Hess energy management control method and device, vehicle and medium

Abstract

The application discloses an energy management control method and device, a vehicle and an HESS energy management control method, and comprises the following steps: uploading HESS state parameters of a vehicle to a cloud end by a vehicle end; dynamically planning a future driving condition of the vehicle by using real-time collected traffic information, and obtaining a speed-time curve and a slope-time curve; setting a time value, extracting a future driving condition of the vehicle in a time period of a preset time value as available working condition information segments; obtaining an optimal battery SOC control sequence in the available working condition information segments according to the HESS state parameters of the vehicle and the available working condition information segments by the cloud end; and performing energy management on the HESS according to the obtained optimal battery SOC control sequence by the vehicle end. The application effectively solves the energy management problem of the HESS in a complex and changeable environment, improves the energy utilization efficiency and economy of the vehicle, and prolongs the service life of key components.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of new energy vehicles, and particularly relates to a HESS energy management control method. BACKGROUND

[0002] Developing new energy buses is an important measure for China to build green and smart cities. As an important part of urban public transportation systems, pure electric buses have advantages such as zero emissions and low noise, but also have development bottlenecks such as short endurance mileage and long charging time. Improving the economy and practicality of pure electric buses is of great significance to break through the bottleneck of their popularization and application. Single battery energy storage is the earliest energy storage solution applied to pure electric buses, and the technology is relatively mature and has been widely used, but it has the disadvantages of low power density and limited cycle life. Compared with the traditional single battery energy storage solution, the hybrid energy storage system (HESS) composed of batteries and supercapacitors can have the advantages of high energy density and high power density, and can improve the economy under different operating conditions by reasonably distributing energy flow.

[0003] HESS has multiple different operating modes, which can generally be divided into battery-dominated mode, supercapacitor-dominated mode and collaborative working mode. How to make operating mode decisions and optimize energy distribution strategies is the key to improving the energy efficiency of hybrid energy storage systems under different operating conditions. There are related researches in this field at present, which can be summarized as: 1) rule-based method and 2) optimization-based method.

[0004] The rule-based HESS control strategy is a strategy that allocates power to the battery and supercapacitor according to the real-time vehicle driving power demand using pre-set rules. This method sets a series of fine logical rules based on expert experience and prior knowledge to realize real-time control and optimization of the hybrid energy storage system. According to the nature of the rules, it can be further divided into two types: deterministic rule-based and fuzzy rule-based.

[0005] In the strategy based on deterministic rules, the "if-then-else" mode is usually used, and accurate threshold values and logical rules are set according to system state variables such as battery state of charge (SOC), demand power, vehicle speed, etc.

[0006] For example, ZHAO et al. (ZHAO Y, WANG W, XIANG C, et al. Research and bench test of nonlinear model predictive control-based power allocation strategy for hybrid energy storage system. IEEE Access, 2018, 6(1): 70770–70787) used a logic threshold strategy to limit battery power by strictly setting the battery SOC range and demand power conditions. When the SOC is lower than a certain threshold or the demand power exceeds the preset range, the excess power is provided by the supercapacitor.

[0007] Fuzzy rule-based strategies introduce fuzzy logic control, mapping input variables to fuzzy sets and deriving control decisions through fuzzy inference, thus achieving more flexible and intelligent control. For example, Zhou Meilan et al. (Zhou Meilan, Feng Jifeng, Zhang Yu, et al. Research on power distribution control strategy of composite energy storage system for pure electric bus. Journal of Electrical Engineering, 2019, 34(23):5001–5013) used power demand, battery SOC and supercapacitor SOC as input variables, and designed fuzzy membership functions and inference rules to output the power distribution ratio of battery and supercapacitor.

[0008] Specifically, the strategy converts precise input variables (such as power demand and battery SOC) into fuzzy language descriptions (such as "low", "medium", "high"), and then makes decisions based on preset "if-then" rules, such as "if power demand is low and battery SOC is high, then the battery will be used for power supply". The system converts the fuzzy decision into a specific power allocation ratio between the battery and the supercapacitor.

[0009] The rule-based HESS control strategy described above has the following limitations:

[0010] 1) There is a contradiction between the rigidity of preset rules and the dynamism of actual applications. Since the control strategy is based on a pre-established rule set, it is difficult to adjust in real time during practical applications. This rigid decision-making mechanism may prevent the system from achieving optimal performance when faced with complex and changing driving conditions. Especially under some extreme or unforeseen system conditions, the preset rules may fail to respond appropriately, thus affecting overall performance.

[0011] 2) Difficulty in achieving global optimization of multiple objectives. Rule-based methods usually focus on a single or limited number of optimization objectives, such as maximizing energy efficiency or minimizing power loss. However, the actual operation of HESS involves multiple interrelated objectives such as energy consumption, battery life, component performance, etc. It is difficult to seek the best balance between these objectives by relying solely on pre-set rules, which may lead to performance improvement in some aspects at the expense of others.

[0012] 3) There is a trade-off between the complexity of rule design and control accuracy. In order to improve control accuracy, it is often necessary to design more complex and detailed rule sets. However, the increase in the number of rules not only increases the computational load of the system, but also may cause conflicts or overlaps between rules, increasing the difficulty of strategy design and debugging. In addition, fixed rule-based control strategies are difficult to adapt to the dynamic changes of HESS performance parameters (such as battery capacity, internal resistance, etc.) with use time and environmental conditions, which may lead to gradual deterioration of control effect over time.

[0013] Optimization-based HESS control strategies are designed to achieve optimal power distribution between battery and supercapacitor and other energy storage devices through control optimization technology. The core idea of this method is to transform the control problem of hybrid energy storage system into a typical optimization control problem, by setting appropriate objective functions (such as minimizing energy consumption, minimizing operating cost, etc.) and constraint conditions (such as battery power limit, etc.), using various optimization algorithms to solve the optimal control strategy. According to the time scale and information utilization of optimization, this type of strategy can be further divided into two types: global optimization-based method and real-time optimization-based method.

[0014] Based on global optimization methods, the theoretically optimal power distribution strategy is calculated considering the energy demand and system state throughout the entire driving cycle for known complete driving cycles. For example, Wang et al. (Wang T, Deng W, Wu J, Zhang Q. Power optimization for hybrid energy storage system of electric vehicle [C]. 2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), 2014: 1-6) used the Dynamic Programming (DP) algorithm to minimize the total energy consumption as the objective function to optimize the power distribution of the battery and supercapacitor throughout the entire driving cycle. Powade et al. (Powade R, Bhateshvar Y. Design of semi-actively controlled battery-supercapacitor hybrid energy storage system [J]. Materials Today: Proceedings, 2023, 72(3): 1503-1509) converted the HESS energy management problem into a multi-objective and solved it using the particle swarm optimization algorithm, taking into account factors such as energy efficiency, battery life, and system cost.

[0015] Based on real-time optimization methods, the locally optimal control decision is quickly calculated by using an instantaneous cost function instead of a global cost function, based on the current system state and short-term prediction information, which is suitable for real-time control in actual driving. For example, Wang (Wang L. Energy management strategy and capacity configuration optimization of hybrid energy storage system for vehicles [D]. University of Science and Technology of China, 2022) used the Model Predictive Control (MPC) method to predict the system behavior in a short period of time (called the prediction horizon) based on the current system state (such as battery SOC, supercapacitor SOC, vehicle speed, etc.) at each control time. MPC uses the system model to make multi-step predictions for this short period of time and obtains the optimal control sequence by solving a local optimization problem. Specifically, it takes the cumulative instantaneous cost function (such as instantaneous energy consumption) in the prediction horizon as the optimization objective, while considering system constraints (such as power limits, etc.), so that real-time performance is guaranteed while also considering the future impact to some extent.

[0016] The above-mentioned HESS control strategy based on optimization has the following limitations: 1) The practical application of the global optimization method is limited. Due to the inability to obtain complete global driving condition information in actual driving, it is difficult to apply the method based on global optimization in practice. Although the driving condition in the future can be obtained by prediction method, there is inevitably deviation between the predicted condition and the actual condition. This is because the actual driving environment has strong randomness and uncertainty, such as sudden traffic incidents, weather changes and other factors, which often cause the actual local driving condition to deviate significantly from the planned predicted condition, thereby affecting the effectiveness of the optimization result. Especially in some extreme or unexpected system conditions, the optimization strategy based on the predicted condition may not be able to respond appropriately, thereby affecting the overall performance. 2) Real-time optimization method is difficult to achieve economic optimization. The method based on real-time optimization usually makes short-term prediction according to the current system state and power demand, and assumes that the power demand remains constant during the deduction process. This simplification assumption, although improving the calculation efficiency, also leads to the inability to fully utilize the future condition information, thereby making it difficult to achieve global economic optimization. Especially in the case of rapid changes in driving conditions, it may lead to frequent adjustment of control decisions, affecting the stability and energy efficiency of the system. SUMMARY

[0017] The present application proposes a HESS energy management control method based on vehicle-cloud integration, aiming to realize a control strategy combining global optimization and real-time self-adaptation to improve the economy, energy utilization efficiency and battery life of HESS.

[0018] To solve the above technical problems, the technical solution adopted by the present application is:

[0019] The HESS energy management control method based on vehicle-cloud integration comprises:

[0020] The vehicle end uploads the HESS state parameters of the vehicle to the cloud end; the cloud end dynamically plans the future driving condition of the vehicle using the real-time collected traffic information to obtain a speed-time curve and a slope-time curve; a time value is set, and the future driving condition of the vehicle before the set time value in the planned future driving condition of the vehicle is extracted as a usable condition information segment;

[0021] The cloud end obtains the optimal battery SOC control sequence within the usable condition information segment according to the HESS state parameters of the vehicle and the usable condition information segment;

[0022] The vehicle end performs energy management on the HESS according to the obtained optimal battery SOC control sequence.

[0023] The HESS energy management control method based on vehicle-cloud integration provided by the present application integrates global optimization of the cloud end and real-time control of the vehicle end. There are three different aspects from other technical solutions:

[0024] 1) Different from the prior art I, the present application plans the global driving condition using dynamic traffic information collected in the cloud, and solves the HESS optimal state sequence within the available driving condition information segment through the DP algorithm. The vehicle end uses the HESS optimal state reference value issued to control the power output of the battery and the super capacitor, realizing the prospective control of fusing future driving condition information. This design can adapt to different driving conditions, and seek a balance between energy consumption, battery life, and component performance within the available driving condition information segment, rather than controlling the HESS according to the principle of optimal at that moment, overcoming the limitations of strong rigidity and difficulty in achieving multi-target global optimization of the rule-based method.

[0025] 2) Different from the prior art II, the present application adopts a vehicle-cloud collaborative architecture, performs global optimization in the cloud, and uses the MPC algorithm for real-time adaptive adjustment at the vehicle end. This design not only overcomes the problem that global optimization methods are difficult to obtain complete condition information in practical applications, but also solves the limitations of real-time optimization methods that are difficult to achieve global economic optimization. By processing the power demand errors caused by traffic environment uncertainty disturbances and prediction driving condition errors in real time at the vehicle end, the present application can effectively cope with randomness and uncertainty in the actual driving environment, ensuring that the system can maintain high efficiency and safe operation when deviating from the planned global condition.

[0026] 3) Different from the prior arts I and II, the present application has a dynamic weight adjustment mechanism that can dynamically adjust the weights of electric energy consumption cost and battery degradation cost in real time according to key factors such as battery SOH, environmental temperature, and system efficiency. This adaptive adjustment not only considers the current operating state, but also takes into account the long-term performance evolution of the HESS, thereby realizing comprehensive optimization control of the system throughout its life cycle. By dynamically balancing short-term operating efficiency and long-term life extension, the problem of traditional methods being difficult to balance immediate benefits and long-term benefits in multi-objective optimization is effectively solved.

[0027] The control system of the present application plans the global driving condition based on dynamic traffic information in the cloud, and calculates the optimal HESS state control sequence within the available condition information segment through the dynamic programming algorithm. The vehicle end uses the MPC algorithm to track the HESS state reference value issued by the cloud in real time, and makes local adjustments to cope with the uncertainty of the actual driving environment. This architecture realizes an effective balance between long-term optimization goals and short-term response needs, improving the adaptive ability and robustness of the HESS in complex and variable environments.

[0028] The updated global driving scenario front segment is used as the available working condition information segment, and the DP algorithm is periodically executed in the cloud to obtain the HESS optimal state control sequence within the available working condition information segment. The available working condition information segment adopts a rolling update mechanism to ensure that the latest traffic information is always used in the optimization process. This dynamic optimization strategy can respond to changes in traffic conditions in a timely manner, while considering battery life and electricity consumption costs, and achieving prospective minimization of the total cost in the future time domain.

[0029] The HESS energy management control method based on vehicle-cloud integration designed by the application has the following remarkable beneficial effects:

[0030] 1) Global optimization and forward-looking control: The dynamic traffic information collected by the cloud is used to plan the global driving scenario, and the DP algorithm is used to solve the available working condition information segment to obtain the HESS optimal state sequence, so that the control strategy focuses on minimizing the total HESS cost of the available working condition information segment, rather than only considering the current cost. This forward-looking control ensures that the super capacitor module can provide sufficient energy supply when the instantaneous power demand of the vehicle is high, effectively preventing the battery from performing high-power charging and discharging, thereby significantly improving the expected service life of the battery.

[0031] 2) Real-time adaptation and robustness: In view of the randomness and uncertainty in the actual driving environment (such as sudden traffic incidents, weather changes, etc.), the application uses the MPC algorithm at the vehicle end to track the battery SOC reference value issued by the cloud, which enables the HESS to handle power demand prediction errors caused by uncertain disturbances in real time, while balancing the long-term and short-term benefits of extending the battery life. By combining global optimization with local adjustment, the application can respond to environmental changes in real time and adaptively adjust the power output of the energy source, significantly improving the economy and energy utilization efficiency of the vehicle in complex and variable environments.

[0032] 3) Dynamic weight adjustment and life cycle optimization: The adaptive weight adjustment method designed by the application dynamically adjusts the weights of electricity consumption cost and battery degradation cost according to the battery SOH, environmental temperature and system efficiency. This mechanism not only considers the current health status of the battery, but also takes into account the influence of external environment and system performance, thereby achieving optimal cost-benefit balance throughout the life cycle of the HESS. This comprehensive consideration ensures that the system can maintain high efficiency in different stages and environments, maximizing the economic benefit and service life of the HESS.

[0033] In summary, the energy management control method proposed in the application effectively solves the energy management problem of HESS in a complex and variable environment by combining global optimization and local adjustment, forward-looking control and real-time self-adaptation, and dynamic weight adjustment. This multi-level and multi-dimensional control strategy not only improves the energy utilization efficiency and economy of the vehicle, but also prolongs the service life of the key components. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 Typical semi-active HESS configuration schematic diagram

[0035] Figure 2 HESS energy management control strategy framework based on vehicle-cloud integration

[0036] Figure 3 DP algorithm rolling execution schematic diagram DETAILED DESCRIPTION

[0037] The technical solutions of the application will be described in detail below with reference to the drawings:

[0038] The HESS system configuration is shown in Figure 1 The HESS system includes a battery, a super capacitor, and a bidirectional DC / DC inverter. The super capacitor realizes output or supplement of energy through the bidirectional DC / DC inverter, allowing the voltage of the super capacitor to change in a wide range. The battery is directly connected to the DC bus, so that the voltage fluctuation of the DC bus is small. The HESS has the following three working modes: 1) super capacitor alone power supply, 2) battery alone power supply, and 3) super capacitor and battery cooperative power supply. The application designs an energy management control strategy for a typical semi-active HESS. When the vehicle is running, the HESS adjusts the energy output ratio of the battery and the super capacitor in real time according to the driving power demand, realizes dynamic switching of different working modes. The energy output by the two is superimposed through the bus, and then transmitted to the DC / AC inverter through the DC bus. The DC / AC inverter converts the direct current into three-phase alternating current and applies it to the driving motor. The motor generates torque after receiving the electric energy, drives the wheels to rotate, and realizes the continuous driving of the vehicle.

[0039] HESS energy management control strategy design

[0040] Establish a bus longitudinal dynamics model:

[0041]

[0042] Where i is the main reduction ratio; η mec is the mechanical transmission efficiency; T m,req is the required motor torque; R wWhere is the wheel radius; m is the vehicle mass; g is the gravitational constant; f is the rolling resistance coefficient; θ is the road slope; C D ρ is the drag coefficient; A is the frontal area; ρ is the air density; v is the vehicle speed; δ is the rotational mass conversion factor; a is the vehicle acceleration.

[0043] Establish a battery dynamic model:

[0044]

[0045] in, I represents the change in battery SOC. bat Q represents the battery current. bat This refers to the battery's nominal capacity; V bat R is the battery open-circuit voltage. bat P is the battery's internal resistance. bat This refers to the battery's output power.

[0046] The capacity degradation Q of lithium iron phosphate batteries is calculated using a semi-empirical model for capacity decay. loss :

[0047]

[0048] Where B1 is the pre-exponential factor under 1C discharge rate conditions, with a value of 27672; R represents the ideal gas constant, with a value of 8.314 J / (mol·K); T is the Kelvin temperature of the battery operating environment; and n is the battery discharge rate, n = I. bat / Q bat A h1 With A hn These are the cumulative ampere-hours under 1C / nC discharge rate conditions. The absolute value of the current is used in the calculation to prevent the charging situation during driving from being missed.

[0049] When the voltage of a supercapacitor drops to half of its maximum voltage, it no longer has the ability to discharge. Therefore, the state of charge (SOC) of a supercapacitor is:

[0050]

[0051] Among them, V SC V is the current voltage of the supercapacitor. max This is the maximum voltage of the supercapacitor; V min This is the minimum voltage of a supercapacitor;

[0052] Establish a dynamic model of the supercapacitor:

[0053]

[0054] in, I represents the change in SOC of the supercapacitor. SCQ is the super capacitor current; V SC V is the super capacitor nominal capacity; V SC V is the super capacitor open circuit voltage; V SC R is the super capacitor internal resistance; Ω SC P is the super capacitor output power.

[0055] The actual operating condition of the bus often deviates from the preset global driving condition due to the complex and changeable traffic environment. This deviation is caused by the random uncertainty and time-varying characteristics of the traffic environment, which causes the vehicle to be unable to strictly follow the pre-planned driving condition. In fact, the vehicle driving condition can be regarded as the organic combination of driving condition segments in different time domains during the process from the starting point to the end point. In view of this, the present application proposes a HESS energy management control strategy of vehicle-cloud integration, aiming to improve the economic efficiency of semi-active HESS, in which the random change characteristics of the traffic environment and the factor of only being able to use part of the future driving condition information are considered, and the control framework is as shown in Figure 2

[0056] Dynamic traffic information is collected through an intelligent transportation system, and the cloud periodically plans and updates the global driving condition of the vehicle from the current position to the end point according to the collected dynamic traffic information, to obtain a speed-time curve and a slope-time curve. For example, the remaining global driving condition from the current position of the vehicle to the end point is updated once every 300 seconds (not limited to 300 seconds, but can be any value between 100 seconds and 500 seconds). This means that from the start of driving, the cloud refreshes the remaining driving condition of the vehicle on the preset route every 300 seconds. After each update, the driving condition of the initial 300 seconds will be used as the available driving condition information segment for the vehicle in the next 300 seconds. After the vehicle drives for 300 seconds according to the available driving condition information segment, the cloud updates the driving condition using newly collected traffic information and generates a new driving condition information segment for the continuous driving of the vehicle.

[0057] Planning and updating the global driving condition of the vehicle from the current position to the end point is prior art in the field, see CN108177648A. The present application controls the hybrid energy storage system of the bus based on the obtained dynamically updated global driving condition.

[0058] At the same time, the cloud uses the currently available driving condition information segment and HESS state parameters as input, and executes the DP algorithm using the longitudinal vehicle dynamics model of the bus. DP is executed once every 60 seconds, to solve the optimal battery SOC control sequence within the available driving condition information segment, and the result is issued to the vehicle end as a reference value. This process continues to roll and execute until the vehicle reaches the end point, as shown in Figure 3 Then, the vehicle end controls the battery SOC using the MPC algorithm, to track the reference value issued by the cloud in real time, and to realize the optimal power distribution between the battery and the super capacitor. ​

[0059] The optimization objective of the DP algorithm is to minimize the power consumption of the HESS internal resistance and the battery capacity degradation cost, and the corresponding objective function is:

[0060]

[0061] where λ1' and λ2' are the modified HESS internal resistance power consumption cost weight and battery capacity degradation cost weight, respectively; k represents the time at time k; N pre is the end time of the available driving cycle information segment; T S is the sampling step; n bat is the number of battery cells; n SC is the number of supercapacitor cells; C ele is the electricity price; C bat is the battery capacity degradation cost; A h1,20％ is the cumulative ampere-hour quantity when the battery capacity degrades by 20% under 1C discharge conditions.

[0062] Subject to system dynamics:

[0063]

[0064] and the following constraints:

[0065]

[0066] where P dem,ref is the predicted vehicle driving demand power, obtained according to the updated global driving cycle of the vehicle from the current position to the end point; P SCDC,ref is the predicted supercapacitor connected DC / DC end output power; P bat,ref is the predicted battery output power; η DCDC is the DC / DC efficiency; I SC,max and I SC,min are the maximum / minimum current of the supercapacitor; I bat,max and I bat,min are the maximum / minimum current of the battery; SOC SC,max and SOC SC,min are the maximum / minimum SOC of the supercapacitor; SOC bat,max and SOC bat,min are the maximum / minimum SOC of the battery; n m is the driving motor speed; η m is the driving motor efficiency; v ref is the speed information in the available driving cycle information segment; θ ref is the slope information in the available driving cycle information segment.

[0067] Due to the random uncertainty of traffic environment and the existence of prediction error, the actual power demand of the vehicle may deviate from the cloud prediction value. In order to accurately respond to the actual demand, the present application determines the accurate power demand by analyzing the accelerator pedal input at the vehicle level, so that the HESS can more accurately supply energy. At the vehicle end, the MPC algorithm is used to design the HESS energy management strategy, which is used to track the optimal battery SOC reference value issued by the cloud, and the characteristics of the super capacitor are used to realize "peak clipping and valley filling" and compensate for the remaining power demand fluctuations. The optimization objective of the MPC algorithm is to minimize the following error between the actual battery SOC and the reference value issued by the cloud, and to minimize the internal resistance power consumption of the HESS. In addition, the MPC also considers the working characteristics of the battery and the super capacitor, the current SOC state and the system constraint conditions to ensure the stability of the energy management strategy. The objective function of the HESS energy management power distribution strategy based on MPC is:

[0068]

[0069] Wherein, α represents the battery SOC tracking error weight coefficient; β and γ respectively represent the internal resistance power consumption weight coefficients of the battery and the super capacitor; N P represents the prediction time domain; k+i|k represents the relevant state of the k+i time predicted at the k time; SOC bat,cloud represents the optimal battery SOC reference value; R bat (SOC bat (k)) and R SC (SOC SC (k)) respectively represent the internal resistance of the battery and the super capacitor corresponding to the k time.

[0070] Subject to system dynamics:

[0071]

[0072] And constraints:

[0073]

[0074] Wherein, P dem represents the actual vehicle demand power; P SCDC represents the output power of the DC / DC end connected to the super capacitor.

[0075] Adaptive adjustment mechanism

[0076] The application is based on the real-time adjustment of the weight coefficients of the HESS electric energy consumption cost and the battery attenuation cost in the cloud DP algorithm objective function (6) according to the battery state of health (SOH) to realize the adaptive balance of the short-term operation cost and the long-term battery life. The battery SOH is 1 in the brand-new state and 0 when reaching the end of life, so SOH ∈ [0, 1]. With the decrease of the battery SOH, the battery capacity attenuation cost weight λ2 can be gradually reduced, and the HESS internal resistance power consumption cost weight λ1 can be increased. This shows that when the battery SOH is low, the control strategy is more inclined to optimize the current operation efficiency of the HESS, rather than excessively focusing on the extension of the battery life. Based on the above adjustment strategy, the application adopts the following form of weight parameter adaptive adjustment mechanism:

[0077]

[0078] Wherein, λ1 and λ2 are the HESS internal resistance power consumption cost weight and the battery capacity attenuation cost weight respectively; k1 and k2 are the weight change slopes of the power consumption cost and the battery attenuation cost respectively; b1 and b2 are the basic weight values of the power consumption cost and the battery attenuation cost respectively, which ensure reasonable weight distribution at extreme SOH values. In order to ensure that the sum of the weight coefficients is always 1, the calculated weights are normalized:

[0079]

[0080] Wherein, λ 1,norm and λ 2,norm are the normalized HESS internal resistance power consumption cost weight and the battery attenuation cost weight respectively. In addition, the application also considers the influence of the environmental temperature (T) and the HESS operation efficiency (η) on the weight, and introduces the correction factor:

[0081]

[0082] Wherein, f T and f η are the correction factors of the environmental temperature and the HESS operation efficiency respectively; λ3 and λ4 are the influence coefficients of the temperature and the efficiency respectively; T ref and η ref are the reference temperature and the reference efficiency respectively.

[0083] In summary, the adaptive adjustment calculation formula of the corrected HESS internal resistance power consumption cost weight and the battery capacity attenuation cost weight is:

[0084]

[0085] Through this adaptive mechanism, the application can dynamically adjust the weights of the power consumption cost and the battery degradation cost according to the real-time changes of the battery SOH, the ambient temperature and the system efficiency, so as to achieve the optimal cost-benefit balance in the whole life cycle of the HESS. This mechanism not only considers the current health status of the battery, but also takes into account the influence of the external environment and the system performance.

[0086] The application designs an energy management control strategy for a single-motor driven pure electric bus equipped with a semi-active HESS. It should be pointed out that the energy management control strategy of the hybrid energy storage system proposed in the application has wide applicability and is not limited to single-motor driven systems, but is also applicable to multi-motor driven buses or other types of electric vehicles.

[0087] In the vehicle-cloud integrated hybrid energy storage system energy management strategy architecture proposed in the application, the remaining global driving cycle is planned and updated using dynamic traffic information in the cloud, and the first 300 seconds of the updated global driving cycle are used as the available driving cycle information segment. It should be pointed out that the 300-second time window is determined based on the current intelligent traffic system's ability to collect dynamic information and plan the global driving cycle. With the advancement of cloud computing technology and traffic information collection and uploading technology, or due to differences in equipment in different regions, this time window may change. However, the core idea of the application remains the same, which is to use the latest traffic information to predict the global driving cycle to optimize energy management. Similarly, the setting of executing the DP algorithm every 60 seconds in the cloud is the result of balancing real-time optimization and computational efficiency. This time interval may also change with the advancement of computing technology and differences in hardware equipment, but the core idea remains the same, which is to balance the optimization effect, real-time performance and computational resource constraints.

[0088] In the vehicle-cloud integrated HESS energy management strategy architecture proposed in the application, the MPC algorithm is used at the vehicle end to track the battery SOC reference value issued by the cloud to realize the use of future driving cycle information and the consideration of battery life. It should be pointed out that this strategy architecture has good flexibility and scalability. For example, replacing the tracking object from the battery SOC reference value to the supercapacitor SOC reference value can also effectively utilize future driving cycle information and optimize system performance. Whether tracking the battery SOC or the supercapacitor SOC, the core idea of the application is to utilize the vehicle-cloud collaborative architecture to effectively manage the HESS through predictive control methods, optimize energy distribution and consider long-term system performance.

[0089] The adaptive adjustment method provided by the application is to realize the adaptive adjustment of the weight parameter in the form of a linear function combined with the ambient temperature and the system efficiency correction factor. It is worth noting that the adaptive adjustment mechanism of the application can be replaced by other methods for adjusting the weight of the battery degradation cost and the power consumption cost based on the SOH change in real time, such as machine learning algorithms, fuzzy logic control, neural networks and the like, but the core idea is still consistent, that is, the weight is dynamically adjusted according to the battery SOH and other related factors, so as to realize the optimal cost-benefit balance in the whole life cycle of the HESS.

Claims

1. A HESS energy management control method based on vehicle-cloud integration, characterized in that, The method comprises the following steps: The vehicle end uploads the HESS state parameters of the vehicle to the cloud end; The cloud end dynamically plans the future driving conditions of the vehicle using real-time collected traffic information, and obtains a speed-time curve and a slope-time curve; A time value is set, and the future driving conditions of the vehicle in a time period of the set time value before the planning are extracted as a usable condition information segment; The cloud end obtains an optimal battery SOC control sequence in the usable condition information segment according to the HESS state parameters of the vehicle and the usable condition information segment; The vehicle end performs energy management on the HESS according to the obtained optimal battery SOC control sequence. The cloud end obtains an optimal battery SOC control sequence in the usable condition information segment according to the HESS state parameters of the vehicle and the usable condition information segment by using a DP algorithm, and the optimization objective of the DP algorithm is to minimize the power consumed by the HESS internal resistance and the battery capacity attenuation cost in the usable condition information segment, and the corresponding objective function is: wherein, and are the modified HESS internal resistance power consumption cost weight and battery capacity degradation cost weight, respectively; denotes the time at the kth moment; is the end time of the available operating condition information segment; is the sampling step; is the number of battery monomers; is the number of super capacitor monomers; and are the battery internal resistance and super capacitor internal resistance at the kth moment, respectively; and are the battery current and super capacitor current at the kth moment, respectively; is the battery discharge rate; is the electricity price; is the battery capacity degradation cost; is the cumulative ampere-hour quantity when the battery capacity degrades by 20% under 1C discharge conditions. subject to system dynamics: wherein, is the battery SOC at the kth moment; is the super capacitor SOC at the kth moment; is the battery current at the kth moment; is the super capacitor current at the kth moment; is the sampling period; is the battery current at the kth moment; is the super capacitor current at the kth moment; is the nominal capacity of the battery; is the nominal capacity of the super capacitor; is the maximum voltage of the super capacitor; is the minimum voltage of the super capacitor; subject to the following constraints: in, The predicted power demand for vehicle travel at time k; The output power of the supercapacitor connected to the DC / DC converter at time k. Let k be the battery output power at time k. and These are the battery open-circuit voltage and the supercapacitor open-circuit voltage at time k, respectively. and These are the battery current and the supercapacitor current at time k, respectively; and These are the internal resistance of the battery and the internal resistance of the supercapacitor at time k, respectively. and These represent the battery SOC and the supercapacitor SOC at time k, respectively. For DC / DC efficiency; This refers to the number of individual battery cells; This refers to the number of individual supercapacitor cells; and These are the battery's maximum and minimum currents, respectively. and These represent the maximum and minimum currents of the supercapacitor, respectively. and These represent the battery's maximum and minimum SOC, respectively. and These represent the maximum and minimum SOC of the supercapacitor, respectively. The cloud takes a set time period as a cycle, and periodically performs the DP algorithm combined with the latest HESS state information to obtain the first SOC state sequence from the moment to the end of the available operating condition information segment The SOC state change sequence of the HESS minimizing the operation cost, and further taking the SOC state change sequence of the battery as the optimal battery SOC control sequence: After the execution of the available operating condition information segment ends, the cloud generates a new available operating condition information segment, and the cycle is repeated to ensure the continuous driving of the vehicle. 2.The HESS energy management control method based on vehicle-cloud integration of claim 1, wherein, The modified HESS internal resistance power consumption cost weight The modified battery capacity degradation cost weight Respectively: wherein, and are the normalized HESS internal resistance electricity consumption cost weight and battery degradation cost weight, respectively; and are the ambient temperature and the correction factor for HESS operating efficiency, respectively. 3.The HESS energy management control method based on vehicle-cloud integration of claim 2, wherein, The adaptive adjustment weight calculation formula is: wherein, with are the HESS internal resistance electricity consumption cost weight and the battery capacity degradation cost weight, respectively; wherein, and are the weight variation slopes for the electricity consumption cost and the battery degradation cost, respectively; SOH is the state of health of the battery; and are the base weight values for the electricity consumption cost and the battery degradation cost, respectively, ensuring a reasonable weight distribution even at extreme SOH values; correction factor for ambient temperature T correction factor for HESS operating efficiency correction factor for HESS operating efficiency respectively wherein, and are the influence coefficients of temperature and efficiency, respectively; and are the reference temperature and reference efficiency, respectively. 4.The HESS energy management control method based on vehicle-cloud integration of claim 1, wherein, In the step of performing energy management on the HESS according to the obtained optimal battery SOC control sequence, the vehicle end designs an HESS energy management strategy by using an MPC algorithm, and the objective function of the MPC-based HESS energy management power distribution strategy is: wherein, represents the battery SOC tracking error weight coefficient; and respectively represent the battery and supercapacitor internal resistance power consumption weight coefficients; represents the prediction horizon; represents the battery SOC predicted at the k+ith moment at the kth moment; represents the optimal battery SOC reference value predicted at the k+ith moment at the kth moment; represents the battery current predicted at the k+ith moment at the kth moment; represents the supercapacitor current predicted at the k+ith moment at the kth moment; and respectively represent the battery internal resistance and supercapacitor internal resistance at the kth moment.

5. The HESS energy management control method based on vehicle-cloud integration according to claim 4, characterized in that, The system dynamics equation and the constraint condition for solving the objective function of the MPC-based HESS energy management power distribution strategy are respectively: System dynamics equation: wherein, and are the battery SOC at the kth moment / the k+1th moment, respectively; and are the battery current and the super capacitor current at the kth moment, respectively; is the sampling period; is the nominal capacity of the battery; and are the open-circuit voltage of the battery and the super capacitor at the kth moment, respectively; and are the internal resistance of the battery and the super capacitor at the kth moment, respectively; and are the output power of the battery and the super capacitor at the kth moment, respectively. Constraint condition: wherein, Pbat(k) is the actual vehicle demand power at the kth moment; Pbat(k) is the actual vehicle demand power at the kth moment; Pbat(k) is the actual vehicle demand power at the kth moment; Pbat(k) is the actual vehicle demand power at the kth moment; Pbat(k) is the actual vehicle demand power at the kth moment; Pbat(k) is the actual vehicle demand power at the kth moment; Pbat(k) is the actual vehicle demand power at the kth moment; Pbat(k) is the actual vehicle demand power at the kth moment; Pbat(k) is the actual vehicle demand power at the kth moment; Pbat(k) is the actual vehicle demand power at the kth moment; Pbat(k) is the actual vehicle demand power at the kth moment; Pbat(k) is the actual vehicle demand power at the kth moment; Pbat(k) is the actual vehicle demand power at the kth moment; Pbat(k) is the actual vehicle demand power at the kth moment; Pbat(k) is the actual vehicle demand power at the kth moment; 6.The HESS energy management control method based on vehicle-cloud integration of claim 1, wherein, The set time value is 100-500 seconds, and the future driving conditions of the vehicle planned in the first 100-500 seconds are extracted as the usable condition information segment.

7. The HESS energy management control method based on vehicle-cloud integration according to claim 6, characterized in that, The set time value is 300 seconds, and the future driving conditions of the vehicle planned in the first 300 seconds are extracted as the usable condition information segment.

8. An HESS energy management control device, characterized by, The cloud end and the vehicle end perform energy management on the HESS of the vehicle according to the HESS energy management control method based on vehicle-cloud integration according to any one of claims 1-7. The cloud end receives the HESS state parameters of the vehicle uploaded by the vehicle end, dynamically plans the future driving conditions of the vehicle using real-time collected traffic information, generates a usable condition information segment for the continuous driving of the vehicle in a set time period, obtains an optimal battery SOC control sequence in the usable condition information segment according to the HESS state parameters of the vehicle and the usable condition information segment, and sends the optimal battery SOC control sequence to the vehicle end. The vehicle end uploads the HESS state parameters of the vehicle to the cloud end, and performs energy management on the HESS according to the obtained optimal battery SOC control sequence.

9. A vehicle characterized by comprising: The HESS of the vehicle is managed by the HESS energy management control method based on vehicle-cloud integration according to any one of claims 1-7. 10.A computer readable storage medium, wherein a program or instructions are stored on the readable storage medium, and the program or instructions are executed by a processor to implement the steps of the HESS energy management control method based on vehicle-cloud integration according to any one of claims 1-7.

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