthy cells, thereby causing the cancer tumor to expand further. (2) In the later stage of cancer, the number of cancer cells at the primary site has accumulated in large quantities. While the cancer cells are growing, some cancer cells begin to metastasize to other parts of the body through blood vessels, thus achieving the spread of cancer.

[0118] (1) Initialize population position and displacement velocity

[0119] For an n-dimensional optimization problem, the position of an individual in the population is cell = (x1, x2, ..., x...). n In the optimization process, the total number of individuals in the population is set to N, then the population C = {cell1, cell2, ..., cell3} N First, the positions of individuals in the population are initialized using a random method:

[0120] cell i =l b +rand(1,n)·(u b -l b )(i=1, 2, 3,...,N) (19)

[0121] Among them, l b Indicates the lower boundary; u b The upper boundary is indicated; rand(1, n)[0, 1] represents a random number in row n.

[0122] For the results of random initialization, an elite reverse learning strategy is used to improve population diversity:

[0123] cell i =δ·(u b +l b )-cell i (20)

[0124]

[0125] Where δ represents a random number in [0, 1]; fit(cell i ) and fit(cell) i ) represent cells respectively i and cell i The fitness, i.e. the result of the cost function calculation, is determined by the algorithm's objective of minimizing fitness, thus preserving cell positions with low fitness.

[0126] The range of individual movement speeds within a population is defined based on the upper boundary of their location:

[0127] u v =0.3·u b (twenty two)

[0128] l v =-0.3·u b (twenty three)

[0129] In the formula, l v and u v These represent the lower and upper boundaries of an individual's movement speed, respectively.

[0130] Individual velocity is initialized as follows:

[0131] V i =l u +rand(1,n)·(u v -l v )(i=1, 2, 3,...,N) (24)

[0132] (2) Stage 1 Early stage of cancer

[0133] At this stage, since the number of cancer cells is still relatively small, it is assumed that the top 20% of individuals in terms of fitness are healthy cells, 20%–40% are cancer cells in contact with healthy cells (called borderline cancer cells), and the remaining are cancer cells inside the tumor (called central cancer cells). To determine the number of these three types of cells, let S1 = 0.2. Then the number of healthy cells is:

[0134] S 1_Num =round(S1·N) (25)

[0135] In the formula, round represents the rounding operation.

[0136] At this stage, healthy cells (1≤i≤S) 1_Num Cells are less threatened by cancer cells, and their position updates are mainly based on the average position of healthy cells, with minimal impact. The position update formula is:

[0137]

[0138]

[0139] D = ln(n) + b (29)

[0140]

[0141] w i ~N(0, UL) (31)

[0142] In the formula, 'a' is a pre-factor that decreases from 0.1 to 0 as the number of iterations increases; 'b' represents a variable that decreases from 1 to 0 as the number of iterations increases. and Let $\frac{i}{i}$ represent the positions of the i-th healthy cell at iterations $it$ and $it+1$, respectively, with the value gradually decreasing as the number of iterations increases; $k$ represents a random number between $-1$ and $1$; $meancell_H$ represents the average position of the healthy cells; $D$ represents the spatial dimension influence coefficient; and $w$ represents the position of the healthy cell. i The zero-mean Gaussian noise vector represents the i-th healthy cell; the covariance is represented by UL, where U = (U1, U2, ..., U...). n) represents the peak value of all healthy cells; L = (L1, L2, ... L n ) represents the minimum boundary of all healthy cells.

[0143] For borderline cancer cells (S1) Num <i≤2·S1 Num As healthy cells near it and it+1 compete for nutrients, they gradually engulf and occupy the positions of the healthy cells. Therefore, their positions are mainly influenced by nearby healthy cells, and the position update formula is:

[0144]

[0145] In the formula, and Let represent the positions of the i-th boundary cancer cell at the and iterations, respectively, and let their values ​​gradually decrease as the number of iterations increases; This represents the position of the healthy cell with the smallest Euclidean distance from the i-th boundary cancer cell.

[0146] For central cancer cells, the main behaviors at this stage are proliferation, division, and gene mutation. The adhesion between cancer cells weakens, leading to metastasis. The location update formula is:

[0147]

[0148]

[0149] In the formula, and Let represent the positions of the i-th central cancer cell at iterations it and it+1, respectively; and Let represent the movement speed of the i-th central cancer cell at iterations it and it+1, respectively; c represents the pre-factor that decreases from 0.3 to 0 as the number of iterations increases; rand is a random number in [0, 1]; Bestpos represents the position of the healthy cell with the lowest fitness. The division and mutation behavior of cancer cells is shown below:

[0150] idx = find(rand(1, n)) <mu) (36)

[0151] newcell = l b +rand(1,n)·(u b -l b (37)

[0152]

[0153] where mu = 0.5 is the mutation factor, and the indices of the numbers in rand(1, n) that are less than mu are found through the find function and stored in idx; newcell represents the position of the newly generated cell, and the number at the idx position in newcell is replaced and saved in the number at the idx position, and saved in it.

[0154] (3) Stage 2 advanced cancer

[0155] At this stage, a large number of cancer cells have accumulated at the primary tumor site. Therefore, the proportion of cancer cells in contact with healthy cells in the total number decreases. Assume that the individuals with the top 10% fitness rankings are healthy cells, 10% - 20% of the cancer cells are boundary cancer cells, and the remaining cancer cells are central cancer cells. To determine the number of the three types of cells, let S2 = 0.1, then the number of healthy cells is:

[0156] S 2_Num = found(S2·N) (40)

[0157] Due to the expansion of the number of cancer cells, at this stage, the positions of healthy cells (1 ≤ i ≤ S 2_Nu m) are near the positions of the healthy cells with the highest fitness in the initial stage. Therefore, the position update formula for healthy cells is:

[0158]

[0159] The position update of boundary cancer cells (S 2_Num <i ≤ 2·S 2_Num ) is the same as in the initial stage of cancer:

[0160]

[0161] Central cancer cells may continue to proliferate and divide at this stage, or may metastasize to other parts of the body through blood vessels, causing the spread of cancer. Its position update formula is:

[0162] R1 = rand, H = 0.5 (43)

[0163]

[0164] where H is the metastasis probability; R1 is a random number on [0, 1]. When R1 < H, the cancer cells continue to proliferate and divide, otherwise the cancer cells spread to other places through blood vessels. The pseudocode is as Figure 5 shown.

[0165] Example 2

[0166] This embodiment also provides a fuel cell hybrid electric heavy-duty truck power source optimization design system, including: an objective function construction module, an energy management module, and a solution module; the objective function construction module is used to construct an objective function for the total cost per unit mileage over the life cycle based on the basic parameters of the fuel cell, power battery, and supercapacitor, the degradation parameters of the fuel cell and power battery, and hydrogen consumption; the energy management module is used to allocate the power of the fuel cell and energy storage system based on the vehicle's average power demand, the SOC of the power battery and supercapacitor, and to perform charging and discharging according to the SOC, maximum charging power, and maximum discharging power of the power battery and supercapacitor; the solution module is used to find the minimum objective function that satisfies the driving conditions under the energy management strategy using a cancer cell competition and metastasis algorithm, thereby completing energy size optimization.

[0167] The objective functions constructed include:

[0168]

[0169] Cost in =N fc ·c fc +N bs ·N bp ·c b +N scs ·N scp ·c sc (46)

[0170] N cycle =(N fc ·c fc +N bs ·N bp ·c b ) / (cost fcdeg +cost bdeg (47)

[0171] cost fcdeg =(D fc / 0.1)·N fc ·c fc (48)

[0172] cost bdeg =(Q b_loss / 0.2)·N bs ·N bp ·c b (49)

[0173] X total =N cycle ·x cycle (50)

[0174] CostH2 =cost H2 ·N cycle (51)

[0175] in, Indicates every 10 years within the lifespan. 4 Total cost consumed per km; Cost in Cost represents the initial cost. H2 This represents the total cost of hydrogen consumed over its lifespan; X total N represents the total mileage during the vehicle's lifespan. fc Indicates the number of cells in a fuel cell; c fc N represents the cost per cell of a fuel cell; bs and N bp These represent the number of batteries connected in series and in parallel, respectively; c b Indicates the cost per cell; N scs and N scp These represent the number of supercapacitors connected in series and in parallel, respectively; c sc Cost indicates the cost of a single supercapacitor. fcdeg and cost bdeg These represent the degradation costs of the fuel cell and battery respectively during a single cycle; cost H2 This is the hydrogen consumption per cycle. The established power demand relationship includes:

[0176]

[0177] Among them, P HESS P represents the power demand of the vehicle's hybrid energy storage system. fc Indicates the output power of the fuel cell; η DC / DC This indicates the efficiency of the DC-DC converter, set to 0.95; if P HESS If the value is greater than 0, HESS discharges; otherwise, HESS charges. DC_dem This indicates the rated power requirement of the DC bus.

[0178] The cancer cell competition and metastasis algorithm consists of two phases: early stage of cancer and late stage of cancer.

[0179] Early signs of cancer include:

[0180]

[0181] D = ln(n) + b(56)

[0182]

[0183] w i ~N(0, UL)(58)

[0184] Where, a represents a pre-factor that decreases from 0.1 to 0 as the number of iterations increases; b represents a variable that decreases from 1 to 0 as the number of iterations increases; and respectively represent the positions of the i-th healthy cell at the it-th and it+1-th iterations, and their values gradually decrease as the number of iterations increases; k represents a random number between [-1, 1]; meancell_H represents the average value of the positions of healthy cells; D represents the spatial dimension influence coefficient; w i represents the zero-mean Gaussian noise vector of the i-th healthy cell; the covariance represents U-L, where U = (U1, U2,... U n ) represents the peak values of all healthy cells; L = (L1, L2,... L n ) represents the minimum boundaries of all healthy cells.

[0185] The expressions in the late stage of cancer include:

[0186] R1 = rand, H = 0.5 (59)

[0187]

[0188] Where, and respectively represent the positions of the i-th central cancer cell at the it-th and it+1-th iterations; represents the division and mutation behavior of cancer cells; H represents the metastasis probability; R1 is a random number on [0, 1]; when R1 < H, cancer cells continue to proliferate and divide, otherwise cancer cells spread to other places through blood vessels; represents the moving speed of the i-th central cancer cell at the it+1-th iteration; c represents a pre-factor that decreases from 0.3 to 0 as the number of iterations increases; rand is a random number on [0, 1].

[0189] Example 3

[0190] This example provides a simulation experiment to verify the advancement of the present invention.

[0191] In this example, fuel cells, batteries, and supercapacitors are all taken as discrete units in monomer form. Referring to the power of currently commercial fuel cell tractors, the minimum value of the fuel cell unit in this example is set to 30 (180 kW), and due to the constraint of the internal space of the tractor, the upper limit of the number of fuel cell units is 60 (360 kW). To avoid too low voltage at the low-voltage end of the DC converter, the minimum values of the series numbers of batteries and supercapacitors are both set to 10, the maximum value of the battery series number is set to 200, and the range of the battery parallel number is set the same as the series number. Considering the power output requirements of supercapacitors, the range of its parallel number is set to [4, 40]. Table 1 lists the setting of the simulation parameters in this example.

[0192] Table 1

[0193]

[0194] During the simulation, the optimization algorithm C3TA provided by this invention first generates a specific energy size, and then calculates the SOC based on the obtained energy size. b SOC sc P DC_dem and P avg The calculated parameters are used to calculate the output power of FC, B, and SC using the constructed APS, and then to calculate the degradation and hydrogen consumption of the vehicle's energy supply system. Finally, the target is optimized using C3TA based on the above calculation results. The C3TA algorithm updates the energy size according to the position update strategy and enters the next loop until the minimum value is retained after the iteration ends.

[0195] For a fuel cell tractor operating under the CHTC-TT-400 cycle condition, the optimal capacity configuration problem was studied under the APS strategy, and the optimal solution was found using the proposed C3TA algorithm. To demonstrate the performance of the proposed C3TA algorithm, we conducted a comparative analysis using the PSO and GWO algorithms. We also compared the performance of APS and Discrete Wavelet Transform (DWT) energy management strategies in optimizing the optimal capacity configuration of the hybrid power system, under the same parameters and constraints. Specifically, the DWT strategy allocates low-frequency, mid-frequency, and high-frequency power to the fuel cell, battery, and supercapacitor, respectively, based on the dynamic characteristics of the three power sources.

[0196] Table 2 shows the comprehensive optimization results of APS and DWT under the C3TA, PSO, and GWO algorithms, respectively, and... Figure 6 China's 10 4 A more intuitive comparison can be made between the overall consumption cost per km and the total mileage driven over the lifespan. The results are as follows: Figure 6 As shown, the hybrid power system employing the APS strategy has a 77% longer lifespan compared to DWT, and its lifespan increases by 77% every 10 years over its lifespan. 4 The total cost per km is reduced by 16% compared to DWT, meaning that under the same operating conditions, the APS energy management strategy will reduce lifespan degradation and lower the overall cost per unit mileage. Meanwhile, comparing the optimization results of different algorithms under the same energy management strategy, it was found that the C3TA algorithm proposed in this invention achieves higher efficiency under APS. Obtained under DWT Comparing the results obtained from the PSO and GWO algorithms, it is clear that C3TA achieves the minimum optimization objective value under both energy management strategies.

[0197] Table 2

[0198]

[0199] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for optimizing the power source design of a fuel cell hybrid electric heavy-duty truck, characterized by the following steps: include: Based on the fundamental parameters of fuel cells, power batteries, and supercapacitors, as well as the degradation parameters and hydrogen consumption of fuel cells and power batteries, an objective function for the total cost per unit mileage over the life cycle is constructed. Based on the vehicle's average power demand, the SOC of the power battery and supercapacitor, an energy management strategy for fuel cell hybrid electric heavy-duty trucks is established. Using a cancer cell competition and metastasis algorithm, under the energy management strategy, we find the objective function that minimizes the driving conditions to achieve energy size optimization; The cancer cell competition and metastasis algorithm comprises two phases: early stage of cancer and late stage of cancer. Early signs of cancer include: In the formula, a represents the pre-factor that decreases from 0.1 to 0 as the number of iterations increases; b represents the variable that decreases from 1 to 0 as the number of iterations increases; and They represent the first i A healthy cell it and it The position at iteration +1 gradually decreases as the number of iterations increases; k Represents a random number between [-1, 1]; This represents the average position of healthy cells; D Indicates the influence coefficient of spatial dimension; Indicates the first i Zero-mean Gaussian noise vector of healthy cells; covariance representation ,in This represents the peak value of all healthy cells; Represents the minimum boundary of all healthy cells; Expressions indicating late-stage cancer include: In the formula, and They represent the first i A central cancer cell it and it+ The position at the first iteration; This indicates the division and mutation behavior of cancer cells; H Indicates the transition probability; R 1 is a random number on [0, 1]; when At this stage, cancer cells continue to proliferate and divide; conversely, cancer cells can spread to other places through blood vessels. Indicates the first i A central cancer cell in it +1 iteration speed; c represents the pre-factor that decreases from 0.3 to 0 as the number of iterations increases; rand It is a random number in [0, 1].

2. The fuel cell hybrid electric heavy-duty truck power source optimization design method according to claim 1, characterized in that, The constructed objective function includes: in, This represents the total cost consumed per 104 km over the lifespan. Indicates the initial cost; This indicates the cost of using hydrogen over its lifespan. This indicates the total mileage driven over the lifespan of the vehicle. Indicates the number of cells in a fuel cell; This indicates the cost per cell of a fuel cell; and These represent the number of batteries connected in series and in parallel, respectively. Indicates the cost per battery; and These represent the number of supercapacitors connected in series and in parallel, respectively. This indicates the cost of a single supercapacitor. and These represent the degradation costs of fuel cells and batteries in a single cycle, respectively. The number of cycles representing the total mileage traveled; Indicates the distance traveled in a single cycle; This is the hydrogen consumption per cycle.

3. The fuel cell hybrid electric heavy-duty truck power source optimization design method according to claim 1, characterized in that, The established energy management strategy includes the power demand of fuel cells and hybrid energy storage: in, Pavg This represents the average power demand over the total operating time from vehicle startup to the current moment. This indicates the power output at the point of maximum efficiency of the fuel cell; Indicates the rated power of the fuel cell. express The preceding dynamic coefficient; Determined by the SOC of the battery and supercapacitor; This indicates the power demand of the hybrid energy storage system; This indicates the output power of the fuel cell; Indicates the efficiency of a DC-DC converter. This indicates the power requirement of the DC bus.

4. A fuel cell hybrid electric heavy-duty truck power source optimization design system, said system being used to implement the method described in any one of claims 1-3, characterized in that, include: Objective function construction module, vehicle energy management module, and solution module; The objective function construction module is used to construct an objective function for the total cost per unit mileage within the life cycle based on the basic parameters of fuel cells, power batteries, and supercapacitors, as well as the degradation parameters and hydrogen consumption of fuel cells and power batteries. The energy management module is used to allocate power to the fuel cell and energy storage system based on the vehicle's average power demand, the SOC of the power battery and supercapacitor, and to charge and discharge the power battery and supercapacitor according to their SOC, maximum charging power and maximum discharging power. The solution module is used to employ a cancer cell competition and metastasis algorithm to find the objective function that minimizes the driving conditions under the energy management strategy, thereby completing energy size optimization.

5. The fuel cell hybrid electric heavy-duty truck power source optimization design system according to claim 4, characterized in that, The constructed objective function includes: in, This represents the total cost consumed per 104 km over the lifespan. Indicates the initial cost; This indicates the cost of using hydrogen over its lifespan. This indicates the total mileage driven over the lifespan of the vehicle. Indicates the number of cells in a fuel cell; This indicates the cost per cell of a fuel cell; and These represent the number of batteries connected in series and in parallel, respectively. Indicates the cost per battery; and These represent the number of supercapacitors connected in series and in parallel, respectively. This indicates the cost of a single supercapacitor. and These represent the degradation costs of fuel cells and batteries in a single cycle, respectively. The number of cycles representing the total mileage traveled; Indicates the distance traveled in a single cycle; This is the hydrogen consumption per cycle.

6. The fuel cell hybrid electric heavy-duty truck power source optimization design system according to claim 4, characterized in that, The established energy management strategy includes the power demand of fuel cells and hybrid energy storage: in, Pavg This represents the average power demand over the total operating time from vehicle startup to the current moment. This indicates the power output at the point of maximum efficiency of the fuel cell; Indicates the rated power of the fuel cell. express The preceding dynamic coefficient; Determined by the SOC of the battery and supercapacitor; This indicates the power demand of the hybrid energy storage system; This indicates the output power of the fuel cell; Indicates the efficiency of a DC-DC converter. This indicates the power requirement of the DC bus.

7. The fuel cell hybrid electric heavy-duty truck power source optimization design system according to claim 4, characterized in that, The cancer cell competition and metastasis algorithm comprises two phases: early stage of cancer and late stage of cancer. Early signs of cancer include: In the formula, a represents the pre-factor that decreases from 0.1 to 0 as the number of iterations increases; b represents the variable that decreases from 1 to 0 as the number of iterations increases; and They represent the first i A healthy cell it and it The position at iteration +1 gradually decreases as the number of iterations increases; k Represents a random number between [-1, 1]; This represents the average position of healthy cells; D Indicates the influence coefficient of spatial dimension; Indicates the first i Zero-mean Gaussian noise vector of healthy cells; covariance representation ,in This represents the peak value of all healthy cells; Represents the minimum boundary of all healthy cells; Expressions indicating late-stage cancer include: In the formula, and They represent the first i A central cancer cell in it and it+ The position at the first iteration; This indicates the division and mutation behavior of cancer cells; H Indicates the transition probability; R 1 is a random number on [0, 1]; when At this stage, cancer cells continue to proliferate and divide; conversely, cancer cells can spread to other places through blood vessels. Indicates the first i A central cancer cell in it +1 iteration speed; c represents the pre-factor that decreases from 0.3 to 0 as the number of iterations increases; rand It is a random number in [0, 1].

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