An energy distribution optimization method for a hybrid energy storage system of an electric vehicle

Abstract

The application discloses a kind of electric vehicle hybrid energy storage system energy distribution optimization method, according to traffic flow characteristic parameter determines current traffic flow type, corresponding determination hybrid energy storage system energy distribution mode: under the condition of congestion flow, power battery alone provides driving demand electric energy, and energy battery charges power battery;Under the condition of limit flow, power battery and energy battery jointly provide driving demand electric energy;When vehicle accelerates under the condition of free flow, the same energy distribution mode as limit flow condition is used;After vehicle reaches desired speed, energy battery alone provides driving demand electric energy;Wherein, the charging power of energy battery to power battery, charging SOC upper limit, the power distribution ratio of common output, are adjusted according to the driving style of driver.The application prolongs the service life of lithium ion battery while improving system efficiency as far as possible, realizes optimal energy distribution.

Description

Technical Field

[0001] This invention belongs to the field of new energy vehicle technology, specifically relating to an energy distribution optimization method for electric vehicle hybrid energy storage systems based on driving style and real-time road traffic information. Background Technology

[0002] As one of the core technologies of electric vehicles, batteries face increasingly higher demands due to the rapid development of electric vehicles. Currently, lithium iron phosphate and ternary lithium-ion batteries, which account for the largest installed base in the electric vehicle market, are typical energy-type lithium-ion batteries, possessing advantages such as high energy density, high energy conversion efficiency, and low self-discharge rate. While these energy-type batteries meet the high energy density requirements of electric vehicle energy storage systems, high power density is necessary when electric vehicles frequently encounter situations requiring high-power charging and discharging, such as acceleration, hill climbing, and regenerative braking. Another type of power battery, represented by supercapacitors, possesses high power density and low internal resistance, making it suitable for high-current discharge, but its lower energy density cannot meet the long-range requirements of electric vehicles. In recent years, power battery technology has continuously developed, achieving improved energy density at the cost of some power density, lifespan, and cost. Examples include lithium-ion capacitors and hybrid electrode batteries, whose laboratory performance shows a significant improvement in energy density compared to traditional power batteries.

[0003] Therefore, hybrid energy storage systems composed of power batteries and energy batteries can effectively meet the high energy density and high power density requirements of today's electric vehicles and effectively extend the battery system's lifespan. Because hybrid energy storage systems are more complex, a reasonable energy distribution strategy needs to be designed to effectively leverage the advantages of both power batteries and energy batteries and improve energy utilization efficiency. This is the key and challenge of energy distribution strategies for hybrid energy storage systems in electric vehicles.

[0004] Currently, commonly used energy allocation methods are mainly divided into rule-based methods and optimization-based methods. Rule-based methods include logic threshold control, filtering, and fuzzy control. These methods are simple and direct, but their allocation effect depends on the established rules. Optimization-based methods include dynamic programming, model predictive control, and particle swarm optimization. These methods achieve better allocation results but have high computational costs and are based on offline data optimization, resulting in poor real-time performance. Furthermore, with the development of machine learning and the advent of the big data era, artificial neural networks and deep learning methods are also being applied to the research of energy allocation strategies for hybrid energy storage systems. However, their allocation effect relies on long-term training with effective data, and their black-box nature makes it difficult to reasonably adjust the allocation results. Therefore, achieving both good allocation performance and simplicity and ease of implementation is a pressing problem to be solved in hybrid energy storage system allocation strategies. Summary of the Invention

[0005] This invention provides an energy distribution optimization method for electric vehicle hybrid energy storage systems based on driving style and real-time road traffic information, which maximizes system efficiency while extending the lifespan of lithium-ion batteries and achieving optimal energy distribution.

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

[0007] An energy distribution optimization method for electric vehicle hybrid energy storage systems based on driving style and real-time road traffic information includes:

[0008] First, based on traffic flow characteristic parameters, the current traffic flow type of vehicles traveling on the road is determined as: congested flow, restricted flow, or free flow;

[0009] Then, the energy allocation method of the hybrid energy storage system is determined according to the traffic flow type:

[0010] (1) In congested traffic conditions, the power battery outputs power to provide electric energy for the electric vehicle to drive, and the energy battery charges the power battery.

[0011] (2) Under the current restriction condition, the power battery and the energy battery work together to provide power for the electric vehicle to drive according to the preset power distribution ratio; at the same time, when the SOC of the power battery is lower than the charging limit, the energy battery charges the power battery.

[0012] (3) Under free flow conditions, there are two further scenarios: when the vehicle is accelerating, the hybrid energy storage system adopts the same energy distribution method as under restricted flow conditions; after the vehicle reaches the desired speed or is set to cruise speed, the energy-type battery outputs power separately to provide electricity for the electric vehicle to drive.

[0013] Among them, the charging power of the energy type battery to the power type battery, the upper limit of the charging SOC of the power type battery, and the preset power distribution ratio when they are output together are determined according to the driver's driving style and the power demand of the motor, the SOX state of the energy type battery and the power type battery, and the predicted energy consumption of the driving section.

[0014] Furthermore, the traffic flow characteristic parameters include traffic flow volume, vehicle speed, and traffic flow density. The specific logic for determining the traffic flow type is as follows:

[0015] Let the traffic flow volume and vehicle speed at the vehicle's current location be Q and V, respectively. Calculate the traffic flow density K at the current location:

[0016]

[0017] like If the current traffic flow type of the vehicle is on the road, it is determined to be congested flow; otherwise, it is determined to be restricted flow or free flow; where S is the driver's habitual following distance and s0 is the driver's safe stopping distance.

[0018] If the flow is determined to be restricted or free-flowing, then it is further determined whether the road traffic flow density at this time satisfies K < K. free If the conditions are met, it is determined to be a free flow; otherwise, it is determined to be a restricted flow; where K free For free traffic flow density, v free This represents the driver's desired vehicle speed.

[0019] Furthermore, the driver's desired vehicle speed v free , is the driver's free-driving speed unaffected by other vehicles on the road, calculated according to the following formula:

[0020] v free =ζ·v L

[0021] In the formula, v L ζ represents the maximum speed limit for the road segment, and ζ is the percentage of a driver's speed when freely driving on roads with different speed limits, calculated from historical driving data.

[0022] Furthermore, the driver's driving style is categorized into three types: aggressive, cautious, and standard, based on energy consumption-related driving style characteristic parameters from the driver's historical electric vehicle driving data.

[0023] Furthermore, under congested traffic conditions, the charging power P of the energy type battery to the power type battery under standard driving style is determined based on the current state of charge (SOX) of the power type battery and the energy type battery. SC_C The upper limit of the power battery charging SOC under standard driving style is determined based on the predicted energy consumption of the driving route. SC_U Among them, SOC SC_U <SOC SC_max SOC SC_max Indicates the upper limit of the SOC of a power battery;

[0024] For aggressive driving styles, increase the charging power and charging SOC limit of power-type batteries:

[0025]

[0026] In the formula, P RSC_C and SOC SC_RU These represent the charging power and maximum SOC of the power-type battery for aggressive driving styles; λ RP and δ RSOCThese are the optimized parameters for the charging power of the power-type battery in an aggressive driving style, and the upper limit of the charging SOC, both with values ​​greater than 1; and the SOC... SC_RU ≤SOC SC_max ;

[0027] For cautious driving styles, reduce the charging power and SOC limit of power-type batteries:

[0028]

[0029] In the formula, P CSC_C and SOC SC_CU These are the charging power and maximum SOC of the power-type battery for a cautious driving style; λ CP and δ CSOC These are the optimized parameters for the charging power of the power-type battery and the upper limit of the charging SOC for a cautious driving style, respectively, and both values ​​are less than 1.

[0030] Furthermore, during vehicle acceleration under restricted flow and free flow conditions, an energy distribution strategy is employed to determine the energy-generating battery output power P under the standard driving style. LIB and power type battery output power P SC ;

[0031] For aggressive driving styles, increase the output power of the power battery and decrease the output power of the energy battery, and increase the SOC limit of the power battery:

[0032]

[0033] SOC SC_RU =δ RSOC SOC SC_U

[0034] In the formula, P RLIB and P RSC To distinguish between the energy-type battery output power and the power-type battery output power that provide electrical energy for electric vehicles under aggressive driving styles; P m η is the power required for electric vehicle operation. RP Optimization parameters for energy-type battery output power for aggressive drivers, 0 < η RP <1; SOC SC_RU Maximum State of Charge (SOC) for power-type batteries designed for aggressive driving styles; SOC SC_U Maximum SOC of power-type battery charging for standard driving style; δ RSOC The optimized parameters for the SOC (State of Charge) limit of power-type batteries designed for aggressive driving styles are all greater than 1; and the SOC... SC_RU <SOC SC_max SOC SC_maxIndicates the upper limit of the SOC of a power battery;

[0035] For cautious drivers, reduce the output power of the power battery and increase the output power of the energy battery, and reduce the SOC limit of the power battery:

[0036]

[0037] SOC SC_CU =δ CSOC SOC SC_U

[0038] In the formula: P CLIB and P CSC To differentiate between the energy-type battery output power and the power-type battery output power that provide electrical energy for electric vehicles under a cautious driving style; η CP η is the optimal parameter for the output power of the energy-type battery for cautious drivers. CP >1; δ CSOC The optimized parameters for the SOC (State of Charge) limit of power-type batteries for cautious driving styles are all less than 1; and the SOC... SC_CU <SOC SC_max .

[0039] Furthermore, braking energy is recovered by the power-type battery in all traffic flow types.

[0040] Furthermore, when the SOC of a power battery reaches its upper limit... SC_max At that time, the power type battery will charge the excess power to the energy type battery.

[0041] Furthermore, when the power battery reaches its maximum charging SOC, the energy battery stops charging the power battery.

[0042] Furthermore, the power batteries in the hybrid energy storage system use supercapacitors, while the energy batteries use lithium-ion batteries.

[0043] Beneficial effects

[0044] Compared with existing hybrid energy storage system energy distribution strategies, this invention, based on the traditional hybrid energy storage system energy distribution strategy, comprehensively considers two factors that significantly affect the energy consumption of electric vehicles: driver's driving style and traffic flow conditions on the road. Furthermore, it applies a series topology to the traditional parallel topology of hybrid energy storage systems, offering at least the following advantages:

[0045] (1) It can effectively adapt to the road traffic conditions of drivers and vehicles, so that the energy distribution strategy of the hybrid energy storage system can be flexibly adjusted and optimized according to driving segmentation and road traffic information, thereby realizing the intelligentization of the hybrid energy storage management system.

[0046] (2) The energy distribution strategy of the hybrid energy storage system is matched with the driver and road traffic conditions. While ensuring the original advantage of extending the life of lithium-ion batteries, the system's energy utilization rate can be improved more effectively, and the driving range of electric vehicles can be increased.

[0047] (3) The addition of a charging optimization strategy for power batteries improves the capacity utilization of power batteries in the hybrid energy storage system and helps to reduce the capacity parameters of power batteries, thereby reducing the manufacturing cost of the hybrid energy storage system. Attached Figure Description

[0048] Figure 1 This is an improved topology diagram of an embodiment of the energy distribution method for an electric vehicle hybrid energy storage system based on driving style and real-time road traffic information described in this specification.

[0049] Figure 2 This is a schematic diagram of the energy distribution method for an electric vehicle hybrid energy storage system based on driving style and real-time road traffic information as described in this specification.

[0050] Figure 3 This is a logic flowchart of the energy distribution method for electric vehicle hybrid energy storage systems based on driving style and real-time road traffic information as described in this specification.

[0051] Figure 4 This is a schematic diagram of energy flow in various modes of the improved hybrid energy storage system topology described in this specification. Detailed Implementation

[0052] The embodiments of the present invention will be described in detail below. These embodiments are based on the technical solutions of the present invention and provide detailed implementation methods and specific operation processes to further explain the technical solutions of the present invention.

[0053] This invention proposes an energy distribution method for a hybrid energy storage system of power-type and energy-type batteries in electric vehicles based on driving style and real-time road traffic information. In this embodiment, the power-type battery is a supercapacitor, and the energy-type battery is a lithium-ion battery such as lithium iron phosphate or ternary lithium. First, the road conditions for vehicle travel are classified into different types based on traffic flow characteristic parameters. Then, under each traffic flow state, the output ratio of lithium-ion batteries and supercapacitors in the hybrid energy storage system of the electric vehicle is adjusted and optimized according to different driver driving styles. This extends the lifespan of lithium-ion batteries while maximizing system efficiency, achieving optimal energy distribution.

[0054] like Figure 1As shown, taking a hybrid energy storage supercapacitor semi-active topology as an example, the improved topology scheme adds K1 and K2 to control the on / off switching of the lithium-ion battery and supercapacitor with the DC bus, thereby enabling switching between parallel mode, supercapacitor-only output mode, and lithium-ion battery-only output mode. Simultaneously, since the supercapacitor's terminal voltage is positively correlated with its SOC, and the lithium-ion battery's operating voltage is relatively stable, the supercapacitor's SOC can be exceeded by controlling the series and parallel connection parameters of the lithium-ion battery pack and the supercapacitor pack. SC_max (SOC SC_max When the voltage is less than 1), the terminal voltage of the supercapacitor is greater than the operating voltage of the lithium-ion battery. After switch K3 is closed, the supercapacitor can use the excess power to charge the lithium-ion battery. When the SOC of the supercapacitor is lower than the SOC... SC_max At this time, the terminal voltage of the supercapacitor is lower than the operating voltage of the lithium-ion battery, thus enabling the lithium-ion battery to charge the supercapacitor when switch K3 is closed.

[0055] like Figure 2 The diagram shows the energy distribution method of the electric vehicle hybrid energy storage system based on driving style and real-time road traffic information described in this embodiment. The traffic flow state layer obtains real-time traffic information of the road the vehicle is currently traveling on through the database of the intelligent transportation big data system, including traffic flow, vehicle speed, and traffic flow density near the vehicle's location. The traffic flow state near the vehicle is divided into congested flow, restricted flow, and free flow: (1) In congested flow, the vehicle speeds are very low, the traffic flow density within the road segment is high, and the traffic flow is very low. At this time, the vehicle will frequently accelerate and decelerate and move forward slowly. (2) In restricted flow, the traffic flow in the road can reach a certain value, and the vehicle can travel at a relatively smooth speed. However, due to the certain traffic flow density in the road, the vehicle speed will be restricted by other vehicles in the road. (3) In free flow, the traffic flow density in the road is very low. The vehicle is no longer restricted by other vehicles while driving. The driver can freely accelerate to a comfortable desired speed according to their own driving style. When the vehicle speed reaches the desired speed or the cruise speed is set, the vehicle will no longer significantly accelerate or brake until the traffic flow state changes.

[0056] Therefore, according to Figure 3 The judgment logic in the energy allocation method shown determines the traffic flow type: Based on the characteristics of different traffic flow states, let the traffic flow volume and vehicle speed on the current road be Q and V, respectively, and calculate the traffic flow density K near the current road:

[0057]

[0058] like If the current traffic flow type of the vehicle is on the road, it is determined to be congested flow; otherwise, it is determined to be restricted flow or free flow. Where S is the driver's habitual following distance and s0 is the driver's safe stopping distance.

[0059] If the flow is determined to be restricted or free-flowing, then it is further determined whether the road traffic flow density at this time satisfies K < K. free If the conditions are met, it is determined to be a free flow; otherwise, it is determined to be a restricted flow.

[0060] Among them, K free For free traffic flow density, v free Let be the driver's desired speed, and be the driver's free-driving speed unaffected by other vehicles on the road. Considering that the vehicle will pass through roads with different speed limits along the entire route, the desired speed will be calculated using the following formula:

[0061] v free =ζ·v L

[0062] In the formula, v L ζ represents the maximum speed limit for the road segment, and ζ is the percentage of a driver's speed when freely driving on roads with different speed limits, calculated from historical driving data.

[0063] For energy distribution layers, such as Figure 2 As shown, the system first switches K1, K2, and K3 toggle the operating mode of the hybrid energy storage system based on the traffic flow type of the current road. Then, based on driving style characteristic parameters related to electric vehicle energy consumption, such as the vehicle's maximum speed, maximum acceleration, maximum deceleration, average acceleration, average deceleration, and acceleration and braking pedal change rates from historical driving data, statistical models and artificial neural networks are used to classify three driving style types: aggressive, cautious, and standard. A multi-objective optimization method is then employed to obtain the optimization parameters for each driving style regarding lithium-ion battery life, system efficiency, manufacturing cost, and energy consumption. The weighting rules for different driving styles towards the optimization objectives are as follows:

[0064] (1) The aggressive driving style focuses on optimizing the lifespan of lithium-ion batteries;

[0065] (2) The cautious driving style focuses on optimizing system efficiency;

[0066] (3) All three driving styles need to further optimize the manufacturing cost and energy consumption of the hybrid energy storage system while meeting the energy needs of the driver for normal driving.

[0067] Based on predicted energy consumption, the power required for supercapacitor charging and the upper limit of SOC are determined under standard driving conditions in congested and restricted traffic to ensure that the supercapacitor has sufficient power for acceleration.

[0068] Then, based on the optimization parameters for aggressive and cautious driving styles, the energy distribution results for the standard driving style, the power required for charging the supercapacitor, and the SOC upper limit are adjusted to obtain the optimal output control strategy for the lithium-ion battery and the supercapacitor. Furthermore, braking energy is recovered by the supercapacitor.

[0069] The following will combine Figure 3 The various modes in the logic flowchart further illustrate the energy allocation layer.

[0070] (1) Mode 1: In this congested flow state, in the improved hybrid energy storage system topology, switches K2 and K3 are closed, and switch K1 is open, switching to the supercapacitor-only output mode, such as... Figure 4 As shown in (a).

[0071] Taking the standard driving style as an example, the supercapacitor outputs its required power P to the motor separately through a bidirectional DC / DC converter. m (P m >0), that is, P m =P SC By analyzing the current state of energy (SOX) of power-type and energy-type batteries and predicting energy consumption, the ability of lithium-ion batteries to operate at a certain power (P) during deceleration and braking can be obtained. SC_C Charge the supercapacitor to a certain SOC value. SC_U (SOC SC_U <SOC SC_max ),like Figure 4 As shown in (b), in order to ensure that the supercapacitor has enough power to ensure the next acceleration, this mode can prevent the lithium-ion battery from outputting high power during the frequent acceleration and deceleration of the electric vehicle, which would cause the capacity to decay rapidly.

[0072] For aggressive driving styles, which have faster start-up speeds and higher instantaneous power and energy consumption, the charging power and SOC limit of supercapacitors should be increased to conserve the electrical energy stored in the hybrid energy storage system.

[0073]

[0074] In the formula, λ RP and δ RSOC These are the optimized parameters for supercapacitor charging power and charging SOC upper limit under aggressive driving style. Based on the optimization weights for energy-type battery life and system efficiency under aggressive driving style, a multi-objective optimization method was used to obtain these parameters. All values ​​are greater than 1 and the SOC is... SC_RU≤SOC SC_max SOC SC_max This indicates the upper limit of the SOC of a power battery.

[0075] For cautious driving styles, which have slower start-up speeds and lower instantaneous power demand and energy consumption, the charging power and SOC limit of the supercapacitor should be reduced to conserve the electrical energy stored in the hybrid energy storage system.

[0076]

[0077] In the formula, λ CP and δ CSOC These are the supercapacitor charging power and charging SOC upper limit optimization parameters for the cautious driving style. Based on the optimization weights for energy-type battery life and system efficiency under the cautious driving style, they are obtained using a multi-objective optimization method, and their values ​​are all less than 1.

[0078] (2) Mode 2: In this mode, the flow is restricted. In the improved hybrid energy storage system topology, switches K1 and K2 are closed, and switch K3 is open. This switches to a parallel connection mode of lithium-ion batteries and supercapacitors. The lithium-ion batteries and supercapacitors provide power to the electric vehicle according to a certain power allocation ratio. The power allocation ratio can be determined using existing energy allocation strategies such as rule-based, optimization-based, and machine learning-based approaches, thereby obtaining the energy-type battery output power P. LIB and power type battery output power P SC .

[0079] Taking the standard driving style as an example, the lithium-ion battery and the supercapacitor work together to output the required power P to the motor. m (P m >0), that is, P m =P SC +P LIB ,like Figure 4 As shown in (c). However, when the power demand is low, the supercapacitor output P is not required. SC When the value is 0, in the improved hybrid energy storage system topology, switches K1 and K3 are closed, and switch K2 is open. The lithium-ion battery maintains the power P required by the motor. m Simultaneously charge the supercapacitor to a certain SOC value. SC_L That is, P SC_C =P LIB -P m ,like Figure 4 As shown in (d).

[0080] For aggressive drivers, to prevent high-power discharge of the lithium-ion battery and ensure that the supercapacitor stores sufficient energy to meet the driver's acceleration needs, thereby improving the overall efficiency of the system and saving energy stored in the hybrid energy storage system, the output power of the supercapacitor should be increased while the output power of the lithium-ion battery should be reduced.

[0081]

[0082] In the formula η RP The supercapacitor output power optimization parameter for aggressive drivers has a value less than 1. It is obtained using a multi-objective optimization method based on the optimization weights for energy-type battery life and system efficiency according to the aggressive driving style.

[0083] For cautious drivers, in contrast to aggressive driving styles, to improve overall system efficiency and conserve the energy stored in the hybrid energy storage system, the output power of the supercapacitor should be reduced while the output power of the lithium-ion battery should be increased.

[0084]

[0085] In the formula η CP The supercapacitor output power optimization parameter for cautious drivers has a value greater than 1. It is obtained by using a multi-objective optimization method based on the optimization weights of energy-type battery life and system efficiency according to the cautious driving style.

[0086] In both aggressive and cautious driving styles, the method for obtaining and optimizing the upper limit of the supercapacitor's charging SOC is the same as in Mode 1.

[0087] (3) Mode 3: This is a free-flow state, and the vehicle speed has reached the driver's desired speed or the cruise speed v. free The vehicle no longer experiences significant acceleration and braking; its power demand decreases and stabilizes. In the improved hybrid energy storage system topology, switch K1 is closed, while switches K2 and K3 are open. The lithium-ion battery-powered unidirectional motor outputs its required power P. m (P m >0), that is, P m =P LIB ,like Figure 4 As shown in (e), the topology of the hybrid energy storage system is switched to a lithium-ion battery-only output mode. The lithium-ion battery provides power to the vehicle at a relatively stable power output, while the supercapacitor no longer outputs power but only recovers a small amount of braking energy to conserve its stored energy. In this mode, driving style has almost no impact on energy consumption, so no further adjustments or optimizations are needed.

[0088] In free-flow conditions, the vehicle reaches the desired speed or the cruise speed v. freeThe initial free acceleration process is the same as in Mode 2.

[0089] (4) Mode 4: In this mode, the improved hybrid energy storage system topology has switch K2 closed and switches K1 and K3 open. Under all traffic flow conditions, braking energy is recovered by the supercapacitor, i.e., P... S ' C =P m '(P m <0), until the SOC upper limit of the supercapacitor. SC_max ,like Figure 4 As shown in (f).

[0090] (5) Mode 5: When the SOC of the supercapacitor reaches the upper limit SOC SC_max In the improved hybrid energy storage system topology, switches K2 and K3 are closed, and switch K1 is open. The supercapacitor charges excess power to the lithium-ion battery to prevent overcharging of the supercapacitor. Figure 4 As shown in (g).

[0091] It should be noted that the above description is only a detailed explanation of the preferred embodiments and principles of the present invention. For those skilled in the art, there may be changes in the specific implementation based on the ideas provided by the present invention, and these changes should also be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing energy distribution in an electric vehicle hybrid energy storage system based on driving style and real-time road traffic information, characterized in that, include: First, based on traffic flow characteristic parameters, the current traffic flow type of vehicles traveling on the road is determined as: congested flow, restricted flow, or free flow; Then, the energy allocation method of the hybrid energy storage system is determined according to the traffic flow type: (1) In congested traffic conditions, the power battery outputs power to provide electric energy for the electric vehicle to drive, and the energy battery charges the power battery; (2) Under the current restriction condition, the power battery and the energy battery work together to provide power for the electric vehicle to drive according to the preset power distribution ratio; at the same time, when the SOC of the power battery is lower than the charging limit, the energy battery charges the power battery. (3) Under free flow conditions, there are two further scenarios: when the vehicle is accelerating, the hybrid energy storage system adopts the same energy distribution method as under restricted flow conditions; after the vehicle reaches the desired speed or is set to cruise speed, the energy-type battery outputs power separately to provide electricity for the electric vehicle. Among them, the charging power of the energy type battery to the power type battery, the upper limit of the charging SOC of the power type battery, and the preset power distribution ratio when they are output together are determined according to the driver's driving style and the motor's power demand, the battery SOX status of the energy type battery and the power type battery, and the predicted energy consumption of the driving section. The drivers' driving styles are categorized into three types: aggressive, cautious, and standard, based on energy consumption-related driving style characteristic parameters from the drivers' historical electric vehicle driving data. During vehicle acceleration under restricted flow and free flow conditions, an energy distribution strategy is employed to determine the energy-generating battery output power under standard driving style. and power type battery output power ; For aggressive driving styles, increase the output power of the power battery and decrease the output power of the energy battery, and increase the SOC limit of the power battery: ； ； In the formula, and To distinguish between the energy-type battery output power and the power-type battery output power that provide electrical energy for electric vehicles with an aggressive driving style; The power required for electric vehicles to operate. Optimize parameters for the energy-type battery output power for aggressive drivers. ; Maximum SOC for power batteries designed for aggressive driving styles; The maximum SOC (State of Charge) for power-type batteries in standard driving styles; The optimized parameters for the SOC limit of power-type batteries for aggressive driving styles are all greater than 1; and , Indicates the upper limit of the SOC of a power battery; For cautious drivers, reduce the output power of the power battery and increase the output power of the energy battery, and reduce the SOC limit of the power battery: ； ； In the formula: and To differentiate between the energy-type battery output power and the power-type battery output power that provide electrical energy for electric vehicles to operate under a cautious driving style; Optimize the output power parameters of the energy-type battery for cautious drivers. ; The optimized parameters for the SOC limit of power-type batteries charged in a cautious driving style are all less than 1; and .

2. The method according to claim 1, characterized in that, The traffic flow characteristic parameters include traffic flow volume, vehicle speed, and traffic flow density. The specific logic for determining the traffic flow type is as follows: Let the traffic flow volume and vehicle speed at the vehicle's current location be Q and V, respectively. Calculate the traffic flow density at the current location. : ； like If the current traffic flow type is determined to be congested flow, then it is determined to be restricted flow or free flow; otherwise, it is determined to be restricted flow or free flow. Based on the driver's habit of maintaining a safe following distance, To ensure a safe stopping distance for drivers; If it is determined to be restricted flow or free flow, then it is further determined whether the road traffic flow density at this time meets the requirements. If the conditions are met, it is determined to be a free flow; otherwise, it is determined to be a restricted flow. For free traffic flow density, , This represents the driver's desired vehicle speed.

3. The method according to claim 2, characterized in that, Driver's desired speed , is the driver's free-driving speed unaffected by other vehicles on the road, calculated according to the following formula: ； In the formula, This is the maximum speed limit for the section of road. This refers to the percentage of a driver's speed relative to the speed limit when driving freely on roads with different speed limits, calculated from the driver's historical driving data.

4. The method according to claim 1, characterized in that, In congested traffic conditions, the charging power of the energy type battery to the power type battery under standard driving style is determined based on the current state of charge (SOX) of both the power type battery and the energy type battery. The upper limit of the power battery charging SOC under standard driving style is determined based on the predicted energy consumption of the driving route. ;in, , Indicates the upper limit of the SOC of a power battery; For aggressive driving styles, increase the charging power and charging SOC limit of power-type batteries: ； In the formula, and These are the charging power and maximum SOC of the power-type battery for aggressive driving styles. and These are the optimized parameters for the charging power of the power-type battery and the upper limit of charging SOC, respectively, both with values ​​greater than 1; and ; For cautious driving styles, reduce the charging power and SOC limit of power-type batteries: ； In the formula, and These are the charging power and maximum SOC of the power-type battery for a cautious driving style. and These are the optimized parameters for the charging power of the power-type battery and the upper limit of the charging SOC for a cautious driving style, respectively, and both values ​​are less than 1.

5. The method according to claim 1, characterized in that, Braking energy is recovered by the power battery under all traffic flow conditions.

6. The method according to claim 1, characterized in that, When the SOC of a power battery reaches its upper limit At that time, the power type battery will charge the excess power to the energy type battery.

7. The method according to claim 1, characterized in that, When the power battery reaches its maximum charging SOC, the energy battery stops charging the power battery.

8. The method according to claim 1, characterized in that, In hybrid energy storage systems, the power batteries use supercapacitors, while the energy batteries use lithium-ion batteries.

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