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DDPG_Prius_test.py
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DDPG_Prius_test.py
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# -*- coding: utf-8 -*-
"""
DDPG_Prius
"""
#import os
#os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
import tensorflow.compat.v1 as tf
import numpy as np
from Prius_model_new import Prius_model
import scipy.io as scio
import matplotlib.pyplot as plt
############################### DDPG ####################################
class DDPG(object):
def __init__(self, a_dim, s_dim, a_bound,):
self.sess = tf.Session()
self.a_dim, self.s_dim, self.a_bound = a_dim, s_dim, a_bound,
self.S = tf.placeholder(tf.float32, [None, s_dim], 's')
with tf.variable_scope('Actor'):
self.a = self._build_a(self.S, scope='eval', trainable=True)
# networks parameters
self.ae_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='Actor/eval')
self.at_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='Actor/target')
self.sess.run(tf.global_variables_initializer())
def choose_action(self, s):
return self.sess.run(self.a, {self.S: s[np.newaxis, :]})[0]
def _build_a(self, s, scope, trainable):
with tf.variable_scope(scope):
net1 = tf.layers.dense(s, 200, activation=tf.nn.relu, name='l1', trainable=trainable)
net2 = tf.layers.dense(net1, 100, activation=tf.nn.relu, name = 'l2', trainable=trainable)
net3 = tf.layers.dense(net2, 50, activation=tf.nn.relu, name = 'l3', trainable=trainable)
a = tf.layers.dense(net3, self.a_dim, activation=tf.nn.sigmoid, name='a', trainable=trainable)
return tf.multiply(a, self.a_bound, name='scaled_a')
def loadmodel(self):
self.saver = tf.train.Saver()
self.saver.restore(self.sess, 'Checkpoints/Prius/save_net.ckpt-' + str(i))
##################### Testing Process ####################
s_dim = 3
a_dim = 1
a_bound = 1
DDPG = DDPG(a_dim, s_dim, a_bound)
data_path = 'Data_Standard Driving Cycles/Standard_NEDC.mat'
data = scio.loadmat(data_path)
car_spd_one = data['speed_vector']
total_milage = np.sum(car_spd_one) / 1000
total_step = 0
step_episode = 0
mean_reward_all = 0
cost_Engine_list = []
cost_all_list = []
cost_Engine_100Km_list = []
mean_reward_list = []
list_even = []
list_odd = []
mean_discrepancy_list = []
SOC_final_list = []
Prius = Prius_model()
for i in range(500, 501): # i represents the number value of checkpoints
DDPG.loadmodel()
SOC = 0.65
SOC_origin = SOC
ep_reward = 0
ep_reward_all = 0
step_episode += 1
SOC_data = []
P_req_list = []
P_out_list = []
Eng_spd_list = []
Eng_trq_list = []
Eng_pwr_list = []
Eng_pwr_opt_list = []
Gen_spd_list = []
Gen_trq_list = []
Gen_pwr_list = []
Mot_spd_list = []
Mot_trq_list = []
Mot_pwr_list = []
Batt_pwr_list = []
inf_batt_list = []
inf_batt_one_list = []
Reward_list = []
Reward_list_all = []
T_list = []
Mot_eta_list = []
Gen_eta_list = []
car_spd = car_spd_one[:, 0]
car_a = car_spd_one[:, 0] - 0
s = np.zeros(s_dim)
s[0] = car_spd / 33.4
s[1] = (car_a - (-1.5)) / (1.5- (-1.5))
s[2] = SOC
for j in range(car_spd_one.shape[1] - 1):
a = DDPG.choose_action(s)
Eng_pwr_opt = (a[0]) * 56000
out, cost, I = Prius.run(car_spd, car_a, Eng_pwr_opt, SOC)
P_req_list.append(float(out['P_req']))
P_out_list.append(float(out['P_out']))
Eng_spd_list.append(float(out['Eng_spd']))
Eng_trq_list.append(float(out['Eng_trq']))
Eng_pwr_list.append(float(out['Eng_pwr']))
Eng_pwr_opt_list.append(float(out['Eng_pwr_opt']))
Mot_spd_list.append(float(out['Mot_spd']))
Mot_trq_list.append(float(out['Mot_trq']))
Mot_pwr_list.append(float(out['Mot_pwr']))
Gen_spd_list.append(float(out['Gen_spd']))
Gen_trq_list.append(float(out['Gen_trq']))
Gen_pwr_list.append(float(out['Gen_pwr']))
Batt_pwr_list.append(float(out['Batt_pwr']))
inf_batt_list.append(int(out['inf_batt']))
inf_batt_one_list.append(int(out['inf_batt_one']))
Mot_eta_list.append(float(out['Mot_eta']))
Gen_eta_list.append(float(out['Gen_eta']))
T_list.append(float(out['T']))
SOC_new = float(out['SOC'])
SOC_data.append(SOC_new)
cost = float(cost)
r = cost
ep_reward += r
Reward_list.append(r)
if SOC_new < 0.6 or SOC_new > 0.85:
r = ((350 * ((0.6 - SOC_new) ** 2)) + cost)
# Obtained from the wheel speed sensor
car_spd = car_spd_one[:, j + 1]
car_a = car_spd_one[:, j + 1] - car_spd_one[:, j]
s_ = np.zeros(s_dim)
s_[0] = car_spd / 33.4
s_[1] = (car_a - (-1.5)) / (1.5- (-1.5))
s_[2] = SOC_new
s = s_
ep_reward_all += r
Reward_list_all.append(r)
total_step += 1
SOC = SOC_new
cost_Engine = (ep_reward / 0.72 / 1000)
cost_all = (ep_reward_all / 0.72 / 1000)
if j == (car_spd_one.shape[1] - 2):
SOC_final_list.append(SOC)
mean_reward = ep_reward_all / car_spd_one.shape[1]
mean_reward_list.append(mean_reward)
cost_Engine += (SOC < SOC_origin) * (SOC_origin - SOC) * (201.6 * 6.5) * 3600 /(42600000) / 0.72
cost_Engine_list.append(cost_Engine)
cost_Engine_100Km_list.append(cost_Engine * (100 / total_milage))
cost_all += (SOC < SOC_origin) * (SOC_origin - SOC) * (201.6 * 6.5) * 3600 /(42600000) / 0.72
cost_all_list.append(cost_all)
print('Episode:', i, ' cost_Engine: %.3f' % cost_Engine, ' Fuel_100Km: %.3f' % (cost_Engine * (100 / total_milage)), ' SOC-final: %.3f' % SOC)
mean_reward_all += mean_reward
if (step_episode % 10) == 0 and step_episode >= 10:
if (step_episode / 10) % 2 == 0:
list_even.append(mean_reward_all)
else:
list_odd.append(mean_reward_all)
mean_reward_all = 0
mean_discrepancy_list = list(map(lambda x, y: y - x, list_even, list_odd))
x = np.arange(0, len(SOC_data), 1)
y = SOC_data
plt.plot(x, y)
plt.xlabel('time')
plt.ylabel('SOC')