Algorithm based on DFFLS and neural network-ASRUKF for storage battery
A neural network and battery technology, which is applied in the field of algorithms based on DFFRLS and neural network-ASRUKF for batteries, can solve the problems that affect the full utilization of the program, and cannot guarantee the semi-positive definiteness of the state covariance matrix, so as to reduce data saturation and improve The effect of precision, speed and precision
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[0041] An algorithm based on DFFRLS and neural network-ASRUKF for a storage battery, comprising the following steps:
[0042] Step 1, DFFRLS online parameter identification.
[0043] Specific steps are as follows:
[0044] 101), algorithm initialization: set the initial covariance matrix P and parameter vector θ(k) as:
[0045]
[0046] 102), parameter update:
[0047] Among them, θ(k) is the estimated parameter value, and L is the filter gain matrix.
[0048] 103), construct dynamic forgetting factor function:
[0049] In the formula: ε(k+1) is the output variance between the theoretical model and the actual model, λ(k+1) is the dynamic forgetting factor function, and α and γ are positive adjustable parameters.
[0050] 104), gain matrix update:
[0051] L(k+1)=P(k)φ(k+1)[λ(k+1)+φ T (k+1)P(k)φ(k+1)] -1 .
[0052] 105), covariance matrix update:
[0053]
[0054] 106), repeat steps 102)-105), stop running when the program meets the termination condition, and...
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