From 89c38be1753c4ec2c9be88eafe8ed5a7ced4ca8d Mon Sep 17 00:00:00 2001
From: "remi.clement@inrae.fr" <arnaud.watlet@umons.ac.be>
Date: Thu, 23 Feb 2023 16:08:26 +0100
Subject: [PATCH] Updates autogain strategy to adjust gain after polarity
inversion test
---
ohmpi.py | 57 ++++++++++++++++++++++++++++++++++----------------------
1 file changed, 35 insertions(+), 22 deletions(-)
diff --git a/ohmpi.py b/ohmpi.py
index 6071e408..e7869726 100644
--- a/ohmpi.py
+++ b/ohmpi.py
@@ -819,18 +819,29 @@ class OhmPi(object):
if autogain:
if self.board_version == 'mb.2023.0.0':
# compute autogain
- self.pin0.value = True
- self.pin1.value = False
- self.pin6.value = True # IHM current injection led on
- time.sleep(injection_duration)
- gain_current = self._gain_auto(AnalogIn(self.ads_current, ads.P0))
- if polarity > 0:
- gain_voltage = self._gain_auto(AnalogIn(self.ads_voltage, ads.P0))
- else:
- gain_voltage = self._gain_auto(AnalogIn(self.ads_voltage, ads.P2))
- self.pin0.value = False
- self.pin1.value = False
- self.pin6.value = False # IHM current injection led off
+ gain_voltage_tmp, gain_current_tmp = [], []
+ for n in [0,1]:
+ if n == 0:
+ self.pin0.value = True
+ self.pin1.value = False
+ self.pin6.value = True # IHM current injection led on
+ else:
+ self.pin0.value = False
+ self.pin1.value = True # current injection nr2
+ self.pin6.value = True # IHM current injection led on
+
+ time.sleep(injection_duration)
+ gain_current_tmp.append(self._gain_auto(AnalogIn(self.ads_current, ads.P0)))
+ if polarity > 0:
+ gain_voltage_tmp.append(self._gain_auto(AnalogIn(self.ads_voltage, ads.P0)))
+ else:
+ gain_voltage_tmp.append(self._gain_auto(AnalogIn(self.ads_voltage, ads.P2)))
+ self.pin0.value = False
+ self.pin1.value = False
+ self.pin6.value = False # IHM current injection led off
+
+ gain_voltage = np.min(gain_voltage_tmp)
+ gain_current = np.min(gain_current_tmp)
self.exec_logger.debug(f'Gain current: {gain_current:.3f}, gain voltage: {gain_voltage:.3f}')
self.ads_current = ads.ADS1115(self.i2c, gain=gain_current, data_rate=860,
address=self.ads_current_address, mode=0)
@@ -844,8 +855,8 @@ class OhmPi(object):
self.ads_voltage = ads.ADS1115(self.i2c, gain=gain_voltage, data_rate=860,
address=self.ads_voltage_address, mode=0)
- print('gain_voltage', gain_voltage)
- print('gain_current', gain_current)
+ #print('gain_voltage', gain_voltage)
+ #print('gain_current', gain_current)
self.pin0.value = False
self.pin1.value = False
@@ -949,16 +960,18 @@ class OhmPi(object):
# TODO send a message on SOH stating the battery level
# let's do some calculation (out of the stacking loop)
- i_stack = np.empty((2 * nb_stack, int(meas.shape[0] // 3))) * np.nan
- vmn_stack = np.empty((2 * nb_stack, int(meas.shape[0] // 3))) * np.nan
- # ps_stack = np.empty((2 * nb_stack, int(meas.shape[0] // 3))) * np.nan
+
+ # i_stack = np.empty(2 * nb_stack, dtype=object)
+ # vmn_stack = np.empty(2 * nb_stack, dtype=object)
+ i_stack, vmn_stack = [], []
+ # select appropriate window length to average the readings
+ window = int(np.min([f.shape[0] for f in fulldata[::2]]) // 3)
for n, meas in enumerate(fulldata[::2]):
# take average from the samples per stack, then sum them all
# average for the last third of the stacked values
# is done outside the loop
- i_stack[n] = meas[-int(meas.shape[0] // 3):, 0]
- vmn_stack[n] = meas[-int(meas.shape[0] // 3):, 1]
- # ps_stack[n] = (np.mean(meas[-int(meas.shape[0] // 3):, 0]))
+ i_stack.append(meas[-int(window):, 0])
+ vmn_stack.append(meas[-int(window):, 1])
sum_i = sum_i + (np.mean(meas[-int(meas.shape[0] // 3):, 0]))
vmn1 = np.mean(meas[-int(meas.shape[0] // 3), 1])
@@ -991,9 +1004,9 @@ class OhmPi(object):
fulldata = a
else:
np.array([[]])
-
+
vmn_stack_mean = np.mean([np.diff(np.mean(vmn_stack[i*2:i*2+2], axis=1)) / 2 for i in range(nb_stack)])
- vmn_std =np.sqrt((np.std(vmn_stack[::2])**2 + np.std(vmn_stack[1::2]))**2) # np.sum([np.std(vmn_stack[::2]),np.std(vmn_stack[1::2])]) # np.sqrt((np.std(vmn_stack[::2])**2 + np.std(vmn_stack[1::2]))**2)
+ vmn_std =np.sqrt(np.std(vmn_stack[::2])**2 + np.std(vmn_stack[1::2])**2) # np.sum([np.std(vmn_stack[::2]),np.std(vmn_stack[1::2])])
i_stack_mean = np.mean(i_stack)
i_std = np.mean(np.array([np.std(i_stack[::2]), np.std(i_stack[1::2])]))
r_stack_mean = vmn_stack_mean / i_stack_mean
--
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