CN-117261686-B - Hybrid energy storage-based electric vehicle energy management method

Abstract

The invention is suitable for the technical field of energy management of electric vehicles, provides an energy management method of an electric vehicle based on hybrid energy storage, according to the speed of the electric vehicle during running, the power required by the load in the vehicle and the battery and super capacitor SOC output the battery power distribution factor lambda through a fuzzy control algorithm, according to the upper and lower limit ranges of the battery and the super capacitor SOC and the output power limit as constraint conditions, the battery power distribution factor lambda is corrected in real time by using a non-dominant sorting genetic algorithm with the battery capacity attenuation degree and hundred kilometers running cost as objective functions.

Inventors

• JIN YINGAI
• YU WENBIN
• MA CHUNQIANG
• JIANG ZHIPENG

Assignees

• 吉林大学

Dates

Publication Date

20260505

Application Date

20230926

Claims (2)

1. 1. The electric automobile energy management method based on the hybrid energy storage is characterized by comprising the following steps of: Step 1, acquiring data in an automobile, and determining the SOC of a battery and a super capacitor, the power P load of an in-automobile load, the running speed v of the electric automobile and the battery terminal voltage U bat ; Step 2, determining the input and output of the fuzzy logic controller, wherein the input of the fuzzy logic controller is battery SOC, super capacitor SOC and total required power P demand , and the output is battery power distribution factor lambda; Step 3, taking each second as a unit time segment, collecting a battery power distribution factor lambda in the unit time segment, calculating the distribution power of the battery and the super capacitor according to the battery power distribution factor lambda and the total required power, taking the battery capacity attenuation degree and hundred kilometers running cost as objective functions, carrying out multi-objective optimization problem solving by adopting a non-dominant sorting genetic algorithm under the constraint of SOC and power output, and taking the battery power distribution factor corresponding to the optimal solution as an initial battery power distribution factor of the next unit segment time; In the step 1, the constraint conditions of the upper limit and the lower limit of the SOC of the battery and the super capacitor are respectively set to be equal to or less than 0.3 and equal to or less than bat ≤0.7,0.2≤SOC sc and equal to or less than 0.9, wherein SOC bat represents the SOC of the battery, SOC sc represents the SOC of the super capacitor, and if SOC bat ＞0.7,SOC sc is more than 0.9, the extra electric quantity is discharged through an unloading circuit; In the step 2, dividing the power required by the power system of the automobile by the maximum input and output power of the motor, wherein the input of the fuzzy controller is the fuzzy domains of [ -1,1], [0.2,0.9] and [0,1]; The step 3 comprises the following specific steps: Step 3.1, calculating the distribution power of the battery and the super capacitor according to a battery power distribution factor lambda, wherein P bat =P demand *λ,P sc =P demand is 1-lambda, and P bat min≤P bat ≤P bat max,P sc min≤P sc ≤P sc max; The battery capacity fade for this period of time can be calculated from the battery terminal voltage U bat and the battery split power P bat by the following method: (4); (5); (6); (7); In the above formula, Q loss is the attenuation of the battery capacity, A, B and z are constants, the values are 0.003-1516 and 0.824 in sequence, E a is the activation energy of the battery, 15162J is taken, C_rate is the charge/discharge rate of the battery, R is the gas constant, 8.314J/mol/K is taken, T bat is the absolute temperature of the battery, 28K is taken, and Bat_cap is the total capacity of the battery; The capacity attenuation Q loss(n) in a single time segment of the battery can be calculated according to the formulas 4-7, and the capacity attenuation Q loss(n) of the single time segment is summed to obtain the battery capacity attenuation degree of the battery under the driving cycle working condition; Step 3.2, establishing an electric automobile running cost objective function: The total driving mileage objective function of the electric automobile is as follows: (8); Wherein M all is the total driving mileage, M n is the single driving cycle driving mileage, A h_n is the single driving cycle total safety time number, and A h_all is the electric automobile total driving mileage total safety time number; Wherein, the total number of driving cycles A h_n can be determined by the following formula: (9); According to equation 9, equation 4 may be converted to the following equation: (10); Thus, according to formulas 4,5, 6, 7, and 9, formula 8 may be converted to the following formulas: (11); when the capacity attenuation degree of the battery reaches 20%, the battery enters a scrapped state, so that 20% of Q loss is taken; the battery hundred kilometer running cost can therefore be determined by: (12); wherein cos t 0 is the inherent cost of the hybrid energy storage system, and cos t ele is the price per degree of electricity; And 3.3, taking the battery capacity attenuation degree and the battery hundred kilometers cost as objective functions, removing individuals which do not meet constraint conditions under the constraint of the energy storage equipment SOC and the power output, adopting a non-dominant ordering genetic algorithm to solve a multi-objective optimization problem, and taking a battery power distribution factor corresponding to the optimal solution as an initial battery power distribution factor of the next unit segment time.
2. 2. The hybrid energy storage-based electric vehicle energy management method of claim 1, wherein step 2 comprises the specific steps of: the input of the fuzzy logic controller is SOC bat 、SOC sc and total required power P demand , the output is battery power distribution factor lambda, and the calculation formula of the total required power P demand is as follows: (1); (2); (3); Wherein f t is the longitudinal running driving force of the automobile, the unit N, P demand0 is the power required by a power system, m is the mass of the automobile, g is the gravity acceleration, m/s 2 , f is the rolling resistance coefficient, v is the running speed of the automobile, m/s, alpha is the road inclination angle, ρ is the air density, kg/m 3 ;C D is the wind resistance coefficient, A is the windward area, m 2 , delta is the rotating mass conversion coefficient, For the efficiency of the mechanical transmission system, And P load is the power required by other loads in the vehicle.

Description

Hybrid energy storage-based electric vehicle energy management method Technical Field The invention belongs to the technical field of energy management of electric automobiles, and particularly relates to an energy management method of an electric automobile based on hybrid energy storage. Background At present, the development of electric automobiles in China is very rapid, but due to the restriction of battery technology in the current stage, the electric automobiles which are only provided with batteries as single energy storage equipment still have the limitation of endurance mileage and cycle life. The super capacitor has the characteristics of rapid charge and discharge, high energy density and long cycle life, can form a composite energy storage system with a battery, and prolongs the service life of the battery. Hybrid energy storage energy management is one of key technologies of electric automobiles, and the optimal design of a power distribution strategy between two energy source devices is a key difficulty of students, and the design of the power distribution strategy needs to be designed by reasonably utilizing the energy storage characteristics of batteries and super capacitors. Accordingly, there is a need for an electric vehicle energy management method based on hybrid energy storage to solve the above-mentioned problems. Disclosure of Invention The embodiment of the invention aims to provide an electric vehicle energy management method based on hybrid energy storage, which aims to solve the problems in the background technology. The embodiment of the invention is realized in such a way that the energy management method of the electric automobile based on the hybrid energy storage comprises the following steps: Step 1, acquiring relevant data in an automobile, and determining the SOC (State of Charge) of a battery and a super capacitor, the power P load of an in-automobile load, the running speed v of the electric automobile and the battery terminal voltage U bat; Step 2, determining the output and the output of the fuzzy logic controller, wherein the input of the fuzzy logic controller is battery SOC, super capacitor SOC and total required power P demand, and the output is battery capacity allocation factor lambda; And 3, taking each second as a unit time segment, collecting a battery capacity distribution factor lambda in the unit time segment, calculating the distribution power of the battery and the super capacitor according to the battery power distribution factor lambda and the total required power, taking the battery capacity attenuation degree and hundred kilometers running cost as objective functions, solving a multi-objective optimization problem by adopting a non-dominant sorting genetic algorithm under the constraint of SOC and power output, and taking the battery power distribution factor corresponding to the optimal solution as an initial battery power distribution factor of the next unit segment time to achieve the optimal distribution effect of the storage battery and the super capacitor. In the step 1, in order to ensure the normal operation of the energy storage units, the constraint conditions of the upper limit and the lower limit of the SOC of the battery and the super capacitor are respectively set to be 0.3- bat≤0.7,0.2≤SOCsc -0.9, wherein the SOC bat represents the battery SOC, the SOC sc represents the super capacitor SOC, if the SOC bat＞0.7,SOCsc is more than 0.9, the extra electric quantity is discharged through an unloading circuit, when the SOC of one energy storage unit is lower than the lower limit, the other energy storage unit can charge the other energy storage unit until the SOC is in a normal range, and if the SOC of the two energy storage units is lower than the lower limit, the energy storage system cannot normally operate. According to a further technical scheme, the step2 comprises the following specific steps: Inputs of the fuzzy logic controller are the SOC bat、SOCsc and the total required power P demand, and outputs are the battery capacity allocation factor lambda. The calculation formula of the total required power P demand is as follows: Pdemand＝Pload+Pdemand0 (3) Wherein f t is the driving force of the vehicle running longitudinally, P demand0 is the power required by the power system, m is the vehicle mass (kg), g is the gravitational acceleration (m/s 2), f is the rolling resistance coefficient, v is the vehicle running speed, alpha is the road inclination angle, ρ is the air density (kg/m 3),CD is the windage coefficient, A is the windward area (m 2), δ is the rotational mass conversion coefficient, η t is the mechanical transmission system efficiency, η m is the inverter and motor efficiency. In the further technical scheme, in the step 2, for the sake of simple calculation, the power required by the power system of the automobile is divided by the maximum input and output power of the motor, and the maximum input and output power is used as