BatterySimulatorBLAST/python/nmc811_grSi_LGMJ1_4Ah_2020.py

# Paul Gasper, NREL
# This model is fit to LG MJ1 cell aging data reported as part of the EU EVERLASTING battery project, report D2.3
# https://everlasting-project.eu/wp-content/uploads/2020/03/EVERLASTING_D2.3_final_20200228.pdf
# Cell tests were reported in early 2020, so likely 2018 or 2019 LG MJ1 cells.

import numpy as np
from functions.extract_stressors import extract_stressors
from functions.state_functions import update_power_state

# EXPERIMENTAL AGING DATA SUMMARY:
# Calendar aging varied SOC (10%, 70%, 90%) and temperature.
# Cycle aging varied temperature and C-rates; all DOD is 80% (10%-90%). NO ACCELERATED FADE OBSERVED.
# Relative discharge capacity is reported from measurements recorded at 25 Celsius and C/20 rate.

# MODEL SENSITIVITY
# The model predicts degradation rate versus time as a function of temperature and average
# state-of-charge and degradation rate versus equivalent full cycles (charge-throughput) as 
# a function of C-rate, temperature, and depth-of-discharge (DOD dependence is assumed to be linear, no aging data)

# MODEL LIMITATIONS
# Cycle degradation predictions WILL NOT PREDICT KNEE-POINT due to limited data.
# OPERATION AT HIGH DOD PREDCTIONS ARE LIKELY INACCURATE (it is unclear what voltage window corresponds to SOCs defined in the test data).
# NMC811 is known to degrade quickly at voltages above 4.1 V.

class Nmc811_GrSi_LGMJ1_4Ah_Battery:

    def __init__(self):
        # States: Internal states of the battery model
        self.states = {
            'qLoss_t': np.array([0]),
            'qLoss_EFC': np.array([0]),
        }

        # Outputs: Battery properties derived from state values
        self.outputs = {
            'q': np.array([1]),
            'q_t': np.array([1]),
            'q_EFC': np.array([1]),
        }

        # Stressors: History of stressors on the battery
        self.stressors = {
            'delta_t_days': np.array([np.nan]), 
            't_days': np.array([0]),
            'delta_efc': np.array([np.nan]), 
            'efc': np.array([0]),
            'TdegK': np.array([np.nan]),
            'soc': np.array([np.nan]),
            'dod': np.array([np.nan]),
            'Crate': np.array([np.nan]),
        }

        # Rates: History of stressor-dependent degradation rates
        self.rates = {
            'k_cal': np.array([np.nan]),
            'k_cyc': np.array([np.nan]),
        }

    # Nominal capacity
    @property
    def _cap(self):
        return 3.5

    # Define life model parameters
    @property
    def _params_life(self):
        return {
            # Capacity fade parameters
            'qcal_A': 0.0353,
            'qcal_B': -1.03e+03,
            'qcal_C': 57.7,
            'qcal_p': 0.743,
            'qcyc_A': 1.77e-07,
            'qcyc_B': 8.08e-13,
            'qcyc_C': 2.21e-07,
            'qcyc_D': 2.25e+03,
            'qcyc_E': 1.14e+04,
            'qcyc_p': 0.695,
        }
        
    # Battery model
    def update_battery_state(self, t_secs, soc, T_celsius):
        # Update the battery states, based both on the degradation state as well as the battery performance
        # at the ambient temperature, T_celsius
        # Inputs:
            #   t_secs (ndarry): vector of the time in seconds since beginning of life for the soc_timeseries data points
            #   soc (ndarry): vector of the state-of-charge of the battery at each t_sec
            #   T_celsius (ndarray): the temperature of the battery during this time period, in Celsius units.
            
        # Check some input types:
        if not isinstance(t_secs, np.ndarray):
            raise TypeError('Input "t_secs" must be a numpy.ndarray')
        if not isinstance(soc, np.ndarray):
            raise TypeError('Input "soc" must be a numpy.ndarray')
        if not isinstance(T_celsius, np.ndarray):
            raise TypeError('Input "T_celsius" must be a numpy.ndarray')
        if not (len(t_secs) == len(soc) and len(t_secs) == len(T_celsius)):
            raise ValueError('All input timeseries must be the same length')

        self.__update_states(t_secs, soc, T_celsius)
        self.__update_outputs()
    
    def __update_states(self, t_secs, soc, T_celsius):
        # Update the battery states, based both on the degradation state as well as the battery performance
        # at the ambient temperature, T_celsius
        # Inputs:
            #   t_secs (ndarry): vector of the time in seconds since beginning of life for the soc_timeseries data points
            #   soc (ndarry): vector of the state-of-charge of the battery at each t_sec
            #   T_celsius (ndarray): the temperature of the battery during this time period, in Celsius units.
            
        # Extract stressors
        delta_t_secs = t_secs[-1] - t_secs[0]
        delta_t_days, delta_efc, TdegK, soc, Ua, dod, Crate, cycles = extract_stressors(t_secs, soc, T_celsius)

        # Grab parameters
        p = self._params_life

        # Calculate the degradation coefficients
        k_cal = p['qcal_A'] * np.exp(p['qcal_B']/TdegK) * np.exp(p['qcal_C']*soc/TdegK)
        k_cyc  = (
            (p['qcyc_A'] + p['qcyc_B']*Crate + p['qcyc_C']*dod)
            * (np.exp(p['qcyc_D']/TdegK) + np.exp(-p['qcyc_E']/TdegK))
        )

        # Calculate time based average of each rate
        k_cal = np.trapz(k_cal, x=t_secs) / delta_t_secs
        k_cyc = np.trapz(k_cyc, x=t_secs) / delta_t_secs

        # Calculate incremental state changes
        states = self.states
        # Capacity
        dq_t = update_power_state(states['qLoss_t'][-1], delta_t_days, k_cal, p['qcal_p'])
        dq_EFC = update_power_state(states['qLoss_EFC'][-1], delta_efc, k_cyc, p['qcyc_p'])

        # Accumulate and store states
        dx = np.array([dq_t, dq_EFC])
        for k, v in zip(states.keys(), dx):
            x = self.states[k][-1] + v
            self.states[k] = np.append(self.states[k], x)

        # Store stressors
        t_days = self.stressors['t_days'][-1] + delta_t_days
        efc = self.stressors['efc'][-1] + delta_efc
        stressors = np.array([delta_t_days, t_days, delta_efc, efc, np.mean(TdegK), np.mean(soc), dod, Crate])
        for k, v in zip(self.stressors.keys(), stressors):
            self.stressors[k] = np.append(self.stressors[k], v)

        # Store rates
        rates = np.array([k_cal, k_cyc])
        for k, v in zip(self.rates.keys(), rates):
            self.rates[k] = np.append(self.rates[k], v)
    
    def __update_outputs(self):
        # Calculate outputs, based on current battery state
        states = self.states

        # Capacity
        q_t = 1 - states['qLoss_t'][-1]
        q_EFC = 1 - states['qLoss_EFC'][-1]
        q = 1 - states['qLoss_t'][-1] - states['qLoss_EFC'][-1]

        # Assemble output
        out = np.array([q, q_t, q_EFC])
        # Store results
        for k, v in zip(list(self.outputs.keys()), out):
            self.outputs[k] = np.append(self.outputs[k], v)