Multi-working-condition energy optimization management system with cooperation of ship shaft power generation and lithium battery

Abstract

The invention provides a ship shaft power generation and lithium battery coordinated multi-working-condition energy optimization management system, which is applied to the technical field of ship power systems and energy management and comprises a data acquisition module, an energy optimization processing module and a power regulation and control module, wherein the energy optimization processing module is provided with a prediction unit, a working condition identification unit, a loss evaluation unit and a decision-making unit, the prediction unit is used for predicting a ship load demand in a preset period, the working condition identification unit is used for identifying a ship operation working condition, and the loss evaluation unit is used for determining a life loss factor by combining a prediction result and an energy storage unit state; the decision-making unit generates a regulation and control instruction based on the life loss factor and the state of the power generation unit to realize power distribution of the power generation unit and the energy storage unit; according to the system, multi-working-condition energy consumption requirements of the ship are met, meanwhile, comprehensive optimization of fuel consumption cost and energy storage unit service life loss cost is achieved, the energy utilization rate is increased, the service life of the energy storage unit is prolonged, and the full-life-cycle operation cost of the ship is reduced.

Description

Technical Field

[0001] This application relates to the field of marine propulsion systems and energy management technology, and in particular to a multi-condition energy optimization management system that integrates ship shaft power generation with lithium batteries. Background Technology

[0002] The multi-condition energy optimization management system for ship shaft generators and lithium batteries is a comprehensive energy management solution. It integrates the electrical energy generated by the ship's main engine shaft generator and the charging and discharging functions of the energy storage unit. Based on the ship's energy demand and load changes under different navigation conditions, it uses intelligent control algorithms to monitor, allocate, and optimize energy in real time, so as to achieve efficient use of ship energy, reduce fuel consumption and pollutant emissions, and improve the stability and reliability of the ship's power system.

[0003] In existing technologies, when a multi-condition energy optimization management system that coordinates ship shaft generators and lithium batteries is working, it first collects data in real time, such as the main engine operating parameters, shaft generator power, load demand, and energy storage unit status under different ship operating conditions. Then, it uses a preset intelligent algorithm to accurately control the output power of the shaft generator based on the characteristics of the operating conditions and energy demand, and rationally plans the charging and discharging strategy of the energy storage unit. This ensures that while meeting the power needs of various ship equipment, it achieves efficient energy utilization and dynamic balance, thereby reducing energy consumption and emissions.

[0004] The above-mentioned solutions still have some problems in practical application. In order to save fuel during the operation of ships, the system generally adopts an optimization strategy aimed at minimizing the instantaneous fuel consumption rate, which often ignores the key factor of the lifespan degradation of high-value lithium batteries. At the same time, the degradation cost of high-value lithium batteries is also a considerable expense, which will lead to a higher comprehensive cost of the ship's entire life cycle. Summary of the Invention

[0005] This application provides a multi-condition energy optimization management system for ship shaft generators and lithium batteries, which can optimize the power distribution between shaft generators and energy storage units according to different ship operating conditions. While ensuring power supply reliability, it can effectively smooth load fluctuations, while taking into account fuel economy and battery life, and ultimately reduce the system's total life cycle operating cost.

[0006] To achieve the above objectives, this application adopts the following technical solution: This application provides a multi-condition energy optimization management system for ship shaft generators and lithium batteries working in tandem. The system includes: a data acquisition module, an energy optimization processing module, and a power regulation module. The energy optimization processing module is electrically connected to the data acquisition module and is used to receive operating parameters and external information from the ship's power system. The operating parameters include energy storage unit status parameters and generator status parameters. The energy optimization processing module includes: a prediction unit, used to predict the ship's load demand curve within a preset prediction period threshold based on the operating parameters of the ship's power system and the external information, and send the prediction result to the loss assessment unit; and a condition identification unit. The system is used to identify the current operating condition of the ship based on the operating parameters of the ship's power system, and send the current operating condition to the loss assessment unit; the loss assessment unit is used to determine the lifetime loss factor of the energy storage unit based on the received prediction results, the current operating condition, and the state parameters of the energy storage unit, and send the lifetime loss factor to the decision unit; the decision unit is used to receive the lifetime loss factor and the state parameters of the power generation unit, and determine the control command for adjusting the output power of the power generation unit and the charging and discharging power of the energy storage unit based on the lifetime loss factor and the state parameters of the power generation unit, so that the power control module realizes the power distribution between the power generation unit and the energy storage unit in the ship's power grid based on the control command.

[0007] In some possible implementations, the loss assessment unit is specifically used to: calculate a lifetime loss factor reflecting the degree of lifetime degradation caused by the unit energy throughput of the energy storage unit based on the state parameters of the energy storage unit, correct the lifetime loss factor based on the prediction results to obtain an adjusted lifetime loss factor, and send the adjusted lifetime loss factor to the decision unit.

[0008] In some possible implementations, the operating condition identification unit is specifically used to: receive operating parameters of the ship's power system from the data acquisition module, the operating parameters of the ship's power system also including the ship's speed, and the status parameters of the power generation unit including the output power of the power generation unit; identify the ship's current operating condition based on the ship's speed and the output power of the power generation unit, and send the current operating condition to the loss assessment unit.

[0009] In some possible implementations, the loss assessment unit has multiple built-in mapping tables for storing lifetime loss factor query parameters under different operating conditions and different prediction characteristics. The loss assessment unit is also used to: receive the current operating condition identified by the operating condition identification unit; receive the prediction result and extract a first feature and a second feature based on the prediction result. The first feature includes one or more of future average load power, future load power fluctuation rate, and future peak power. The second feature includes one or more of load ramp-up rate, load duration, and number of load abrupt changes. Based on the current operating condition and the first feature, determine a target mapping table from the multiple mapping tables. Calculate an initial lifetime loss factor based on the energy storage unit state parameters and the query parameters corresponding to the target mapping table. Correct the initial lifetime loss factor based on the second feature to obtain an adjusted lifetime loss factor. The lifetime loss factor is used to characterize the degree of lifetime degradation of the energy storage unit under different operating conditions and serves as the basis for the decision-making unit to optimize power allocation and protect lifetime.

[0010] In some possible implementations, the decision unit is specifically used to: determine the fuel consumption cost based on a preset fuel price and the output power of the power generation unit, and determine the energy storage unit loss cost based on the lifespan loss factor and the energy throughput of the energy storage unit; using the sum of the fuel consumption cost and the energy storage unit loss cost as the optimization target, and obtaining the target output power value signal of the power generation unit and the target charge / discharge power value signal of the energy storage unit by performing optimization calculations; and generating control commands based on the target output power value signal of the power generation unit and the target charge / discharge power value signal of the energy storage unit.

[0011] In some possible implementations, the decision unit further includes a calculator for performing optimization calculations. After performing the optimization calculations, the calculator obtains a target output power value signal for the power generation unit and a target charge / discharge power value signal for the energy storage unit. The calculator includes a first input port, a second input port, and a third input port. The first input port is used to receive a first signal related to fuel consumption costs, the second input port is used to receive a second signal related to energy storage unit loss costs, and the third input port is used to receive a load demand power signal from the ship's electrical grid. Specifically, the calculator is used to: based on an equivalent minimum consumption strategy, and under the constraint of satisfying the load demand power signal, fuse the first signal and the second signal, and output the target output power value signal for the power generation unit and the target charge / discharge power value signal for the energy storage unit. The constraint of the load demand power signal is that the load demand power signal received by the third input port is equal to the sum of the target output power value signal for the power generation unit and the target charge / discharge power value signal for the energy storage unit.

[0012] In some possible implementations, the decision unit generates a first instruction for regulating the power generation unit based on the target output power value signal of the power generation unit; and generates a second instruction for regulating the energy storage unit based on the target charge / discharge power value signal of the energy storage unit; the first instruction and the second instruction constitute the regulation instruction.

[0013] In some possible implementations, the energy optimization processing module further includes a feedback unit electrically connected to the loss assessment unit and the decision unit. This feedback unit receives the lifetime loss factor from the loss assessment unit and energy storage unit status parameters from the data acquisition module, including the energy throughput rate of the energy storage unit. Specifically, the feedback unit is used to: determine a loss value based on the lifetime loss factor and the energy throughput rate, and obtain a cumulative loss value by summing the loss value; if the cumulative loss value exceeds a preset threshold, output an alarm signal and input the alarm signal to the decision unit, so that the decision unit generates a safety instruction based on the alarm signal. This safety instruction is set as the highest priority control instruction among the control instructions, used to control the energy storage unit to exit operation and control the power generation unit to bear the full load demand of the ship's power system.

[0014] In some possible implementations, the operating condition identification unit is used to identify multiple operating conditions, including one or more of acceleration, deceleration, cruising, loading / unloading, and severe sea conditions.

[0015] In some possible implementations, the method includes: collecting operating parameters and external information of the ship's power system, the operating parameters including energy storage unit status parameters and power generation unit status parameters; predicting the ship's load demand curve based on the operating parameters and the external information within a preset prediction period threshold, as the prediction result; identifying the ship's current operating condition based on the operating parameters to obtain current operating condition information; determining the lifespan loss factor of the energy storage unit based on the prediction result, the current operating condition information, and the energy storage unit status parameters; generating control commands for adjusting the output power of the power generation unit and the charging and discharging power of the energy storage unit based on the lifespan loss factor and the power generation unit status parameters, the control commands also including safety commands, the safety commands having the highest priority and used to control the energy storage unit to exit operation; and controlling the power distribution between the power generation unit and the energy storage unit according to the control commands.

[0016] As can be seen from the above technical solution, this application has the following beneficial effects: 1. This application addresses the problem in existing technologies that only minimize instantaneous fuel consumption rate as the optimization objective while neglecting the lifespan degradation of energy storage units. By introducing a prediction unit, operating condition identification unit, and loss assessment unit into the energy optimization processing module, it achieves a comprehensive consideration of fuel consumption costs and energy storage unit lifespan degradation costs. This solution can dynamically adjust the lifespan degradation factor based on different operating conditions and prediction results, thereby reducing the lifespan degradation of energy storage units caused by overcharging and discharging under frequent acceleration and deceleration conditions in short-distance transportation, reducing the risk of single-use loss of high-value lithium batteries, and helping to control the overall operating costs of ships throughout their entire life cycle.

[0017] 2. This application constructs a comprehensive optimization objective function for fuel consumption cost and energy storage unit loss cost in the decision-making unit, and takes the minimum equivalent consumption as the optimization principle, so that the system can achieve an overall balance between fuel cost and battery life cost while meeting the ship's load requirements. Compared with the existing technology that unilaterally pursues the minimum fuel consumption, this application can maintain the economy and practicality of energy dispatch under multiple operating conditions.

[0018] 3. This application uses a feedback unit to monitor and determine the lifetime loss factor and energy throughput rate of the energy storage unit in real time. This allows for timely triggering of safety commands when the energy storage unit approaches a high loss risk or safety risk state, causing the energy storage unit to shut down and the power generation unit to assume all load requirements. This mechanism not only avoids safety hazards such as swelling and thermal runaway caused by overcharging and discharging of batteries in a low health state, but also ensures the stability of the system under high-intensity usage scenarios. Thus, it improves energy efficiency while extending the lifespan of the energy storage unit and ensuring operational safety. Attached Figure Description

[0019] Figure 1 This is a structural example diagram of the multi-condition energy optimization management system for ship shaft power generation and lithium battery coordination in this application; Figure 2 This is a unit structure diagram of the energy optimization processing module of this application; Figure 3 This is a flowchart of the multi-condition energy optimization management system for ship shaft power generation and lithium battery coordination, as described in this application. Detailed Implementation

[0020] The terms "first," "second," and "third," etc., used in this application specification, claims, and drawings are used to distinguish different objects, not to limit a specific order.

[0021] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0022] Research has found that high-value lithium battery energy storage units are used in short-haul transportation to accommodate frequent acceleration and deceleration. However, in order to save fuel during operation, ships generally adopt optimization strategies that aim to minimize instantaneous fuel consumption. This causes the system to frequently instruct the battery to charge and discharge, which accelerates battery aging and wear, making the value of a single battery loss relatively high. On the other hand, short-haul voyages consume relatively little fuel, and when fuel prices are low, the value of fuel may be lower than the value of a single battery loss. This leads to a higher overall cost over the ship's entire life cycle.

[0023] To address the aforementioned issues, this application provides a multi-condition energy optimization management system for ship shaft-driven power generation and lithium battery coordination. The system includes: a data acquisition module, an energy optimization processing module, and a power regulation module. The energy optimization processing module is electrically connected to the data acquisition module and is used to receive operating parameters of the ship's power system and external information from the data acquisition module. The operating parameters include energy storage unit status parameters and power generation unit status parameters. The energy optimization processing module includes: a prediction unit, used to predict the ship's load demand curve within a preset prediction period threshold based on the operating parameters of the ship's power system and the external information, and send the prediction result to the loss assessment unit; and a condition identification unit. The system is configured to: identify the current operating condition of the ship based on the operating parameters of the ship's power system, and send the current operating condition to the loss assessment unit; determine the lifetime loss factor of the energy storage unit based on the received prediction results, the current operating condition, and the state parameters of the energy storage unit, and send the lifetime loss factor to the decision unit; and receive the lifetime loss factor and the state parameters of the power generation unit, and determine, based on the lifetime loss factor and the state parameters of the power generation unit, a control command for adjusting the output power of the power generation unit and the charging and discharging power of the energy storage unit, so that the power control module can realize the power distribution between the power generation unit and the energy storage unit in the ship's power grid based on the control command.

[0024] Example 1, as Figure 1 and Figure 2 As shown, this application provides a multi-condition energy optimization management system for ship shaft power generation and lithium battery coordination. The system includes: a data acquisition module, an energy optimization processing module, and a power regulation module.

[0025] The data acquisition module can obtain the operating parameters through the ship's main engine monitoring system, generator controller, and battery management system. It can also obtain the external information through the navigation monitoring system and meteorological information interface. The operating parameters include energy storage unit status parameters and power generation unit status parameters. Energy storage unit status parameters include, but are not limited to, battery state of charge (SOC), battery temperature, battery charge and discharge rate, battery state of health (SOH), battery internal resistance, and battery cycle count. Power generation unit status parameters include, but are not limited to, power generation unit output power, fuel consumption rate, shaft speed, lubrication status, power generation efficiency, and emission parameters. External information includes, but is not limited to, route position, real-time weather conditions, sailing speed, and port berthing plan. The power generation unit includes shaft-driven generators and fuel-powered generator sets.

[0026] The energy optimization processing module is electrically connected to the data acquisition module. The energy optimization processing module can be deployed in the ship energy management system (EMS) and implemented using an embedded processor or industrial computer. It is used to predict future loads, identify current operating conditions, and determine the life loss factor of energy storage units based on the operating parameters and external information collected from the data acquisition module. It also generates corresponding power control commands after comprehensively considering energy utilization efficiency, fuel consumption costs, and energy storage unit life decay.

[0027] The power regulation module is electrically connected to the energy optimization processing module. It is used to receive power regulation commands and is connected to the bidirectional DC / DC converter of the generator excitation controller, inverter and energy storage unit through control signals. In this way, the power output power of the generator unit and the charging and discharging power of the energy storage unit are dynamically allocated and adjusted according to the power regulation commands to realize energy optimization management under multiple operating conditions.

[0028] It should be noted that the lifetime loss factor is used to characterize the degree of lifetime degradation of the energy storage unit under different operating conditions. As a constraint parameter in the energy dispatching process, the lifetime loss factor is used to select the scheme with less lifetime loss of the energy storage unit among multiple energy allocation schemes, and to trigger protection or adjustment strategies when the lifetime loss factor reaches a preset threshold, so as to extend the service life of the energy storage unit.

[0029] It should be noted that the energy storage unit includes lithium batteries.

[0030] Furthermore, the multi-condition energy optimization management system for the coordinated operation of ship shaft generators and lithium batteries can, during the coordinated operation of ship shaft generators and energy storage units, make forward-looking judgments on future load demands and dynamically identify different operating conditions based on real-time collected operating parameters and external information, combined with the prediction and evaluation functions of the energy optimization processing module. Furthermore, by determining the lifespan loss factor and generating power control commands, it can ensure energy supply stability while also considering fuel economy and extending the lifespan of the energy storage units. Compared to existing energy allocation methods that rely on single operating parameters or fixed control strategies, this application not only achieves efficient coordination between the generator and energy storage units but also significantly improves energy utilization efficiency and system robustness under complex sea conditions and variable operating conditions, thereby effectively reducing ship operating costs and enhancing the reliability of overall energy management.

[0031] Example 2, as Figure 1 and Figure 2 As shown, specifically: The energy optimization processing module includes a prediction unit; The prediction unit receives ship operating parameters and external information from the data acquisition module, performs preprocessing operations such as denoising and normalization on the data, and extracts key feature variables that reflect load change trends. Based on these key feature variables, the prediction unit establishes a time series prediction model within a preset prediction period threshold. This model captures the nonlinear mapping relationship between the key feature variables and load power changes, and generates a future load demand curve as the prediction result. The prediction result is then sent to the loss assessment unit.

[0032] It should be noted that the operating parameters include main engine speed, propulsion power, fuel consumption rate, etc., and the external information includes route position, real-time weather conditions, sailing speed, and port berthing plan, etc. It should be noted that the key characteristic variables include the rate of change of ship speed, the rate of fluctuation of main engine speed, the environmental wind and wave level, and port operation progress information, etc. It should be noted that the prediction period threshold can be set according to the operating scenario, and the range can be from 30 minutes to 2 hours, so as to balance the real-time performance and stability of the prediction.

[0033] Furthermore, by setting up a prediction unit, dynamic prediction of future load demand can be achieved. This allows the energy optimization processing module to move beyond relying on static historical averages and instead perform forward-looking extrapolations based on real-time operating parameters and external information. This not only enables the early identification of future load power peaks and fluctuation trends, preventing excessive impact on energy storage units due to sudden operating conditions, but also provides accurate input parameters for correcting lifetime loss factors. Compared to existing technologies, the prediction unit improves prediction accuracy and robustness, enhancing the system's adaptability to complex sea conditions and multiple operating conditions.

[0034] Example 3, as Figure 1 and Figure 2 As shown, specifically: The energy optimization processing module also includes an operating condition identification unit; The operating condition identification unit receives ship operating parameters from the data acquisition module and can identify the ship's current operating condition based solely on ship speed and the power output of the generator unit to meet basic operating condition classification requirements. To further improve identification accuracy, the operating condition identification unit can also combine operating parameters such as main engine speed and propulsion power to correct and subdivide the operating condition features formed by the ship speed and the power output of the generator unit, thereby achieving more refined identification of operating conditions in complex navigation scenarios. Specifically, the operating condition identification unit receives ship operating parameters from the data acquisition module, performs feature extraction and pattern matching processing on the operating parameters, and within a set identification window, classifies and determines the ship's current operating state based on the coupling relationship between multi-dimensional parameters, thereby generating the current operating condition information and sending the current operating condition information to the loss assessment unit.

[0035] It should be noted that the operating parameters include ship speed, output power of the power generation unit, main engine speed, and propulsion power, etc. It should be noted that the operating conditions include acceleration, deceleration, cruising, loading and unloading, and severe sea conditions. It should be noted that the recognition window can be set according to the navigation scenario, and the range can be from 5 minutes to 30 minutes, so as to balance the real-time performance and stability of the working condition recognition.

[0036] Furthermore, by setting up an operating condition identification unit, the ship's operating status can be identified in real time under different navigation conditions. This allows the energy optimization processing module to make accurate judgments based on dynamic operating parameters, rather than relying on static operating condition assumptions. This not only ensures that the determination of the lifespan loss factor is more in line with the actual operating environment, but also provides timely and effective input for the power allocation of the decision-making unit, avoiding fuel waste and overcharging / discharging of energy storage units due to power scheduling lags. Compared with the existing technology that makes rough judgments based on a single operating parameter, the operating condition identification unit, through feature extraction and pattern matching of multi-dimensional parameters, enables the system to have higher adaptability to sudden sea states and complex operating conditions, thereby significantly improving the economy, robustness, and safety of the coordinated operation of ship shaft generators and energy storage units.

[0037] Example 4, as Figure 1 and Figure 2 As shown, specifically: The energy optimization processing module also includes a loss assessment unit; The loss assessment unit receives the prediction results and extracts a first feature and a second feature from the prediction results; the first feature includes one or more of the following: future average load power, future load power fluctuation rate, and future peak power; the second feature includes one or more of the following: load ramp-up rate, load duration, and number of load abrupt changes.

[0038] The loss assessment unit also receives the current operating condition identified by the operating condition identification unit; based on the current operating condition and the first feature, the loss assessment unit determines the target mapping table from multiple built-in mapping tables and obtains the query parameters corresponding to the target mapping table.

[0039] The loss assessment unit calculates the initial lifetime loss factor based on the energy storage unit's state parameters and the corresponding query parameters in the target mapping table.

[0040] The loss assessment unit corrects the initial lifetime loss factor based on the second feature to obtain the adjusted lifetime loss factor, and sends the adjusted lifetime loss factor to the decision unit so that the decision unit can comprehensively consider fuel consumption cost and energy storage unit loss cost in power allocation optimization.

[0041] It should be noted that the target mapping table matches the current operating condition category and the interval corresponding to the first feature, and is used to provide the query parameters required to calculate the initial life loss factor; It should be noted that the initial lifetime degradation factor is used to reflect the degree of lifetime degradation caused by the unit energy throughput of the energy storage unit under the current operating conditions and status. It should be noted that the current operating condition information can only reflect the ship's real-time operating status, while the first feature can reveal the trend and fluctuation of future load demand. Therefore, by introducing the first feature into the determination of the target mapping table, the matching deviation caused by relying solely on the current operating condition can be avoided, thereby making the assessment of the life loss factor more accurate. It should be noted that the second feature is used to reflect the dynamic characteristics and duration of load changes, thereby performing time-varying correction on the initial lifetime loss factor.

[0042] Furthermore, by deploying a loss assessment unit, dynamic quantitative assessment of energy storage unit lifespan loss can be achieved. This allows the energy optimization processing module to move away from relying on static empirical models in power allocation decisions, instead combining current operating condition information, predicting future load characteristics of the unit output, and energy storage unit state parameters to generate a lifespan loss factor that changes in real time with operating conditions. This not only ensures the accuracy and foresight of energy storage unit lifespan degradation assessment but also makes the decision-making unit more scientific and reasonable in balancing fuel consumption costs and energy storage unit loss costs. Compared to existing technologies, this significantly improves the system's adaptability to complex navigation scenarios, reduces total lifecycle maintenance costs, and enhances the safety and economy of collaborative operation between ship shaft generators and energy storage units.

[0043] Example 5, as Figure 1 and Figure 2 As shown, specifically: The energy optimization processing module also includes a decision-making unit; The decision-making unit calculates fuel consumption cost based on the power output of the power generation unit and the fuel price; it calculates energy storage unit loss cost based on the energy storage unit's state parameters, lifetime loss factor, and energy throughput. The sum of fuel consumption cost and energy storage unit loss cost is used as the optimization objective, and a load demand power signal is introduced to form a power balance constraint condition, that is, the load demand power signal is equal to the sum of the target output power value signal of the power generation unit and the target charging and discharging power value signal of the energy storage unit.

[0044] The decision-making unit also includes a calculator, which has a first input port, a second input port and a third input port.

[0045] The first input port is connected to a fuel consumption cost related signal, which is calculated by combining the output power of the power generation unit with the fuel price and the efficiency characteristics of the power generation unit. The second input port is connected to the energy storage unit loss cost related signal, which is calculated by comprehensively considering the energy storage unit state parameters, lifetime loss factor, and energy throughput. The third input port is connected to the load demand power signal, which is output by the prediction unit.

[0046] The calculator can solve the optimization problem based on the strategy of minimizing equivalent consumption to obtain the target output power signal of the power generation unit and the target charge / discharge power signal of the energy storage unit. In another embodiment, the calculator can also use dynamic programming, model predictive control, or other optimization algorithms to solve the problem and obtain the target output power signal of the power generation unit and the target charge / discharge power signal of the energy storage unit. The calculator converts the target power signal into a control command and sends it to the power control module for execution.

[0047] It should be noted that the control commands include commands for controlling the power generation unit and commands for controlling the energy storage unit; It should be noted that the first input port is used to provide fuel consumption cost related signals, reflecting the economic cost of the power generation unit's output per unit power under current operating conditions; It should be noted that the second input port is used to provide signals related to the energy storage unit's loss cost, reflecting the lifespan degradation cost of the energy storage unit under unit energy charge and discharge. It should be noted that the third input port is used to provide the load demand power signal to ensure that the optimization process meets the ship's immediate energy needs. It should be noted that the equivalent consumption minimization strategy achieves synergistic optimization by integrating the signals from the three sources, thus avoiding the problem of existing technologies that only consider fuel economy while ignoring energy storage lifespan loss.

[0048] Furthermore, by setting up a decision-making unit, both fuel consumption costs and energy storage unit lifespan degradation factors can be considered simultaneously during power allocation. This ensures that the optimization results are not limited to simple fuel economy but also take into account the full life-cycle benefits of the energy storage unit. Based on the lifespan degradation factor from the degradation assessment unit, the decision-making unit dynamically constrains the charge-discharge depth and frequency of the battery under different operating conditions, effectively avoiding excessive degradation caused by over-cycling and deep discharge. This not only extends the lifespan of the energy storage unit and reduces the operation and maintenance costs caused by frequent battery replacements, but also enables the system to achieve a balance between economy and durability in energy optimization management. Compared with existing technologies, this significantly improves the safety and reliability of the coordinated operation of ship shaft generators and energy storage units.

[0049] Example 6, as Figure 1 and Figure 2 As shown, specifically: The energy optimization processing module also includes a feedback unit; The feedback unit is electrically connected to the loss assessment unit and the decision unit. It receives lifetime loss factors and energy storage unit status parameters, and performs real-time cumulative calculations and safety threshold determinations on these parameters. The feedback unit calculates the instantaneous loss value based on the lifetime loss factor and the energy throughput rate in the energy storage unit status parameters, and accumulates these values ​​over time to obtain the cumulative loss value. When the cumulative loss value exceeds a preset threshold, the feedback unit generates an alarm signal and sends it to the decision unit. The decision unit then generates the highest priority safety command, including controlling the energy storage unit to exit the operating mode and controlling the power generation unit to independently handle all load demand.

[0050] It should be noted that the power generation unit includes power generation equipment that is in regular operation and power generation equipment that is not in operation. After the energy storage unit exits the working mode, the power generation equipment that is in regular operation will bear the full load power demand. If the power generation equipment that is in regular operation cannot bear the full load power demand, the power generation equipment that is not in operation will be started to supplement the remaining load power demand. It should be noted that the lifetime loss factor is obtained by the loss assessment unit; It should be noted that the energy storage unit state parameters include energy throughput rate, charge / discharge rate and temperature drift, wherein the energy throughput rate is used to characterize the energy flow intensity of the energy storage unit under different load conditions. It should be noted that the preset threshold can be set according to the rated life design value of the energy storage unit or the actual operation and maintenance strategy. Its value range can be 70% to 90% of the total life loss, so as to achieve a balance between safety and availability.

[0051] Furthermore, the feedback unit can establish a real-time loss monitoring and accumulation judgment mechanism based on the lifespan loss factor and the energy storage unit's state parameters. This ensures that the energy optimization processing module is not only guaranteed in terms of economic optimization (a comprehensive balance between fuel consumption costs and energy storage unit loss costs), but also possesses proactive protection capabilities in terms of safety and reliability. When the feedback unit detects that the accumulated loss value is approaching a preset threshold, it can promptly trigger a safe exit logic and issue the highest priority instruction to the decision-making unit, thereby preventing excessive degradation or sudden failure of the energy storage unit. Compared to existing technologies that rely on single operating indicators such as voltage and current to trigger protection, the feedback unit uses the lifespan loss factor and energy throughput rate as core criteria to achieve early warning and dynamic adjustment under multiple operating conditions. This ensures that the system protected by the claims achieves fuel economy and extended energy storage lifespan while further improving the overall safety, robustness, and continuous energy supply capability during operation.

[0052] Example 7, as Figure 3 As shown, this application provides a method for a multi-condition energy optimization management system that coordinates ship shaft power generation with lithium batteries. The specific steps of the method include: First, the operating parameters and external information of the ship's propulsion system are collected. The operating parameters include the status parameters of the energy storage unit and the power generation unit, and the external information includes weather conditions and navigation plans. The energy storage unit status parameters reflect the remaining available capacity, energy throughput rate, and battery health level of the energy storage unit; the power generation unit status parameters reflect the available power output and fuel consumption efficiency of the power generation unit.

[0053] Secondly, within a preset prediction period threshold, the future load demand curve of the ship is predicted by combining the operating parameters and external information. This prediction result can identify power demand fluctuation trends for future voyages in advance, avoiding efficiency losses caused by passive responses from power generation or energy storage units.

[0054] Then, the ship's current operating condition is identified based on the operating parameters to obtain current operating condition information. Different operating conditions (such as low-speed berthing, cruising, and frequent acceleration and deceleration) have different focuses on energy allocation strategies. For example, under frequent acceleration and deceleration conditions, priority is given to scheduling energy storage units to reduce instantaneous fuel consumption, while under constant-speed cruising conditions, priority is given to utilizing the high-efficiency power range output of the power generation units.

[0055] Then, based on the prediction results, the current operating condition information, and the energy storage unit's state parameters, the lifetime loss factor of the energy storage unit is determined. This lifetime loss factor quantifies the lifetime degradation of the energy storage unit under specific depths of charge and energy throughput rates, providing a lifetime constraint basis for power allocation.

[0056] Subsequently, based on the lifespan loss factor and the state parameters of the power generation unit, control commands are generated to adjust the output power of the power generation unit and the charging and discharging power of the energy storage unit. These control commands aim to reduce total cost, which includes fuel consumption cost and energy storage unit lifespan loss cost. This control command establishes a dynamic balance between fuel economy and energy storage lifespan, preventing excessively shortened energy storage unit lifespan due to simply reducing fuel consumption.

[0057] The control commands also include safety commands. These safety commands, as additional battery safety logic, have the highest priority and are used to trigger the energy storage unit to shut down when it detects over-temperature, near-limit state of charge, or excessive lifetime degradation factor. These safety commands further ensure system safety while ensuring the optimization goals are achieved.

[0058] Finally, the control command is input into the power control module to execute the power allocation between the power generation unit and the energy storage unit, thereby achieving energy-saving operation under multiple operating conditions and protecting the lifespan of the energy storage unit.

[0059] Furthermore, the method of deploying a multi-condition energy optimization management system that coordinates ship shaft generators and lithium batteries has the advantage of ensuring a stable power supply to the ship's power system while achieving dynamic power optimization allocation between the generator and energy storage units, thereby reducing the overall operating costs of fuel consumption and energy storage unit lifespan loss. Simultaneously, the inclusion of a high-priority safety command in the control instructions ensures that the energy storage unit can be promptly deactivated when it experiences overheating, approaches its state of charge limit, or exceeds its lifespan loss factor, preventing thermal failure due to overuse and effectively reducing the probability of major accidents such as battery explosions and fires, thus improving the safety and reliability of the ship's power system. Compared to existing technologies that only aim for energy efficiency optimization while neglecting energy storage unit lifespan and safety factors, this method achieves energy saving and consumption reduction while also considering battery life extension and safety risk control, resulting in a comprehensive optimization effect that combines economy and safety.

[0060] The foregoing has shown and described the basic principles, main features, and advantages of this application. Those skilled in the art should understand that this application is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this application. Various changes and modifications can be made to this application without departing from the spirit and scope thereof, and all such changes and modifications fall within the scope of this application as claimed. The scope of protection of this application is defined by the appended claims and their equivalents.

Claims

1. A multi-condition energy optimization management system for ship shaft-driven power generation and lithium battery coordination, characterized in that, The system includes: a data acquisition module, an energy optimization processing module, and a power regulation module; The energy optimization processing module is electrically connected to the data acquisition module and is used to receive operating parameters and external information of the ship's power system from the data acquisition module. The operating parameters include energy storage unit status parameters and power generation unit status parameters. The energy optimization processing module includes: The prediction unit is used to predict the ship's load demand curve within a preset prediction period threshold based on the operating parameters of the ship's power system and the external information, and send the prediction result to the loss assessment unit. The operating condition identification unit is used to identify the current operating condition of the ship based on the operating parameters of the ship's power system, and send the current operating condition to the loss assessment unit. The loss assessment unit is used to determine the lifetime loss factor of the energy storage unit based on the received prediction results, the current operating conditions and the energy storage unit state parameters, and send the lifetime loss factor to the decision unit. The decision unit receives the lifetime loss factor and the state parameters of the power generation unit, and determines the control command for adjusting the output power of the power generation unit and the charging and discharging power of the energy storage unit based on the lifetime loss factor and the state parameters of the power generation unit, so that the power control module realizes the power distribution between the power generation unit and the energy storage unit in the ship's power grid based on the control command.

2. The system according to claim 1, characterized in that, The loss assessment unit is specifically used to: calculate a lifetime loss factor reflecting the degree of lifetime degradation caused by the unit energy throughput of the energy storage unit based on the state parameters of the energy storage unit, correct the lifetime loss factor based on the prediction results to obtain an adjusted lifetime loss factor, and send the adjusted lifetime loss factor to the decision unit.

3. The system according to claim 1, characterized in that, The operating condition identification unit is specifically used for: The system receives operating parameters of the ship's power system from the data acquisition module. The operating parameters of the ship's power system also include the ship's speed. The status parameters of the power generation unit include the output power of the power generation unit. The ship's current operating condition is identified based on its speed and the output power of the power generation unit, and the current operating condition is sent to the loss assessment unit.

4. The system according to claim 3, characterized in that, The loss assessment unit has multiple built-in mapping tables, which are used to store lifetime loss factor query parameters under different operating conditions and different prediction characteristics. The loss assessment unit is also used for: Receive the current operating condition identified by the operating condition identification unit; The prediction result is received, and a first feature and a second feature are extracted based on the prediction result. The first feature includes at least one of future average load power, future load power fluctuation rate and future peak power. The second feature includes at least one of load ramp rate, load duration and load mutation number. Based on the current operating conditions and the first feature, determine the target mapping table from the plurality of mapping tables; The initial lifetime loss factor is calculated based on the energy storage unit state parameters and the query parameters corresponding to the target mapping table. The initial lifetime loss factor is corrected based on the second feature to obtain the adjusted lifetime loss factor. The lifetime loss factor is used to characterize the degree of lifetime degradation of the energy storage unit under different operating conditions and serves as the basis for the decision-making unit to optimize power allocation and protect lifetime.

5. The system according to claim 1, characterized in that, The decision-making unit is specifically used to: determine the fuel consumption cost based on the preset fuel price and the output power of the power generation unit, and determine the energy storage unit loss cost based on the life loss factor and the energy throughput of the energy storage unit. Using the sum of the fuel consumption cost and the energy storage unit loss cost as the optimization objective, the target output power value signal of the power generation unit and the target charging and discharging power value signal of the energy storage unit are obtained by performing optimization calculations. Control commands are generated based on the target output power value signal of the power generation unit and the target charge / discharge power value signal of the energy storage unit.

6. The system according to claim 5, characterized in that, The decision-making unit also includes a calculator for performing optimization calculations. After performing optimization calculations, the calculator obtains the target output power value signal of the power generation unit and the target charging and discharging power value signal of the energy storage unit. The calculator includes a first input port, a second input port, and a third input port; the first input port is used to receive a first signal related to fuel consumption costs, the second input port is used to receive a second signal related to energy storage unit loss costs, and the third input port is used to receive a load demand power signal from the ship's electrical grid. The calculator is specifically used to: based on the equivalent minimum consumption strategy, under the constraint of the load demand power signal, fuse the first signal and the second signal, and output the target output power value signal of the power generation unit and the target charge / discharge power value signal of the energy storage unit. The constraint of the load demand power signal is that the load demand power signal received by the third input port is equal to the sum of the target output power value signal of the power generation unit and the target charge / discharge power value signal of the energy storage unit.

7. The system according to claim 5, characterized in that, The decision-making unit generates a first instruction for regulating the power generation unit based on the target output power value signal of the power generation unit. Based on the target charge / discharge power value signal of the energy storage unit, a second command for regulating the energy storage unit is generated; The first instruction and the second instruction together constitute the control instruction.

8. The system according to claim 1, characterized in that, The energy optimization processing module further includes a feedback unit, which is electrically connected to the loss assessment unit and the decision unit, receives the lifetime loss factor from the loss assessment unit, and receives the energy storage unit status parameters from the data acquisition module. The energy storage unit status parameters also include the energy throughput rate of the energy storage unit. The feedback unit is specifically used to: determine the loss value based on the lifetime loss factor and the energy throughput rate, and obtain the cumulative loss value by accumulating the loss value; If the accumulated loss value exceeds a preset threshold, an alarm signal is output and input to the decision unit so that the decision unit generates a safety instruction based on the alarm signal. The safety instruction is set as the highest priority control instruction among the control instructions, used to control the energy storage unit to stop working and control the power generation unit to bear all the load requirements of the ship's power system.

9. The system according to claim 3, characterized in that, The operating condition identification unit is used to identify multiple operating conditions, including one or more of acceleration, deceleration, cruising, loading and unloading, and severe sea conditions.

10. A method for multi-condition energy optimization management of ship shaft-driven power generation and lithium battery coordination, characterized in that, Applied to the system as described in any one of claims 1-9, the method comprises: Collect operating parameters and external information of the ship's power system, including energy storage unit status parameters and power generation unit status parameters; Within a preset prediction period threshold, the ship's load demand curve is predicted based on the operating parameters and the external information as the prediction result; The current operating condition of the ship is identified based on the operating parameters, and the current operating condition information is obtained; Based on the prediction results, the current operating condition information, and the energy storage unit status parameters, the lifespan loss factor of the energy storage unit is determined. Based on the lifetime loss factor and the state parameters of the power generation unit, a control command is generated to adjust the output power of the power generation unit and the charging and discharging power of the energy storage unit. The control command also includes a safety command, which has the highest priority and is used to control the energy storage unit to exit operation. The power distribution between the power generation unit and the energy storage unit is adjusted according to the control command.

Images