A method includes receiving current measurements from at least one current sensor configured to measure current of a battery system in communication with a power distribution network having a power plant. The method also includes receiving voltage measurements from at least one voltage sensor config
A method includes receiving current measurements from at least one current sensor configured to measure current of a battery system in communication with a power distribution network having a power plant. The method also includes receiving voltage measurements from at least one voltage sensor configured to measure voltage of the battery system and temperature measurements from at least one temperature sensor configured to measure temperature of the battery system. The method includes determining an impedance parameter of the battery system based on the received measurements, a temperature parameter of the battery system based on the received measurements, a predicted voltage parameter based on the impedance parameter, and a predicted temperature parameter based on the temperature parameter. The method includes commanding the battery system to charge power from the power plant or discharge power from the power plant based on the predicted voltage parameter and the predicted temperature parameter.
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1. A method comprising: receiving, at data processing hardware, current measurements from at least one current sensor configured to measure current of a battery system in communication with a power distribution network having a power plant distributing power to one or more consumers;receiving, at da
1. A method comprising: receiving, at data processing hardware, current measurements from at least one current sensor configured to measure current of a battery system in communication with a power distribution network having a power plant distributing power to one or more consumers;receiving, at data processing hardware, voltage measurements from at least one voltage sensor configured to measure voltage of the battery system;receiving, at data processing hardware, temperature measurements from at least one temperature sensor configured to measure temperature of the battery system;determining, by the data processing hardware, an impedance parameter of the battery system based on the received measurements;determining, by the data processing hardware, a temperature parameter of the battery system based on the received measurements;determining, by the data processing hardware, a predicted voltage parameter based on the impedance parameter;determining, by the data processing hardware, a predicted temperature parameter based on the temperature parameter; andcommanding, by the data processing hardware, the battery system to charge power from the power plant or discharge power from the power plant based on the predicted voltage parameter and the predicted temperature parameter. 2. The method of claim 1, wherein determining the impedance parameter or the temperature parameter comprises determining a transfer function H(w) of a time series f(t) defined in a time interval [−T, T], where T is an integer greater than zero, the transfer function H(w), in a complex domain, being defined as: H(w)=∫−T+T∫(t)e−iωtdt, where w=2πF, F is a frequency of a time series of the received measurements, defined as tεRn and FεCn. 3. The method of claim 2, wherein the transfer function H(w) is defined as a ratio between a Fourier transform of an output variable y(t) and an input variable x(t), where the output variable y(t) is one of the impedance parameter or the temperature parameter, and the input variable x(t) is one or more of the received measurements, and the transfer function H(w) in a discrete domain is determined as: H(w)=∑p=1Ny(p)e-iωp∑p=1Nx(p)e-iωp. 4. The method of claim 1, wherein determining one of the predicted voltage parameter or the predicted temperature parameter includes executing a time series analysis implementing an auto-regressive model. 5. The method of claim 4, wherein the auto-regressive model AR(p) is defines as: Xt=c+Σi=1pφiXt-i+εi;where φ1-φp are parameters of the model, c is a constant, and εt is white noise. 6. The method of claim 4, further comprising implementing a neural network approach, an empirical recursive method, or a Yule-Walker approach to determine an optimal solution of the auto-regressive model AR(p). 7. The method of claim 1, wherein commanding, by the data processing hardware, the battery system to charge power from the power plant includes commanding the battery system to store power from the power plant. 8. The method of claim 1, further comprising updating an impedance profile, a voltage profile, or a temperature profile based on the voltage measurements or the temperature measurements. 9. The method of claim 1, wherein determining the predicted voltage parameter includes: training the data processing hardware to generate a best fit of the voltage measurements or the temperature measurements; andpredicting, by the data processing hardware, the predicted voltage parameter or the predicted temperature parameter based on the best fit of the voltage measurements or the temperature measurements, respectively. 10. The method of claim 1, further comprising: tracking, by the data processing hardware, a remaining available capacity of the battery system; anddetermining, by the data processing hardware, one of a charge state or life cycle of the battery system. 11. A system comprising: data processing hardware; andmemory hardware in communication with the data processing hardware, the memory hardware storing instructions that when executed on the data processing hardware cause the data processing hardware to perform operations comprising: receiving current measurements from at least one current sensor configured to measure current of a battery system in communication with a power distribution network having a power plant distributing power to one or more consumers;receiving voltage measurements from at least one voltage sensor configured to measure voltage of the battery system;receiving temperature measurements from at least one temperature sensor configured to measure temperature of the battery system;determining an impedance parameter of the battery system based on the received measurements;determining a temperature parameter of the battery system based on the received measurements;determining a predicted voltage parameter based on the impedance parameter;determining a predicted temperature parameter based on the temperature parameter; andcommanding the battery system to charge power from the power plant or discharge power from the power plant based on the predicted voltage parameter and the predicted temperature parameter. 12. The system of claim 11, wherein determining the impedance parameter or the temperature parameter comprises determining a transfer function H(w) of a time series f(t) defined in a time interval [−T, T], where T is an integer greater than zero, the transfer function H(w), in a complex domain, being defined as: H(w)=∫−T+Tf(t)e−ωtdt, where w=2πF, F is a frequency of a time series of the received measurements, defined as tεRn and FεCn. 13. The system of claim 12, wherein the transfer function H(w) is defined as a ratio between a Fourier transform of an output variable y(t) and an input variable x(t), where the output variable y(t) is one of the impedance parameter or the temperature parameter, and the input variable x(t) is one or more of the received measurements, and the transfer function H(w) in a discrete domain is determined as: H(w)=∑p=1Ny(p)e-iωp∑p=1Nx(p)e-iωp. 14. The system of claim 11, wherein determining one of the predicted voltage parameter or the predicted temperature parameter includes executing a time series analysis implementing an auto-regressive model. 15. The system of claim 14, wherein the auto-regressive model AR(p) is defines as: Xt=c+Σi=1pφiXt-i+εi;where φ1-φp are parameters of the model, c is a constant, and εt is white noise. 16. The system of claim 14, wherein the instructions, when executed on the data processing hardware, cause the data processing hardware to perform operations comprising: implementing a neural network approach, an empirical recursive method, or a Yule-Walker approach to determine an optimal solution of the auto-regressive model AR(p). 17. The system of claim 11, wherein commanding, by the data processing hardware, the battery system to charge power from the power plant includes commanding the battery system to store power from the power plant. 18. The system of claim 11, wherein the instructions, when executed on the data processing hardware, cause the data processing hardware to perform operations comprising: updating an impedance profile, a voltage profile, or a temperature profile based on the voltage measurements or the temperature measurements. 19. The system of claim 11, wherein determining the predicted voltage parameter includes: training the data processing hardware to generate a best fit of the voltage measurements or the temperature measurements; andpredicting, by the data processing hardware, the predicted voltage parameter or the predicted temperature parameter based on the best fit of the voltage measurements or the temperature measurements, respectively. 20. The system of claim 11, wherein the instructions, when executed on the data processing hardware, cause the data processing hardware to perform operations comprising: tracking a remaining available capacity of the battery system; anddetermining one of a charge state or life cycle of the battery system.
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