Ship energy storage system power scheduling method considering state coupling characteristics

Abstract

The invention relates to the field of ship power control containing an energy storage system, in particular to a ship energy storage system power scheduling method considering state coupling characteristics, which comprises the following steps: S1, establishing a ship dynamic model of a ship sailing in a limited water area; s2, constructing a ship electric propulsion power model based on the ship dynamics model; s3, constructing a state coupling model in the ship navigation process based on the analysis of the state coupling characteristics of the energy storage system in the ship navigation process; s4, constructing a ship energy system power generation dispatching model based on the ship electric propulsion power model in combination with the charging and discharging characteristics of the energy storage system; and S5, calculating power demands of the battery and the super capacitor in the energy storage system based on the total power demand of the energy storage system and the battery dynamic power feasible range, and performing power scheduling between the battery and the super capacitor by taking minimization of a power error as a target in combination with a power balance equation. According to the invention, the reasonability of power distribution of the energy storage system and the stable operation and safety of the ship can be improved.

Description

Technical Field

[0001] This invention relates to the field of power control for ships with energy storage systems, and more specifically to a power scheduling method for ship energy storage systems that considers state coupling characteristics. Background Technology

[0002] With increasing global emphasis on environmental protection and the shipping industry's pursuit of sustainable development, all-electric ships (AES), as an emerging green shipping solution, have gradually become a hot topic in industry research and development. The DC-powered design of all-electric ships not only gives them excellent environmental friendliness but also endows them with efficient speed control capabilities. However, this new type of ship also faces many challenges in actual operation, especially compared to traditional ships, where the complexity and vulnerability of their electrical systems are more pronounced.

[0003] In actual operation, the electrical systems of all-electric ships need to cope with complex and uncertain navigation conditions. These conditions include, but are not limited to, changes in navigation resistance, fluctuations in power demand, and the special requirements of restricted waterways. In restricted waterways, the navigation space for ships is relatively small and may be restricted by bridges, port facilities, and other vessels. In such situations, ships need to frequently adjust their speed and direction, making power demand difficult to predict. For example, when a ship enters a port, it may need to suddenly decelerate or stop, while when leaving the port, it needs to accelerate rapidly. This frequent power variation places extremely high demands on the ship's electrical system.

[0004] Currently, the commonly used energy storage components in marine energy storage systems mainly include batteries and supercapacitors. Batteries have high energy density and can store large amounts of electrical energy, providing a long-term energy supply for ships, making them suitable for handling prolonged low-power operation. However, batteries have relatively low power density and slow charging and discharging speeds, making it difficult to respond quickly and provide sufficient power in the face of short-term high-power demands from ships, such as during acceleration or collision avoidance emergencies. Supercapacitors, on the other hand, have extremely high power density and rapid charging and discharging characteristics, capable of providing or absorbing large amounts of electrical energy instantaneously, making them ideal for meeting short-term high-power needs of ships. However, supercapacitors have lower energy density and cannot store large amounts of energy for extended periods like batteries.

[0005] Current power allocation in marine energy storage systems lacks comprehensive consideration of the characteristics of batteries and supercapacitors. It fails to allocate power between the two based on the ship's actual navigation conditions and the energy storage system's status, leading to overcharging and discharging of batteries under unsuitable conditions and shortened lifespan. Furthermore, supercapacitors may also fail to fully utilize their rapid response advantage due to unreasonable power allocation. Therefore, how to fully consider the characteristics of batteries and supercapacitors and achieve a scientific and rational power allocation between them in marine energy storage systems has become a critical issue that urgently needs to be addressed in the current marine energy field. Summary of the Invention

[0006] To address the shortcomings of the existing technologies, the technical problem to be solved by this invention is: how to provide a power scheduling method for ship energy storage systems that considers state coupling characteristics, calculates the power demand of batteries and supercapacitors in the energy storage system based on the total power demand of the energy storage system and the feasible range of battery dynamic power, and performs power scheduling between batteries and supercapacitors with the goal of minimizing power error in combination with the power balance equation, thereby improving the rationality of power allocation in the energy storage system, as well as the stable operation and safety of the ship.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0008] A power scheduling method for a ship energy storage system considering state coupling characteristics includes:

[0009] S1: Establish a ship dynamics model for navigation in a limited water area; the ship dynamics model is used to calculate the total resistance of the ship when navigating in shallow and narrow waters;

[0010] S2: Construct a ship electric propulsion power model based on the ship dynamics model; the ship electric propulsion power model is used to calculate the ship's electric propulsion power;

[0011] S3: Based on the analysis of the state coupling characteristics of the energy storage system during ship navigation, a state coupling model is constructed for the ship navigation process; the state coupling model is used to calculate the dynamic power feasible range of the battery;

[0012] S4: Based on the ship electric propulsion power model and the charging and discharging characteristics of the energy storage system, construct a ship energy system power generation scheduling model; solve the ship energy system power generation scheduling model to obtain the total power demand of the energy storage system;

[0013] S5: Calculate the power demand of batteries and supercapacitors in the energy storage system based on the total power demand of the energy storage system and the feasible range of battery dynamic power, and combine the power balance equation to perform power scheduling between batteries and supercapacitors with the goal of minimizing power error.

[0014] Preferably, in step S1, the ship dynamics model is represented as follows:

[0015] R shallow =R h +R wave +R wind ;

[0016] in:

[0017] R h =R v +R app +Rw +R b +R tr +R a ;

[0018]

[0019] S A =L pp (1.8d+C B B);

[0020] In the formula: R shallow R represents the total resistance a ship encounters while navigating in shallow, narrow waters. h R is the hydrostatic resistance; wave For wave drag; R wind For wind resistance; F r S represents the Froude number; A Indicates the wetted area of ​​a ship; ρ, U, g, C B These represent water density, sailing speed, gravitational acceleration, and arresting factor, respectively; H is wave height; C wind ρ air A T U wind These are drag coefficient, air density, frontal area, and wind speed, respectively; R v R app R w R b R tr R a These are viscous drag, attached drag, wave-breaking drag, additional drag at the bow, additional drag at the stern, and hull-related drag; R v R represents viscous resistance; f and R pv These are frictional resistance and viscous pressure resistance, respectively; C f and C pv These are the frictional resistance coefficient and the viscous pressure resistance coefficient, respectively; R e L is the Reynolds coefficient; k1 is the ship type coefficient; L pp d represents the length of the hull; d represents the draft of the hull; and B represents the width of the hull.

[0021] Preferably, in step S2, the ship's electric propulsion power model is constructed through the following steps:

[0022] S201: Constructing an electric propulsion model based on ship dynamics;

[0023] The electric propulsion model is represented as follows:

[0024]

[0025] In the formula: P shallowIndicates electric propulsion power; j, η m These represent the number and efficiency of the propulsion motors, respectively; K T and K Q These are the thrust and torque coefficients, respectively; J is the propulsion speed ratio; D r Indicates the propeller diameter;

[0026] S202: Reconstruct the ship dynamics-based electric propulsion model in step S201 into a data-driven ship electric propulsion power model;

[0027] The power model of ship electric propulsion is represented as follows:

[0028]

[0029] In the formula: P s ω represents the electric propulsion power; ω is the propeller speed. The propulsion speed is related to the actual propulsion performance of the propeller; w is the wake coefficient; a0, a1, a2, a 2+j ,a 2+2j Regression parameters based on deep neural networks.

[0030] Preferably, in step S3, the state coupling model during the ship's navigation process is represented as follows:

[0031]

[0032] In the formula: x represents the state variable; y represents the output variable; and diag is the diagonal matrix SOC. bat and SOC uc SOC of batteries and supercapacitors, respectively; I bat and I uc These are the battery current and the supercapacitor current, respectively; P bat and P uc These represent the outputs of the battery and the supercapacitor, respectively; ΔT is the control range; η bat and η uc Power efficiencies of batteries and supercapacitors, respectively; R0 and U. P These are the battery's ohmic resistance and polarization voltage, respectively. R uc C uc These represent the initial voltage, internal resistance, and initial capacitance of the supercapacitor, respectively; C bat , These are the battery's current capacity and initial capacity, respectively. and Θ represents the minimum and maximum power for charging or discharging the battery, respectively; Θ is the state coupling function.

[0033] Preferably, in step S4, the feasible range of dynamic power of the battery is calculated through the following steps:

[0034] S401: Identify the initial internal parameters of the battery using a recursive least squares algorithm;

[0035] S402: Estimate the battery's state of charge (SOC) using a dynamic output model derived from a minimum error optimization method. bat ;

[0036] S403: Estimate the state of health (SOH) of the battery by considering a capacity degradation model that takes into account dynamic voltage decay;

[0037] S404: The battery's state of charge (SOC) is combined with the following formula in the state-coupled model. bat Based on the State of Health (SOH), calculate the feasible range of dynamic power for the battery:

[0038]

[0039] In the formula: This indicates the feasible range of dynamic power for the battery. and These are the minimum and maximum power for charging or discharging the battery, respectively; SOC bat SOH represents the state of charge and state of health of the battery; Θ is the state coupling function.

[0040] Preferably, in step S402, the state of charge of the battery is estimated through the following steps:

[0041] S4021: Construct the following dynamic output model:

[0042]

[0043] In the formula: U bat Indicates the battery voltage; C p R p and I p These represent polarization capacity, polarization resistance, and polarization current, respectively; OCV represents the battery open-circuit voltage.

[0044] S4022: The SOC estimation process of the dynamic output model in step S4021 is modeled as the following minimum output error optimization problem:

[0045]

[0046] In the formula: To estimate the state variables; G v and G i These represent the output voltage error and the output current error, respectively; Q1, Q2, and R are the weights. The cost function is defined as follows: N is the runtime within each cycle.

[0047] S4023: Solve the minimum output error optimization problem in step S4022 to obtain... And extract the battery's state of charge (SOC) from it. bat .

[0048] Preferably, in step S403, the battery health status is estimated through the following steps:

[0049] S4031: Construct the following capacity degradation model:

[0050]

[0051] In the formula: The estimated initial capacity for period n; C r I is the current rate; r U is the standard current in 1C; p χ is the polarization voltage of the battery. n is the derivative of OCV; V is the measurement noise.

[0052] S4032: Convert the capacity degradation model in step S4031 into Formula 1;

[0053] Formula 1 is expressed as:

[0054]

[0055] In the formula: This represents the standard current in the j-th 1C with period n;

[0056] S4033: Solve Formula 1 above to obtain the battery's current capacity C. bat And through the formula in the state coupling model Calculate the battery's state of health (SOH).

[0057] Preferably, in step S4, the objective function of the ship energy system power generation scheduling model is expressed as:

[0058]

[0059] The constraints of the ship energy system power generation dispatch model are expressed as follows:

[0060]

[0061] In the formula: This represents the total power requirement of the energy storage system. Power requirements for marine main engines; DoD bat and W bat These represent the depth of discharge and energy of the battery, respectively; DoD uc and W ucf1 and f2 are the depth of discharge and energy of the supercapacitor, respectively; f2 = ±10⁻³ and f3 ±10⁻⁴ are the equivalent fuel consumption coefficients of the battery and the supercapacitor, respectively. The corresponding values ​​are negative when the energy storage system is discharging, and negative otherwise.

[0062] Preferably, in step S5, power dispatching between the battery and the supercapacitor in the energy storage system is achieved through the following steps:

[0063] S501: The power requirements of batteries and supercapacitors in the energy storage system are obtained by combining the total power requirements of the energy storage system with the feedforward method.

[0064] S502: Based on the feasible range of battery dynamic power, calculate the battery power error Δe using the following formula. bat ;

[0065]

[0066] In the formula: Indicates the battery's power requirement; P bat This indicates the battery's output power; SOP indicates the battery's dynamic power feasible range. These represent the minimum power for charging and discharging the battery, respectively; N bat Indicates the number of batteries;

[0067] S503: The power error Δe of the supercapacitor is calculated using the following formula. uc :

[0068]

[0069] In the formula: This indicates the power requirement of the supercapacitor; P uc This indicates the output power of the supercapacitor;

[0070] S504: Power error Δe via battery bat The power error Δe of the supercapacitor uc Calculate the total power error Δe of the energy storage system ess =Δe bat +Δe uc ;

[0071] S505: Introduces a self-learning controller to predict the state trajectory changes of the energy storage system and constructs a power balance equation, combined with the power feedforward method error Δe ess To achieve the goal of power scheduling between batteries and supercapacitors in an energy storage system, the optimal power allocation between batteries and supercapacitors in the energy storage system is obtained.

[0072] The power balance equation is expressed as follows:

[0073] Pess (k)=P uc (k)+P bat (k)+i ILC (k)U uc (k);

[0074] In the formula: P ess P represents the total power of the energy storage system. uc P represents the output power of the supercapacitor. bat Indicates the battery's output power; U uc This indicates the voltage of the supercapacitor;

[0075] in:

[0076]

[0077] In the formula: i ILC This represents the compensation current of the supercapacitor; G ILC Represents a self-learning controller; CR*G I T represents the transfer function of the current closed-loop controller; c K is the time constant; ILc The gain of the self-learning controller, including the gain of the energy storage system in buck and boost modes.

[0078] Preferably, in step S501, the power requirements of the battery and supercapacitor in the energy storage system are calculated using the following formula:

[0079]

[0080] In the formula: and The power requirements for batteries and supercapacitors are respectively; P ref For power feedforward; Rated DC voltage; K p K i These are the proportional and integral terms of the voltage controller, respectively; T s is the time constant.

[0081] Compared with existing technologies, the power scheduling method for ship energy storage systems that considers state coupling characteristics in this invention has the following advantages:

[0082] This invention establishes a ship dynamics model for navigation in confined waters and constructs a ship electric propulsion power model based on this model. By establishing a ship dynamics model for navigation in confined waters, the total resistance of the ship in shallow and narrow waters can be calculated. The ship electric propulsion power model constructed based on this model can accurately calculate the required electric propulsion power, providing a data foundation for the subsequent construction of a ship energy system power generation scheduling model. Furthermore, the accurate calculation of electric propulsion power ensures a high degree of matching between energy supply and actual propulsion demand, avoiding energy waste or insufficient power due to oversupply or undersupply, thereby improving the ship's energy utilization efficiency. Simultaneously, navigation conditions in confined waters (such as shallow and narrow waters) are complex and variable. Traditional dynamic models cannot accurately reflect the forces acting on the ship in such environments. The ship dynamics model of this invention fully considers the characteristics of confined waters, enabling a more realistic simulation of the ship's navigation state and improving the accuracy of the subsequent power generation scheduling model.

[0083] This invention constructs a state coupling model for the ship's navigation process based on the analysis of the state coupling characteristics of the energy storage system, and calculates the dynamic power feasible range of the battery using this model. This state coupling model allows for accurate calculation of the battery's dynamic power feasible range, providing a precise data foundation for subsequent power scheduling of the energy storage system. Furthermore, during ship navigation, the power output of the battery can be rationally allocated based on its real-time state and dynamic power feasible range, avoiding overcharging and discharging and operation under unsuitable conditions, thereby improving the battery life of the energy storage system.

[0084] This invention constructs a ship energy system power generation scheduling model based on a ship electric propulsion power model combined with the charging and discharging characteristics of energy storage systems. This model comprehensively considers the ship's propulsion needs and the state of the energy storage system. It enables coordinated scheduling of power generation equipment (such as generator sets) and energy storage equipment (such as batteries and supercapacitors) within the ship's energy system, achieving optimized energy allocation. This allows the power generation equipment to rationally adjust its output power according to the ship's actual needs and the state of the energy storage system, thus improving the rationality of power allocation within the energy storage system. Furthermore, ships encounter various complex operating conditions during navigation. This model can rapidly adjust the energy system's power generation scheduling strategy based on the real-time propulsion power demand calculated by the ship's electric propulsion power model and the charging and discharging characteristics of the energy storage system. This allows the energy system to better adapt to changes in complex navigation conditions, ensuring a stable and reliable energy supply for the ship under various circumstances, further improving the reliability and safety of ship operation.

[0085] This invention calculates the power requirements of batteries and supercapacitors in an energy storage system based on the total power demand and the feasible range of battery dynamic power. It then uses a power balance equation to perform power scheduling between the batteries and supercapacitors with the goal of minimizing power error. This scheduling method fully considers the different characteristics of batteries and supercapacitors, rationally allocating power according to actual needs. This avoids energy waste or equipment overload caused by unreasonable power allocation, improving the rationality and scientific nature of power allocation in the energy storage system. Simultaneously, batteries in energy storage systems have high energy density, making them suitable for providing stable power output over long periods, while supercapacitors have high power density and fast charging / discharging speeds, making them suitable for handling short-term high power demands. By minimizing power error through power scheduling, the advantages of batteries and supercapacitors are complemented, thereby improving the overall performance of the energy storage system. Attached Figure Description

[0086] To make the objectives, technical solutions, and advantages of the invention clearer, the invention will now be described in further detail with reference to the accompanying drawings, wherein:

[0087] Figure 1 A logic block diagram of a power scheduling method for a ship energy storage system that takes into account state coupling characteristics.

[0088] Figure 2 This is a structural block diagram of a ship.

[0089] Figure 3 This is a framework diagram of an electric propulsion model. Detailed Implementation

[0090] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but only to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0091] The following detailed explanation illustrates the specific implementation methods:

[0092] Example:

[0093] This embodiment discloses a power scheduling method for a ship energy storage system that considers state coupling characteristics.

[0094] like Figure 1 As shown, a power scheduling method for ship energy storage systems considering state coupling characteristics includes:

[0095] S1: Establish a ship dynamics model for navigation in a limited water area; the ship dynamics model is used to calculate the total resistance of the ship when navigating in shallow and narrow waters;

[0096] S2: Construct a ship electric propulsion power model based on the ship dynamics model; the ship electric propulsion power model is used to calculate the ship's electric propulsion power;

[0097] S3: Based on the analysis of the state coupling characteristics of the energy storage system during ship navigation, a state coupling model is constructed during ship navigation; the state coupling model is used to calculate the dynamic power feasible range of the battery (throughout its entire life cycle);

[0098] S4: Based on the ship electric propulsion power model and the charging and discharging characteristics of the energy storage system, a ship energy system power generation scheduling model is constructed for the coordinated scheduling of ship energy system power generation; the total power demand of the energy storage system is obtained by solving the ship energy system power generation scheduling model.

[0099] S5: Based on the total power demand of the energy storage system and the feasible range of battery dynamic power, calculate the power demand of the battery and supercapacitor in the energy storage system, and combine the power balance equation to perform power scheduling between the battery and supercapacitor in the energy storage system with the goal of minimizing power error. Based on the power scheduling between the battery and supercapacitor, the energy storage system achieves coordinated energy allocation of electrical resources.

[0100] This invention establishes a ship dynamics model for navigation in confined waters and constructs a ship electric propulsion power model based on this model. By establishing a ship dynamics model for navigation in confined waters, the total resistance of the ship in shallow and narrow waters can be calculated. The ship electric propulsion power model constructed based on this model can accurately calculate the required electric propulsion power, providing a data foundation for the subsequent construction of a ship energy system power generation scheduling model. Furthermore, the accurate calculation of electric propulsion power ensures a high degree of matching between energy supply and actual propulsion demand, avoiding energy waste or insufficient power due to oversupply or undersupply, thereby improving the ship's energy utilization efficiency. Simultaneously, navigation conditions in confined waters (such as shallow and narrow waters) are complex and variable. Traditional dynamic models cannot accurately reflect the forces acting on the ship in such environments. The ship dynamics model of this invention fully considers the characteristics of confined waters, enabling a more realistic simulation of the ship's navigation state and improving the accuracy of the subsequent power generation scheduling model.

[0101] This invention constructs a state coupling model for the ship's navigation process based on the analysis of the state coupling characteristics of the energy storage system, and calculates the dynamic power feasible range of the battery using this model. This state coupling model allows for accurate calculation of the battery's dynamic power feasible range, providing a precise data foundation for subsequent power scheduling of the energy storage system. Furthermore, during ship navigation, the power output of the battery can be rationally allocated based on its real-time state and dynamic power feasible range, avoiding overcharging and discharging and operation under unsuitable conditions, thereby improving the battery life of the energy storage system.

[0102] This invention constructs a ship energy system power generation scheduling model based on a ship electric propulsion power model combined with the charging and discharging characteristics of energy storage systems. This model comprehensively considers the ship's propulsion needs and the state of the energy storage system. It enables coordinated scheduling of power generation equipment (such as generator sets) and energy storage equipment (such as batteries and supercapacitors) within the ship's energy system, achieving optimized energy allocation. This allows the power generation equipment to rationally adjust its output power according to the ship's actual needs and the state of the energy storage system, thus improving the rationality of power allocation within the energy storage system. Furthermore, ships encounter various complex operating conditions during navigation. This model can rapidly adjust the energy system's power generation scheduling strategy based on the real-time propulsion power demand calculated by the ship's electric propulsion power model and the charging and discharging characteristics of the energy storage system. This allows the energy system to better adapt to changes in complex navigation conditions, ensuring a stable and reliable energy supply for the ship under various circumstances, further improving the reliability and safety of ship operation.

[0103] This invention calculates the power requirements of batteries and supercapacitors in an energy storage system based on the total power demand and the feasible range of battery dynamic power. It then uses a power balance equation to perform power scheduling between the batteries and supercapacitors with the goal of minimizing power error. This scheduling method fully considers the different characteristics of batteries and supercapacitors, rationally allocating power according to actual needs. This avoids energy waste or equipment overload caused by unreasonable power allocation, improving the rationality and scientific nature of power allocation in the energy storage system. Simultaneously, batteries in energy storage systems have high energy density, making them suitable for providing stable power output over long periods, while supercapacitors have high power density and fast charging / discharging speeds, making them suitable for handling short-term high power demands. By minimizing power error through power scheduling, the advantages of batteries and supercapacitors are complemented, thereby improving the overall performance of the energy storage system.

[0104] To better illustrate the technical solution of the present invention, this embodiment is described in the following parts.

[0105] In this embodiment, a typical 1500V DC shipborne microgrid is used as an example, and its structure is as follows: Figure 2 As shown, the DC microgrid system uses an energy network (blue lines and arrows) and a communication network (green lines and arrows). This distributed structure reduces AC wiring in the bus circuits, and the power generated by the main unit, energy cells, and power supercapacitors is used to power the propulsion, service, and navigation systems.

[0106] I. Ship Dynamics Model

[0107] Based on typical confined water scenarios, a dynamic model of ship navigation in confined waters is established to analyze the impact of shallow and narrow waters on ship navigation resistance.

[0108] The ship dynamics model is represented as follows:

[0109] R shallow =R h +R wave +R wind ;

[0110] in:

[0111] R h =R v +R app +R w +R b +R tr +R a ;

[0112]

[0113] Considering the bulkhead effect, a bulkhead function y+ is used to describe the influence of the bulkhead on the above hydrodynamic coefficients. That is, the smaller y+ is, the greater the pulsation velocity of the fluid on the bulkhead and the greater the influence of viscosity, and vice versa.

[0114]

[0115] S A =L pp (1.8d+C B B);

[0116] In the formula: R shallow R represents the total resistance a ship encounters while navigating in shallow, narrow waters. h R is the hydrostatic resistance; wave For wave drag; R wind For wind resistance; F r S represents the Froude number; A Indicates the wetted area of ​​a ship; ρ, U, g, C BThese represent water density, sailing speed, gravitational acceleration, and arresting factor, respectively; H is wave height; C wind ρ air A T U wind These are drag coefficient, air density, frontal area, and wind speed, respectively; R v R app R w R b R tr R a These are viscous drag, appendage drag, wave-breaking drag, additional drag from the bow, additional drag from stern filtration, and model ship-related drag, respectively; R v R represents viscous resistance; f and R pv These are frictional resistance and viscous pressure resistance, respectively; C f and C pv These are the frictional resistance coefficient and the viscous pressure resistance coefficient, respectively; R e ρ is the Reynolds number; k1 is the hull form factor; p represents fluid pressure drag; μ represents dynamic viscosity; For Laplace operator; η is the precise distance from the ship to the bulkhead in meters; L pp d represents the length of the hull; d represents the draft of the hull; and B represents the width of the hull.

[0117] II. Ship Electric Propulsion Power Model

[0118] Based on the total navigation resistance and ship dynamics model, a data-driven power model for ship electric propulsion is constructed to improve the accuracy of electric propulsion power estimation.

[0119] like Figure 3 As shown, the ship's electric propulsion power model is constructed through the following steps:

[0120] S201: Based on the ship dynamics model, and combined with the total sailing resistance, construct an electric propulsion model based on ship dynamics (to deduce the electric power transmission process from the electric propulsion motor to the propeller);

[0121] The electric propulsion model is represented as follows:

[0122]

[0123] In the formula: P shallow Indicates electric propulsion power; j, η m These represent the number and efficiency of the propulsion motors, respectively; K T and K Q These are the thrust and torque coefficients, derived from marine test data, namely K. T = -0.1429J 2 -0.3286J +0.3486 and KQ = -0.0123J 2 -0.0311J + 0.0402, where J is the propulsion speed ratio; D r Indicates the propeller diameter;

[0124] S202: Uncertain wave collisions, such as variations in submersion depth and wave frequency fluctuations, can lead to unpredictable thrust loss and propeller mechanical wear. Combining the complementary advantages of interpretable ship dynamics models and data-driven methods, a data-driven electric propulsion dynamics model is proposed to further capture navigation uncertainties. Specifically, the ship dynamics-based electric propulsion model in step S201 is reconstructed into a data-driven ship electric propulsion dynamics model.

[0125] The power model of ship electric propulsion is represented as follows:

[0126]

[0127] In the formula: P s ω represents the electric propulsion power; ω is the propeller speed. The propulsion speed is related to the actual propulsion performance of the propeller; w is the wake coefficient; a0, a1, a2, a 2+j ,a 2+2j These are the regression parameters for general deep neural network methods.

[0128] III. State Coupling Model

[0129] Based on battery datasets from land-based applications and a general joint state estimation method, the state coupling characteristics of energy storage systems during ship navigation are analyzed.

[0130] The state-coupled model of a ship during navigation is represented as follows:

[0131]

[0132] In the formula: x represents the state variable; y represents the output variable; and diag is the diagonal matrix SOC. bat and SOC uc SOC of batteries and supercapacitors, respectively; I bat and I uc These are the battery current and the supercapacitor current, respectively; P bat and P uc These represent the outputs of the battery and the supercapacitor, respectively; ΔT is the control range; η bat and η uc Power efficiency, including discharge efficiency, for batteries and supercapacitors, respectively. and charging efficiency R0 and U P These are the battery's ohmic resistance and polarization voltage, respectively.

[0133] R uc C uc These are the initial voltage, internal resistance, and initial capacitance of the supercapacitor, respectively. These are the battery's current capacity and initial capacity, respectively. and Θ represents the minimum and maximum power for charging or discharging the battery, respectively; Θ is the state coupling function.

[0134] IV. Feasible Range of Dynamic Power

[0135] Considering the frequent swaying and uncertain shipping environment, a joint estimation method is adopted to capture the coupling relationship between SOC, SOH and SOP under harsh marine applications.

[0136] Specifically, the feasible range of dynamic power of the battery is calculated through the following steps:

[0137] S401: Identify the initial internal parameters of the battery using a recursive least squares (RLS) algorithm with a forgetting factor;

[0138] S402: Estimating the battery's state of charge (SOC) using a dynamic output model derived from a minimum error optimization method. bat ;

[0139] S403: Estimate the state of health (SOH) of the battery by using a capacity degradation model that takes into account dynamic voltage decay;

[0140] S404: The battery's state of charge (SOC) is combined with the following formula in the state-coupled model. bat Based on the State of Health (SOH), calculate the feasible range of dynamic power for the battery:

[0141]

[0142] In the formula: This indicates the feasible dynamic power range throughout the battery's entire lifespan. and These are the minimum and maximum power for charging or discharging the battery, respectively; SOC bat SOH represents the state of charge and state of health of the battery; Θ is the state coupling function.

[0143] 1) Estimate the battery's state of charge using the following steps:

[0144] S4021: To decouple the impact of long-term lifetime degradation on short-term operation and accurately estimate SOC, a dynamic output model is established:

[0145]

[0146] In the formula: Ubat Indicates the battery voltage; C p R p and I p These represent polarization capacity, polarization resistance, and polarization current, respectively; OCV represents the battery open-circuit voltage.

[0147] S4022: Considering the direct relationship between SOC and current, the model output can be used to represent the change in SOC. The SOC estimation process of the dynamic output model in step S4021 is modeled as the following minimum output error optimization problem:

[0148]

[0149] In the formula: To estimate the state variables; G v and G i These represent the output voltage error and the output current error, respectively; Q1, Q2, and R are the weights. The cost function is defined as follows: N is the runtime within each cycle.

[0150] S4023: Solve the minimum output error optimization problem in step S4022 to obtain... And extract the battery's state of charge (SOC) from it. bat .

[0151] The capacity degradation error is calculated using the following formula, and the estimation error of the battery state of charge is reduced by correcting the capacity degradation error.

[0152] The formula for calculating capacity degradation error is as follows:

[0153]

[0154] In the formula: K represents the actual capacity of the battery; K is the gain matrix of the H∞ estimator; e U For output voltage error; Γ1 and Γ2 are error transfer functions; U b and I b These are the bias noises of the voltage and current sensors, respectively; U v and I v These are the variance noises of the voltage and current sensors, respectively.

[0155] 2) Estimate the battery's health status using the following steps:

[0156] S4031: A capacity degradation model that considers dynamic voltage decay is used to establish the coupling relationship between voltage dynamics, current rate conditions and capacity degradation in each discharge / charge cycle n;

[0157] The capacity degradation model is expressed as:

[0158]

[0159] In the formula: The estimated initial capacity for period n; C r I is the current rate; r U is the standard current in 1C; p χ is the polarization voltage of the battery. n is the derivative of OCV; V is the measurement noise.

[0160] S4032: Convert the capacity degradation model in step S4031 into Formula 1;

[0161] Formula 1 is expressed as:

[0162]

[0163] In the formula: This represents the standard current in the j-th 1C with period n;

[0164] S4033: Solve Formula 1 above to obtain the battery's current capacity C. bat And through the formula in the state coupling model Calculate the battery's health status.

[0165] V. Power Generation Dispatch Model for Ship Energy Systems

[0166] Taking full account of the charging and discharging characteristics of the energy storage system, the power generation of the ship's energy system is coordinated and scheduled to reduce the fuel consumption and gas emissions of the diesel engine.

[0167] The objective function of the ship energy system power generation scheduling model is expressed as:

[0168]

[0169] The constraints of the ship energy system power generation dispatch model are expressed as follows:

[0170]

[0171] The SFC calculation formula is as follows:

[0172]

[0173] In the formula: This represents the total power requirement of the energy storage system. To meet the power requirements of marine main engines, the main engine output is controlled by rotational speed and excitation current; DoD bat and W bat These represent the depth of discharge and energy of the battery, respectively; DoD uc and W uc, respectively, represent the depth of discharge and energy of the supercapacitor; SFC and f1 represent the specific fuel consumption (kg / h) and emission coefficient, respectively; f2 = ±10-3 and f3 ±10-4 represent the equivalent fuel consumption coefficients of the battery and supercapacitor, respectively, which are negative when the corresponding ESS is discharged, and vice versa; FCC = 82.26% represents the carbon content of the fuel; EGF represents the exhaust gas flow rate (m3 / h); ρ1 = 1.977 kg / m represents the density of CO2; C1 = 0.29% and C2 = 0.04% represent the CO2 concentration in the exhaust gas and the dry CO2 concentration in the atmosphere, respectively.

[0174] VI. Power Coordination and Dispatch

[0175] The power feedforward method is used to coordinate and schedule the power between the battery and the supercapacitor in the energy storage system, preventing the battery from being overcharged / over-discharged, and reducing the resulting power deficit.

[0176] Specifically, the power coordination and scheduling between batteries and supercapacitors in the energy storage system is achieved through the following steps:

[0177] S501: The power requirements of batteries and supercapacitors in the energy storage system are obtained by combining the total power requirements of the energy storage system with the feedforward method.

[0178] The power requirements of batteries and supercapacitors in the energy storage system can be calculated using the following formula:

[0179]

[0180] In the formula: and The power requirements for batteries and supercapacitors are respectively; P ref For power feedforward; Rated DC voltage; K p K i These are the proportional and integral terms of the voltage controller, respectively; T s This is the time constant. The current requirements of the battery and supercapacitor can then be obtained. and

[0181] S502: Based on the feasible range of battery dynamic power, calculate the battery power error Δe using the following formula. bat ;

[0182]

[0183] In the formula: Indicates the battery's power requirement; P bat This indicates the battery's output power; SOP indicates the battery's dynamic power feasible range. These represent the minimum power for charging and discharging the battery, respectively; N batIndicates the number of batteries;

[0184] S503: The power error Δe of the supercapacitor is calculated using the following formula. uc :

[0185]

[0186] In the formula: This indicates the power requirement of the supercapacitor; P uc This indicates the output power of the supercapacitor;

[0187] S504: Power error Δe via battery bat The power error Δe of the supercapacitor uc Calculate the total power error Δe of the energy storage system ess =Δe bat +Δe uc ;

[0188] S505: Introduces a self-learning controller (ILC) to predict changes in the state trajectory of the energy storage system (reducing the total power error Δe). ess And construct the power balance equation, combined with the power feedforward error Δe ess To achieve the goal of power scheduling between batteries and supercapacitors in an energy storage system, the optimal power allocation between batteries and supercapacitors in the energy storage system is obtained.

[0189] The power balance equation is expressed as follows:

[0190] P ess (k)=P uc (k)+P bat (k)+i ILC (k)U uc (k);

[0191] In the formula: P ess P represents the total power of the energy storage system (ESS); uc P represents the output power of the supercapacitor. bat Indicates the battery's output power; U uc This indicates the voltage of the supercapacitor;

[0192]

[0193] In the formula: i ILC This represents the compensation current of the supercapacitor; G ILC Represents a self-learning controller; CR*G I T represents the transfer function of the current closed-loop controller; c K is the time constant; ILc The gain of the self-learning controller, including the gain of the energy storage system in buck mode and boost mode;

[0194] This invention addresses the voltage stability problem of ship power systems in shallow and narrow waters, and proposes a health management method for energy storage systems that considers state coupling characteristics. This method coordinates the energy distribution of multiple power sources in the system, reducing power deficits on the ship while preventing battery overload.

[0195] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit the technical solutions. Those skilled in the art should understand that any modifications or equivalent substitutions to the technical solutions of the present invention without departing from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A power scheduling method for a ship energy storage system considering state coupling characteristics, characterized in that, include: S1: Establish a ship dynamics model for navigation in a limited water area; the ship dynamics model is used to calculate the total resistance of the ship when navigating in shallow and narrow waters; S2: Construct a ship electric propulsion power model based on the ship dynamics model; the ship electric propulsion power model is used to calculate the ship's electric propulsion power; S3: Based on the analysis of the state coupling characteristics of the energy storage system during ship navigation, a state coupling model is constructed for the ship navigation process; the state coupling model is used to calculate the dynamic power feasible range of the battery; S4: Based on the ship electric propulsion power model and the charging and discharging characteristics of the energy storage system, construct a ship energy system power generation scheduling model; solve the ship energy system power generation scheduling model to obtain the total power demand of the energy storage system; S5: Calculate the power demand of batteries and supercapacitors in the energy storage system based on the total power demand of the energy storage system and the feasible range of battery dynamic power, and combine the power balance equation to perform power scheduling between batteries and supercapacitors with the goal of minimizing power error.

2. The power dispatching method for ship energy storage systems considering state coupling characteristics as described in claim 1, characterized in that: In step S1, the ship dynamics model is represented as follows: R shallow =R h +R wave +R wind ; in: R h =R v +R app +R w +R b +R tr +R a ; S A =L pp (1.8d+C B B); In the formula: R shallow R represents the total resistance a ship encounters while navigating in shallow, narrow waters. h R is the hydrostatic resistance; wave For wave drag; R wind For wind resistance; F r S represents the Froude number; A Indicates the wetted area of ​​the ship; ρ, U, g, C B These represent water density, sailing speed, gravitational acceleration, and arresting factor, respectively; H is wave height; C wind ρ air A T U wind These are drag coefficient, air density, frontal area, and wind speed, respectively; R v R app R w R b R tr R a These are viscous drag, attached drag, wave-breaking drag, additional drag at the bow, additional drag at the stern, and hull-related drag; R v R represents viscous resistance; f and R pv These are frictional resistance and viscous pressure resistance, respectively; C f and C pv These are the frictional resistance coefficient and the viscous pressure resistance coefficient, respectively; R e L is the Reynolds coefficient; k1 is the ship type coefficient; L pp d represents the length of the hull; d represents the draft of the hull; and B represents the width of the hull.

3. The power dispatching method for ship energy storage systems considering state coupling characteristics as described in claim 2, characterized in that: In step S2, the ship's electric propulsion power model is constructed through the following steps: S201: Constructing an electric propulsion model based on ship dynamics; The electric propulsion model is represented as: In the formula: P shallow Indicates electric propulsion power; j, η m These represent the number and efficiency of the propulsion motors, respectively; K T and K Q These are the thrust and torque coefficients, respectively; J is the propulsion speed ratio; D r Indicates the propeller diameter; S202: Reconstruct the ship dynamics-based electric propulsion model in step S201 into a data-driven ship electric propulsion power model; The power model of ship electric propulsion is represented as follows: In the formula: P s ω represents the electric propulsion power; ω is the propeller speed. The propulsion speed is related to the actual propulsion performance of the propeller; w is the wake coefficient; a0, a1, a2, a 2+j ,a 2+2j Regression parameters based on deep neural networks.

4. The power dispatching method for ship energy storage systems considering state coupling characteristics as described in claim 1, characterized in that: In step S3, the state coupling model during the ship's navigation process is represented as follows: In the formula: x represents the state variable; y represents the output variable; and diag is the diagonal matrix SOC. bat and SOC uc The SOC of batteries and supercapacitors, respectively; I bat and I uc These are the battery current and the supercapacitor current, respectively; P bat and P uc These represent the outputs of the battery and the supercapacitor, respectively; ΔT is the control range. η bat and η uc Power efficiencies of batteries and supercapacitors, respectively; R0 and U. P These are the battery's ohmic resistance and polarization voltage, respectively. R uc C uc These represent the initial voltage, internal resistance, and initial capacitance of the supercapacitor, respectively; C bat , These are the battery's current capacity and initial capacity, respectively. and These are the minimum and maximum power for charging or discharging the battery, respectively. Θ is the state coupling function.

5. The power dispatching method for ship energy storage systems considering state coupling characteristics as described in claim 4, characterized in that: In step S4, the feasible range of dynamic power of the battery is calculated through the following steps: S401: Identify the initial internal parameters of the battery using a recursive least squares algorithm; S402: Estimate the battery's state of charge (SOC) using a dynamic output model derived from a minimum error optimization method. bat ; S403: Estimate the state of health (SOH) of the battery by considering a capacity degradation model that takes into account dynamic voltage decay; S404: The battery's state of charge (SOC) is combined with the following formula in the state-coupled model. bat Based on the State of Health (SOH), calculate the feasible range of dynamic power for the battery: In the formula: This indicates the feasible range of dynamic power for the battery. and These are the minimum and maximum power for charging or discharging the battery, respectively; SOC bat SOH represents the state of charge and state of health of the battery; Θ is the state coupling function.

6. The power scheduling method for ship energy storage systems considering state coupling characteristics as described in claim 5, characterized in that: In step S402, the state of charge of the battery is estimated through the following steps: S4021: Construct the following dynamic output model: In the formula: U bat Indicates the battery voltage; C p R p and I p These represent polarization capacity, polarization resistance, and polarization current, respectively; OCV represents the battery open-circuit voltage. S4022: The SOC estimation process of the dynamic output model in step S4021 is modeled as the following minimum output error optimization problem: In the formula: To estimate the state variables; G v and G i These represent the output voltage error and the output current error, respectively; Q1, Q2, and R are the weights. The cost function is defined as follows: N is the runtime within each cycle. S4023: Solve the minimum output error optimization problem in step S4022 to obtain... And extract the battery's state of charge (SOC) from it. bat .

7. The power dispatching method for ship energy storage systems considering state coupling characteristics as described in claim 5, characterized in that: In step S403, the battery health status is estimated through the following steps: S4031: Construct the following capacity degradation model: In the formula: The estimated initial capacity for period n; C r I is the current rate; r U is the standard current in 1C; p χ is the polarization voltage of the battery. n is the derivative of OCV; V is the measurement noise. S4032: Convert the capacity degradation model in step S4031 into Formula 1; Formula 1 is expressed as: In the formula: This represents the standard current in the j-th 1C with period n; S4033: Solve Formula 1 above to obtain the battery's current capacity C. bat And through the formula in the state coupling model Calculate the battery's state of health (SOH).

8. The power dispatching method for ship energy storage systems considering state coupling characteristics as described in claim 1, characterized in that: In step S4, the objective function of the ship energy system power generation scheduling model is expressed as: The constraints of the ship energy system power generation dispatch model are expressed as follows: In the formula: This represents the total power requirement of the energy storage system. Power requirements for marine main engines; DoD bat and W bat These represent the depth of discharge and energy of the battery, respectively; DoD uc and W uc f1 and f2 are the depth of discharge and energy of the supercapacitor, respectively; f2 = ±10⁻³ and f3 ±10⁻⁴ are the equivalent fuel consumption coefficients of the battery and the supercapacitor, respectively. The corresponding values ​​are negative when the energy storage system is discharging, and negative otherwise.

9. The power dispatching method for a ship energy storage system considering state coupling characteristics as described in claim 1, characterized in that: In step S5, power dispatching between the battery and the supercapacitor in the energy storage system is achieved through the following steps: S501: The power requirements of batteries and supercapacitors in the energy storage system are obtained by combining the total power requirements of the energy storage system with the feedforward method. S502: Based on the feasible range of battery dynamic power, calculate the battery power error Δe using the following formula. bat ; In the formula: Indicates the battery's power requirement; P bat This indicates the battery's output power; SOP indicates the battery's dynamic power feasible range. These represent the minimum power for charging and discharging the battery, respectively; N bat Indicates the number of batteries; S503: The power error Δe of the supercapacitor is calculated using the following formula. uc : In the formula: This indicates the power requirement of the supercapacitor; P uc This indicates the output power of the supercapacitor; S504: Power error Δe via battery bat The power error Δe of the supercapacitor uc Calculate the total power error Δe of the energy storage system ess =Δe bat +Δe uc ; S505: Introduces a self-learning controller to predict the state trajectory changes of the energy storage system and constructs a power balance equation, combined with the power feedforward method error Δe ess To achieve the goal of power scheduling between batteries and supercapacitors in an energy storage system, the optimal power allocation between batteries and supercapacitors in the energy storage system is obtained. The power balance equation is expressed as follows: P ess (k)=P uc (k)+P bat (k)+i ILC (k)U uc (k); In the formula: P ess P represents the total power of the energy storage system. uc P represents the output power of the supercapacitor; bat Indicates the battery's output power; U uc This indicates the voltage of the supercapacitor; in: In the formula: i ILC This represents the compensation current of the supercapacitor; G ILC Represents a self-learning controller; CR*G I T represents the transfer function of the current closed-loop controller; c K is the time constant; ILc The gain of the self-learning controller, including the gain of the energy storage system in buck and boost modes.

10. The power scheduling method for a ship energy storage system considering state coupling characteristics as described in claim 9, characterized in that: In step S501, the power requirements of the battery and supercapacitor in the energy storage system are calculated using the following formula: In the formula: and The power requirements for batteries and supercapacitors are respectively; P ref For power feedforward; The rated DC voltage; K p K i These are the proportional and integral terms of the voltage controller, respectively; T s is the time constant.

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