Method, apparatus and device for identifying battery parameters and storage medium

An apparatus for identifying parameters of a battery, comprising:

• a determination module configured to determine to-be-identified parameters of the battery and determine an initial population, wherein the initial population comprises N individuals each comprising all the to-be-identified parameters, and the initial population is determined as a current population;
• a screening module configured to perform a screening step:
• for at least part of individuals in the current population, updating positions of the individuals in the current population through local optimization and generating corresponding first individuals, updating the positions of the individuals in the current population through Levy flight and generating corresponding second individuals;
• selecting an optimal individual as an individual in the next generation population according to fitness of the individuals in the current population and the corresponding first individuals and the corresponding second individuals, wherein the next generation population is determined as the current population for a next round of screening;
• an iteration determination module configured to repeat the screening step until a termination condition is met, to obtain a final population;
• and
• an output module configured to determine the to-be-identified parameters comprised in the optimal individual in the final population as the parameters of the battery, and monitor physical and chemical changes inside the battery based on the determined parameters of the battery, wherein
• the screening module comprises a Levy flight module configured to determine a step size in the Levy flight according to the number of screening steps and update the positions of the individuals in the current population through the Levy flight, wherein there is a negative correlation between the step size and the number of screening steps, and the Levy flight module is configured to determine the step size in the Levy flight according to the number of screening steps form an expression
• α
• =
• α
• 0
• +
• α
• range
• 1
• +
• exp
• (
• a
• ⁡
• (
• t
• -
• bT
• )
• )
• ,
• wherein α represents the step size in the Levy flight, α0 represents an initially set step size, αrange represents a range of the step size, T is the maximum number of screening steps, t is the current number of screening steps, and a and b represent set constants, 0<a<1, and ⅓<b<⅔;
• wherein the local optimization is performed based on the following equation:
• xit′=xit+r1⊗H(Pa−r2)⊗(xjt−xkt)
• xit represents a position of an i-th sample individual in a t-th round, xit′ represents a new position of the i-th sample individual after random migration, r1 and r2 represent random number vectors, Pa represents a detection probability, xjt and xkt respectively represent two randomly selected individuals in the current population. and H( ) represents a Heaviside function.

An electronic device, comprising a processor and a memory, wherein

• the memory storing a computer program, and the processor is configured to execute the computer program stored in the memory to implement the method for identifying parameters of a battery, wherein the method comprises:
• determining to-be-identified parameters of the battery and determining an initial population, wherein the initial population comprises N individuals each comprising all the to-be-identified parameters;
• performing a screening step with the initial population as a current population:
• for at least part of individuals in the current population, updating positions of the individuals in the current population through local optimization and generating corresponding first individuals, and updating the positions of the individuals in the current population through Levy flight and generating corresponding second individuals;
• selecting an optimal individual according to fitness of the individuals in the current population and the corresponding first individuals and the corresponding second individuals, as an individual in a next generation population, wherein the next generation population is determined as the current population in a next round of screening step;
• repeating the above screening step until a termination condition is met, to obtain a final population;
• and determining the to-be-identified parameters comprised in the optimal individual in the final population as the parameters of the battery, and monitoring physical and chemical changes inside the battery based on the determined parameters of the battery, wherein the updating the positions of the individuals in the current population through the Levy flight comprises:
• determining a step size in the Levy flight according to the number of screening steps and updating the positions of the individuals in the current population through the Levy flight, wherein there is a negative correlation between the step size and the number of screening steps, and the determining the step size in the Levy flight according to the number of screening steps comprises:
• determining the step size form an expression
• α
• =
• α
• 0
• +
• α
• range
• 1
• +
• exp
• (
• a
• ⁡
• (
• t
• -
• bT
• )
• )
• ,
• wherein α represents the step size in the Levy flight, α0 represents an initially set step size, αrange represents a range of the step size, T is the maximum number of screening steps, t is the current number of screening steps, and a and b represent set constants, 0<a<1, and ⅓<b<⅔;
• wherein the local optimization is performed based on the following equation:
• xit′=xit+r1⊗H(Pa−r2)⊗(xjt−xkt)
• xit represents a position of an i-th sample individual in a t-th round, xit′ represents a new position of the i-th sample individual after random migration, r1 and r2 represent random number vectors, Pa represents a detection probability, xjt and xkt respectively represent two randomly selected individuals in the current population, and H( ) represents a Heaviside function.

A computer-readable storage medium with a computer program stored thereon, wherein when the computer program is executed by a processor, the method for identifying parameters of a battery is implemented, wherein the method comprises:

• determining to-be-identified parameters of the battery and determining an initial population, wherein the initial population comprises N individuals each comprising all the to-be-identified parameters;
• performing a screening step with the initial population as a current population:
• for at least part of individuals in the current population, updating positions of the individuals in the current population through local optimization and generating corresponding first individuals, and updating the positions of the individuals in the current population through Levy flight and generating corresponding second individuals;
• selecting an optimal individual according to fitness of the individuals in the current population and the corresponding first individuals and the corresponding second individuals, as an individual in a next generation population, wherein the next generation population is determined as the current population in a next round of screening step;
• repeating the above screening step until a termination condition is met, to obtain a final population;
• and determining the to-be-identified parameters comprised in the optimal individual in the final population as the parameters of the battery, and monitoring physical and chemical changes inside the battery based on the determined parameters of the battery, wherein the updating the positions of the individuals in the current population through the Levy flight comprises:
• determining a step size in the Levy flight according to the number of screening steps and updating the positions of the individuals in the current population through the Levy flight, wherein there is a negative correlation between the step size and the number of screening steps, and the determining the step size in the Levy flight according to the number of screening steps comprises:
• determining the step size form an expression
• α
• =
• α
• 0
• +
• α
• range
• 1
• +
• exp
• (
• a
• ⁡
• (
• t
• -
• bT
• )
• )
• ,
• wherein α represents the step size in the Levy flight, α0 represents an initially set step size, αrange represents a range of the step size, T is the maximum number of screening steps, t is the current number of screening steps, and a and b represent set constants, 0<a<1, and ⅓<b<⅔;
• wherein the local optimization is performed based on the following equation:
• xit′=xit+r1⊗H(Pa−r2)⊗(xjt−xkt)
• xit represents a position of an i-th sample individual in a t-th round, xit′ represents a new position of the i-th sample individual after random migration, r1 and r2 represent random number vectors, Pa represents a detection probability, xjt and xkt respectively represent two randomly selected individuals in the current population, and H( ) represents a Heaviside function.