A hybrid energy storage based energy management method for electric vehicles

Abstract

This invention relates to the field of electric vehicle energy management technology and provides an electric vehicle energy management method based on hybrid energy storage. Based on the electric vehicle's speed, the power demand of the in-vehicle load, and the state of charge (SOC) of the battery and supercapacitor, a fuzzy control algorithm outputs a battery power allocation factor λ. Using the upper and lower limits of the battery and supercapacitor SOC and output power limitations as constraints, and with battery capacity degradation and operating cost per 100 kilometers as objective functions, a non-dominated sorting genetic algorithm is used to correct the battery power allocation factor λ in real time. This invention can utilize the characteristics of both battery and supercapacitor energy storage units to rationally allocate the required power and slow down battery capacity degradation.

Description

Technical Field

[0001] This invention belongs to the field of electric vehicle energy management technology, and particularly relates to an electric vehicle energy management method based on hybrid energy storage. Background Technology

[0002] Currently, my country's electric vehicle industry is developing rapidly. However, due to limitations in current battery technology, electric vehicles that rely solely on batteries as a single energy storage device still face limitations in driving range and cycle life. Supercapacitors, with their rapid charging and discharging, high energy density, and long cycle life, can be combined with batteries to form a composite energy storage system, extending battery life.

[0003] Hybrid energy storage management is one of the key technologies for electric vehicles. Optimizing the power distribution strategy between the two energy source devices is a crucial challenge for researchers, requiring the rational utilization of the energy storage characteristics of batteries and supercapacitors. Therefore, a hybrid energy storage-based energy management method for electric vehicles is needed to address these issues. Summary of the Invention

[0004] The purpose of this invention is to provide an energy management method for electric vehicles based on hybrid energy storage, which aims to solve the problems mentioned in the background art.

[0005] The present invention is implemented as follows: an energy management method for electric vehicles based on hybrid energy storage includes the following steps:

[0006] Step 1: Collect relevant data from inside the vehicle to determine the SOC (State of Charge) of the battery and supercapacitor, and the power P of the load inside the vehicle. load The electric vehicle's driving speed v and battery terminal voltage U bat ;

[0007] Step 2: Determine the output and parameters of the fuzzy logic controller: The inputs of the fuzzy logic controller are the battery SOC, the supercapacitor SOC, and the total power demand P. demand The output is the battery capacity allocation factor λ.

[0008] Step 3: Using one second as a unit time segment, collect the battery capacity allocation factor λ within the unit time segment. Calculate the allocated power of the battery and supercapacitor based on the battery power allocation factor λ and the total power demand. Using battery capacity degradation and operating cost per 100 kilometers as objective functions, and under the constraints of SOC and power output, use a non-dominated sorting genetic algorithm to solve the multi-objective optimization problem. Use the battery power allocation factor corresponding to the optimal solution as the initial battery power allocation factor for the next unit time segment to achieve the best allocation effect between the battery and supercapacitor.

[0009] In a further technical solution, in step 1, to ensure the normal operation of the energy storage unit, the upper and lower limits of the SOC of the battery and the supercapacitor are set as follows: 0.3 ≤ SOC bat ≤0.7, 0.2≤SOC sc ≤0.9, where SOC bat Indicates battery SOC, SOC sc This indicates the SOC (State of Charge) of the supercapacitor. bat >0.7, SOC sc If the SOC of one of the energy storage units is below the lower limit, the other energy storage device can charge it until the SOC is within the normal range. If the SOC of both units is below the lower limit, the energy storage system cannot work properly.

[0010] In a further technical solution, step 2 includes the following specific steps:

[0011] The input to the fuzzy logic controller is SOC. bat SOC sc and total power demand P demand The output is the battery capacity allocation factor λ. The total power demand P... demand The calculation formula is as follows:

[0012]

[0013]

[0014] P demand =P load +P demand0 (3)

[0015] Among them, f t P is the driving force for the longitudinal movement of the car. demand0 The power required by the power system is given by m, where m is the vehicle mass (kg) and g is the acceleration due to gravity (m / s²). 2 f is the rolling resistance coefficient, v is the vehicle speed, α is the road inclination angle, and ρ is the air density (kg / m³). 3 ), C D Where A is the drag coefficient and A is the frontal area (m²). 2 ), where δ is the rotational mass conversion factor, and η is the rotational mass conversion factor. t For the efficiency of the mechanical transmission system, η m For inverter and motor efficiency.

[0016] A further technical solution involves, in step 2, for ease of calculation, dividing the vehicle's power system demand by the maximum input and output power of the motor as the input to the fuzzy controller, with the fuzzy universe of discourse being [-1, 1]. SOCbat SOC sc The fuzzy universes of discourse for the battery power allocation factor λ are [0.3 0.7], [0.2,0.9] and [0,1], respectively.

[0017] In a further technical solution, step 3 includes the following specific steps:

[0018] Step 3.1: Calculate the power distribution between the battery and the supercapacitor based on the battery power distribution factor λ, P battery =P demand *λ,P sc =P demand *(1-λ), where P bat min≤P battery ≤P bat max, P sc min≤P sc ≤P sc max.

[0019] According to the battery terminal voltage U bat and battery power distribution P battery The battery capacity degradation rate during this period can be calculated using the following method:

[0020]

[0021]

[0022]

[0023]

[0024] In the above formula, Q loss This represents the degradation of battery capacity; A, B, and z are all constants, with values ​​of 0.003, -1516, and 0.824 respectively; E a is the activation energy of the battery, taken as 15162 J; C_rate is the charge / discharge rate of the battery; R is the gas constant, taken as 8.314 J / mol / K; T bat This is the absolute temperature of the battery, set to 288K; Bat_cap is the total battery capacity.

[0025] Equation 4-7 can be used to calculate the capacity decay Q of a battery within a single time segment. loss(n) Capacity decay Q for a single time segment loss(n) By summing the results, we can obtain the battery capacity degradation rate after the driving cycle.

[0026] Step 3.2: Establish the objective function for the operating cost of electric vehicles:

[0027] The objective function for the total driving range of electric vehicles is:

[0028]

[0029] In the formula M all M represents the total mileage. n For the mileage of a single driving cycle, A h_n A represents the total ampere-hours for a single driving cycle. h_all This refers to the total ampere-hours of the electric vehicle's total driving range.

[0030] Among them, the total ampere-hours of a single driving cycle A h_n It can be determined by the following formula:

[0031]

[0032] According to Equation 9, Equation 4 can be transformed into the following equation:

[0033]

[0034] Therefore, based on equations 4, 5, 6, 7, and 9, equation 8 can be transformed into the following equation:

[0035]

[0036] Generally, when the battery capacity decays by 20%, the battery is considered unusable. Therefore, Q loss Take 20%.

[0037] Therefore, the operating cost of a battery per 100 kilometers can be determined by the following formula:

[0038]

[0039] In the formula, cos t0 is the inherent cost of the hybrid energy storage system, and cos t ele The price is per kilowatt-hour.

[0040] Step 3.3: Using battery capacity degradation and battery cost per 100 kilometers as objective functions, under the constraints of energy storage device SOC and power output, remove individuals that do not meet the constraints, and use a non-dominated sorting genetic algorithm to solve the multi-objective optimization problem. The battery power allocation factor corresponding to the optimal solution is used as the initial battery power allocation factor for the next unit time segment.

[0041] This invention provides an energy management method for electric vehicles based on hybrid energy storage. Based on the electric vehicle's speed, the power demand of the in-vehicle load, and the state of charge (SOC) of the battery and supercapacitor, a fuzzy control algorithm outputs a battery power allocation factor λ. Using the upper and lower limits of the battery and supercapacitor SOC and output power limitations as constraints, and with battery capacity degradation and 100-kilometer operating cost as objective functions, a non-dominated sorting genetic algorithm is used to correct the battery power allocation factor λ in real time. This invention can utilize the characteristics of both battery and supercapacitor energy storage units to rationally allocate the required power and slow down battery capacity degradation. Attached Figure Description

[0042] Figure 1 A schematic diagram illustrating the working principle of an electric vehicle energy management method based on hybrid energy storage, provided in an embodiment of the present invention.

[0043] Figure 2 The flowchart is for a non-dominated sorting genetic algorithm;

[0044] Figure 3 The membership function diagram of the battery power allocation factor λ;

[0045] Figure 4 Power demand P for automobiles demand Membership function graph;

[0046] Figure 5 This is a membership function graph of the supercapacitor's state of charge (SOC).

[0047] Figure 6 This is a graph showing the membership function of battery SOC. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0049] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0050] like Figure 1 As shown, an embodiment of the present invention provides an energy management method for electric vehicles based on hybrid energy storage, comprising the following steps:

[0051] Step 1: Collect relevant data from inside the vehicle to determine the SOC (State of Charge) of the battery and supercapacitor, and the power P of the load inside the vehicle. load The electric vehicle's driving speed v and battery terminal voltage U bat ;

[0052] Step 2: Determine the output and parameters of the fuzzy logic controller: The inputs of the fuzzy logic controller are the battery SOC, the supercapacitor SOC, and the total power demand P. demand The output is the battery capacity allocation factor λ.

[0053] Step 3: Using one second as a unit time segment, collect the battery capacity allocation factor λ within the unit time segment. Calculate the allocated power of the battery and supercapacitor based on the battery power allocation factor λ and the total power demand. Using battery capacity degradation and operating cost per 100 kilometers as objective functions, and under the constraints of SOC and power output, use a non-dominated sorting genetic algorithm to solve the multi-objective optimization problem. Use the battery power allocation factor corresponding to the optimal solution as the initial battery power allocation factor for the next unit time segment to achieve the best allocation effect between the battery and supercapacitor.

[0054] In a preferred embodiment of the present invention, in step 1, to ensure the normal operation of the energy storage unit, the upper and lower limits of the State of Charge (SOC) for the battery and the supercapacitor are set as follows: 0.3 ≤ SOC bat ≤0.7, 0.2≤SOC sc ≤0.9, where SOC bat Indicates battery SOC, SOC sc This indicates the SOC (State of Charge) of the supercapacitor. bat >0.7, SOC sc If the SOC of one of the energy storage units is below the lower limit, the other energy storage device can charge it until the SOC is within the normal range. If the SOC of both units is below the lower limit, the energy storage system cannot work properly.

[0055] In a preferred embodiment of the present invention, step 2 includes the following specific steps:

[0056] The input to the fuzzy logic controller is SOC. bat SOC sc and total power demand P demand The output is the battery capacity allocation factor λ. The total power demand P... demand The calculation formula is as follows:

[0057]

[0058]

[0059] P demand =P load +P demand0 (3)

[0060] Among them, f t P is the longitudinal driving force (N) of the car.demand0 Where m is the power required by the power system (W), m is the vehicle mass (kg), and g is the acceleration due to gravity (m / s²). 2 f is the rolling resistance coefficient, v is the vehicle speed (m / s), α is the road inclination angle, and ρ is the air density (kg / m³). 3 ), C D η is the drag coefficient, A is the frontal area (m2), δ is the rotational mass conversion factor, and η is the rotational mass conversion factor. t For the efficiency of the mechanical transmission system, η m For inverter and motor efficiency.

[0061] In a preferred embodiment of the present invention, in step 2, for ease of calculation, the power required by the vehicle's power system is divided by the maximum input and output power of the motor, which is used as the input to the fuzzy controller, with the fuzzy universe of discourse being [-1, 1]. SOC bat SOC sc The fuzzy universes of discourse for the battery power allocation factor λ are [0.3, 0.7], [0.2, 0.9], and [0, 1], respectively. The fuzzy subsets are shown in Table 1 below:

[0062] Table 1: Linguistic Values ​​of Fuzzy Variables

[0063] Fuzzy variables linguistic values ​​of fuzzy variables <![CDATA[SOC bat ]]> L,M,G <![CDATA[SOC sc ]]> L,M,G <![CDATA[P demand ]]> NB, NS, ZE, PS, PB λ L,M,G

[0064] The membership function of a fuzzy variable is as follows: Figure 3-6 As shown, where Figure 3 Let λ be the membership function of the battery power allocation factor. Figure 4 Power demand P for automobiles demand membership function, Figure 5 For SOC sc membership function, Figure 6 For SOC bat The membership functions and fuzzy logic control rules are shown in Table 2:

[0065] Table 2 Fuzzy Logic Control Rules

[0066]

[0067] Where L: small; M: medium; G: large; NB: negative large; NS: negative small; ZE: 0; PS: positive small; PB: positive large.

[0068] In a preferred embodiment of the present invention, step 3 includes the following specific steps:

[0069] Step 3.1: Calculate the power distribution between the battery and the supercapacitor based on the battery power distribution factor λ, P battery =P demand*λ,P sc =P demand *(1-λ), where P bat min≤P battery ≤P bat max, P sc min≤P sc ≤P sc max.

[0070] According to the battery terminal voltage U bat and battery power distribution P battery The battery capacity degradation rate during this period can be calculated using the following method:

[0071]

[0072]

[0073]

[0074]

[0075] In the above formula, Q loss This represents the degradation of battery capacity; A, B, and z are all constants, with values ​​of 0.003, -1516, and 0.824 respectively; E a is the activation energy of the battery, taken as 15162 J; C_rate is the charge / discharge rate of the battery; R is the gas constant, taken as 8.314 J / mol / K; T bat This is the absolute temperature of the battery, set to 288K; Bat_cap is the total battery capacity.

[0076] The capacity decay Q of the battery within a single time segment can be calculated using equations (4)-(7). loss(n) Capacity decay Q for a single time segment loss(n) By summing the results, we can obtain the battery capacity degradation rate after the driving cycle.

[0077] Step 3.2: Establish the objective function for the operating cost of electric vehicles:

[0078] The objective function for the total driving range of electric vehicles is:

[0079]

[0080] In the formula M all M represents the total mileage. n For the mileage of a single driving cycle, A h_n A represents the total ampere-hours for a single driving cycle. h_all This refers to the total ampere-hours of the electric vehicle's total driving range.

[0081] Among them, the total ampere-hours of a single driving cycle A h_n It can be determined by the following formula:

[0082]

[0083] According to Equation 9, Equation 4 can be transformed into the following equation:

[0084]

[0085] Therefore, based on equations 4, 5, 6, 7, and 9, equation 8 can be transformed into the following equation:

[0086]

[0087] Generally, when the battery capacity decays by 20%, the battery is considered unusable. Therefore, Q loss Take 20%.

[0088] Therefore, the operating cost of a battery per 100 kilometers can be determined by the following formula:

[0089]

[0090] In the formula, cos t0 is the inherent cost of the hybrid energy storage system, and cos t ele The price is per kilowatt-hour.

[0091] Step 3.3: Using battery capacity degradation and battery cost per 100 kilometers as objective functions, and under the constraints of energy storage device SOC and power output, individuals that do not meet the constraints are removed. A non-dominated sorting genetic algorithm is used to solve the multi-objective optimization problem. The battery power allocation factor corresponding to the optimal solution is used as the initial battery power allocation factor for the next unit time segment. The non-dominated sorting genetic algorithm process is as follows: Figure 2 As shown.

[0092] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An energy management method for electric vehicles based on hybrid energy storage, characterized in that, Includes the following steps: Step 1: Collect data from inside the vehicle to determine the SOC of the battery and supercapacitor, and the power P of the load inside the vehicle. load The electric vehicle's driving speed v and battery terminal voltage U bat ; Step 2: Determine the input and output of the fuzzy logic controller: The inputs of the fuzzy logic controller are the battery SOC, the supercapacitor SOC, and the total power demand P. demand The output is the battery power allocation factor λ; Step 3: Using one second as a unit time segment, collect the battery power allocation factor λ within the unit time segment. Calculate the allocated power between the battery and the supercapacitor based on the battery power allocation factor λ and the total power demand. Using battery capacity degradation and operating cost per 100 kilometers as objective functions, and under the constraints of SOC and power output, use a non-dominated sorting genetic algorithm to solve the multi-objective optimization problem. Use the battery power allocation factor corresponding to the optimal solution as the initial battery power allocation factor for the next unit time segment. In step 1, the upper and lower limits of the State of Charge (SOC) for the battery and the supercapacitor are set as follows: 0.3 ≤ SOC bat ≤0.7, 0.2≤SOC sc ≤0.9, where SOC bat Indicates battery SOC, SOC sc This indicates the SOC (State of Charge) of the supercapacitor. bat >0.7, SOC sc >0.9, excess power is removed through the unloading circuit; when the SOC of one of the energy storage units is lower than the lower limit, it can be charged by another energy storage device until the SOC is within the normal range. If the SOC of both is lower than the lower limit, the energy storage system cannot work properly. In step 2, the power requirement of the vehicle's powertrain is divided by the maximum input and output power of the motor, and this division is used as the input to the fuzzy controller, with the fuzzy universe of discourse being [-1,1]. (SOC) bat SOC sc The fuzzy universes of discourse for the battery power allocation factor λ are [0.3 0.7], [0.2,0.9], and [0,1], respectively. Step 3 includes the following specific steps: Step 3.1: Calculate the power distribution between the battery and the supercapacitor based on the battery power distribution factor λ, P bat =P demand *λ,P sc =P demand *(1-λ), where P bat min≤P bat ≤P bat max, P sc min≤P sc ≤P sc max; According to the battery terminal voltage U bat and battery power distribution P bat The battery capacity degradation rate during this period can be calculated using the following method: （4）； （5）； （6）； （7）； In the above formula, Q loss This represents the degradation of battery capacity; A, B, and z are all constants, with values ​​of 0.003, -1516, and 0.824 respectively; E a is the activation energy of the battery, taken as 15162 J; C_rate is the charge / discharge rate of the battery; R is the gas constant, taken as 8.314 J / mol / K; T bat This is the absolute temperature of the battery, set to 288K; Bat_cap is the total battery capacity. Equation 4-7 can be used to calculate the capacity decay Q of a battery within a single time segment. loss(n) Capacity decay Q for a single time segment loss(n) Summing these values ​​will give you the battery capacity degradation rate after the driving cycle. Step 3.2: Establish the objective function for the operating cost of electric vehicles: The objective function for the total driving range of electric vehicles is: (8)； In the formula M all M represents the total mileage. n For the mileage of a single driving cycle, A h_n A represents the total ampere-hours for a single driving cycle. h_all This refers to the total ampere-hours of the electric vehicle's total mileage. Among them, the total ampere-hours of a single driving cycle A h_n It can be determined by the following formula: (9)； According to Equation 9, Equation 4 can be transformed into the following equation: (10)； Therefore, based on equations 4, 5, 6, 7, and 9, equation 8 can be transformed into the following equation: (11)； When the battery capacity decays by 20%, the battery is considered unusable. Therefore, Q loss Take 20%; Therefore, the operating cost of a battery per 100 kilometers can be determined by the following formula: (12)； In the formula, cos t0 is the inherent cost of the hybrid energy storage system, and cos t ele Price per kilowatt-hour; Step 3.3: Using battery capacity degradation and battery cost per 100 kilometers as objective functions, under the constraints of energy storage device SOC and power output, remove individuals that do not meet the constraints, and use a non-dominated sorting genetic algorithm to solve the multi-objective optimization problem. The battery power allocation factor corresponding to the optimal solution is used as the initial battery power allocation factor for the next unit time segment.

2. The electric vehicle energy management method based on hybrid energy storage according to claim 1, characterized in that, Step 2 includes the following specific steps: The input to the fuzzy logic controller is SOC. bat SOC sc and total power demand P demand The output is the battery power allocation factor λ; the total power demand P demand The calculation formula is as follows: （1）； （2）； （3）； Among them, f t P is the longitudinal driving force of a car, measured in N. demand0 The power required by the power system is given by m; the vehicle mass is given in kg; and g is the acceleration due to gravity, in m / s². 2 f is the rolling resistance coefficient; v is the vehicle speed in m / s; α is the road inclination angle; ρ is the air density in kg / m³. 3 C D A is the drag coefficient; A is the frontal area, in m². 2 δ is the rotational mass conversion factor. For the efficiency of mechanical transmission systems, For inverter and motor efficiency; P load To meet the power requirements of other loads inside the vehicle.

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