fcev.py

import os
import random
import numpy as np
import pandas as pd
import gymnasium as gym
from gymnasium.spaces import Box
from scipy.interpolate import interp1d
from dataclasses import dataclass, field

def load_drive_cycle(path_or_dir):
    """Load a drive cycle from a CSV file or a directory containing CSV files.

    Args:
        path_or_dir (str): Path to a CSV file or a directory containing CSVs.

    Returns:
        dict: Dictionary with 'Time' and 'Speed' in SI units (seconds, m/s).

    Raises:
        ValueError: If no CSV file is found or required columns are missing.
    """
    if os.path.isfile(path_or_dir) and path_or_dir.endswith('.csv'):
        csv_path = path_or_dir
    elif os.path.isdir(path_or_dir):
        # 遍历目录及子目录下所有csv文件
        csv_files = []
        for root, _, files in os.walk(path_or_dir):
            for f in files:
                if f.endswith('.csv'):
                    csv_files.append(os.path.join(root, f))
        if not csv_files:
            raise ValueError(f"No CSV file found in directory: {path_or_dir}")
        csv_path = random.choice(csv_files)
        print(f"Randomly selected CSV drive cycle: {csv_path}")
    else:
        raise ValueError(f"Invalid path: {path_or_dir}")

    df = pd.read_csv(csv_path)

    if 'Time' not in df.columns or 'Speed' not in df.columns:
        raise ValueError("CSV must contain 'Time' and 'Speed' columns")

    # Ensure time starts at zero
    time = df['Time'].to_numpy()
    time = time - time[0]

    return {
        'Time': time,
        'Speed': df['Speed'].to_numpy() / 3.6 # Convert km/h to m/s
    }


def fcev_ext(u, w, veh, fd, fc, batt):
    """
    External dynamic model for a fuel cell electric vehicle (FCEV) primarily powered by a fuel cell.
    Supports the main cooling loop with a three-way valve split control structure.

    Inputs:
        u[0]: Fuel cell output ratio (range: 0 to 1)
        u[1]: Total cooling level (normalized, range: 0.2 to 1.0)
        u[2]: Split ratio to the fuel cell system (FCS) (range: 0 to 1)
        w[0]: Vehicle speed (m/s)
        w[1]: Vehicle acceleration (m/s²)

    Returns:
        intVar: List of intermediate variables
        unfeas: Infeasibility flag
    """

    # === Input Processing ===
    v = w[0]  # vehicle speed
    a = w[1]  # vehicle acceleration
    fc_ratio = u[0]  # 燃料电池功率输出比例
    cooling_level = u[1]  # 冷却强度 [0.2, 1.0]
    split_ratio = u[2]  # 三通阀分流比例 α

    # === Wheel Dynamics ===
    wheel_spd = v / veh.wh_radius
    wheel_acc = a / veh.wh_radius

    rolling = veh.mass * veh.gravity * (veh.first_rrc + veh.second_rrc * v)
    aero = veh.aero_coeff * v ** 2
    inertia = veh.mass * a
    veh_force = rolling + aero + inertia

    wheel_trq = veh_force * veh.wh_radius + veh.axle_loss * (wheel_spd != 0)

    # === Final Drive ===
    fd_spd = fd.spdRatio * wheel_spd
    fd_trq = wheel_trq / fd.spdRatio + fd.loss * np.sign(wheel_trq)

    # === Simple Motor Dynamics ===
    shaft_spd = fd_spd
    shaft_trq = fd_trq
    mot_pwr = shaft_spd * shaft_trq

    # === Fuel Cell Ideal Output ===
    fc_ratio = np.clip(fc_ratio, 0, 1)
    fc_pwr = fc_ratio * fc.maxPwr
    batt_pwr = mot_pwr - fc_pwr

    # === Fuel Cell Efficiency & Hydrogen Usage ===
    fc_eff = fc.effMap(fc_pwr)
    fc_eff = np.maximum(fc_eff, np.finfo(float).eps)
    h2_rate = fc.h2Map(fc_pwr)
    # print(f"H2 Rate: {h2_rate}, || Fuel Cell Power: {fc_pwr}")

    # === Cooling System ===
    fc.maxCoolFlow = 0.2  # 假设的最大冷却流速 kg/s
    m_dot_total = cooling_level * fc.maxCoolFlow

    m_dot_fc = split_ratio * m_dot_total
    m_dot_batt = (1 - split_ratio) * m_dot_total

    # === Feasibility Check ===
    fc_unfeas = (fc_pwr > fc.maxPwr) | (fc_pwr < fc.minPwr)
    batt_unfeas = (batt_pwr > batt.maxPwr) | (batt_pwr < batt.minPwr)
    unfeas = fc_unfeas | batt_unfeas

    # === Output Intermediate Variables ===
    intVar = [
        batt_pwr,  # 1
        fc_pwr,  # 2
        h2_rate,  # 3
        np.ones_like(fc_pwr) * shaft_spd,  # 4
        m_dot_fc,  # 5
        m_dot_batt  # 6
    ]

    return intVar, unfeas


def fcev_int(x, w, u, intVar, batt, fc):
    """
     Internal state update function for a fuel cell electric vehicle (FCEV),
     including thermal and electrical models for both battery and fuel cell.

     Args:
         x (list[float]): Current state variables:
             x[0]: State of Charge (SOC)
             x[1]: Fuel cell temperature (T_fc)
             x[2]: Battery core temperature (T_core)
             x[3]: Battery surface temperature (T_surf)
         w (Union[list, dict]): External inputs (e.g., vehicle speed/acc) and previous controls if available.
         u (list[float]): Control inputs:
             u[0]: Fuel cell output ratio (0 to 1)
             u[1]: Normalized total cooling level (0.2 to 1.0)
             u[2]: Coolant split ratio to fuel cell (0 to 1)
         intVar (list[float]): Intermediate variables:
             intVar[0]: Battery power
             intVar[1]: Fuel cell power
             intVar[2]: Hydrogen consumption rate
             intVar[4]: Coolant flow rate through fuel cell
             intVar[5]: Coolant flow rate through battery
         batt: Battery model object (with methods like `ocv()`, `chrgRes()`, `dischrgRes()`, etc.)
         fc: Fuel cell model object (with methods like `effMap()` and `heatCap`)

     Returns:
         tuple:
             x_new (list[float]): Updated state vector [SOC, T_fc, T_core, T_surf]
             stageCost (float): Calculated cost including hydrogen consumption and penalties
             unfeas (bool): Feasibility flag (True if thermal or electrical limits exceeded)
             H2_rate (float): Hydrogen consumption rate
             dT_core (float): Battery core temperature change rate
             dT_fc (float): Fuel cell temperature change rate
    """
    # === Extract state variables ===
    SOC = x[0]
    T_fc = x[1]
    T_core = x[2]
    T_surf = x[3]

    # === Extract intermediate variables ===
    battPwr = intVar[0]
    fcPwr = intVar[1]
    H2_rate = intVar[2]
    m_dot_fc = intVar[4]
    m_dot_batt = intVar[5]

    # === Constants ===
    dt = 1.0                  # Time step (s)
    T_amb = 20.0              # Ambient temperature (°C)
    cp = 4180.0               # Specific heat capacity of coolant (J/kg°C)

    # === Battery electrical model ===
    battPwr_adj = np.where(battPwr > 0, battPwr / 0.95, battPwr * 0.95)
    Rbat = np.where(battPwr > 0, batt.dischrgRes(SOC), batt.chrgRes(SOC))
    Vbat = batt.ocv(SOC)

    sqrt_term = Vbat ** 2 - 4 * Rbat * battPwr_adj
    sqrt_term = np.maximum(sqrt_term, 0)
    Ibatt = (Vbat - np.sqrt(sqrt_term)) / (2 * Rbat)
    Ibatt = np.real(Ibatt)

    # === SOC update ===
    SOC_new = SOC - Ibatt / (batt.maxCap * 3600) * dt
    battUnfeas = (Ibatt > batt.maxCurr(SOC)) | (Ibatt < batt.minCurr(SOC))

    # === Fuel Cell Thermal Model ===
    eta_fc = np.maximum(fc.effMap(fcPwr), np.finfo(float).eps)
    Qgen_fc = (1 - eta_fc) * fcPwr
    Qcool_fc = m_dot_fc * cp * (T_fc - T_amb)
    dT_fc = (Qgen_fc - Qcool_fc) / fc.heatCap
    T_fc_new = T_fc + dt * dT_fc

    # === Battery thermal model ===
    Qgen_batt = Ibatt ** 2 * Rbat
    Rth_in = 0.2
    Ccore = 3000.0
    Csurf = 2000.0
    Qcool_batt = m_dot_batt * cp * (T_surf - T_amb)

    dT_core = (Qgen_batt - (T_core - T_surf) / Rth_in) / Ccore
    dT_surf = ((T_core - T_surf) / Rth_in - Qcool_batt) / Csurf

    T_core_new = T_core + dt * dT_core
    T_surf_new = T_surf + dt * dT_surf

    # === Stage cost calculation ===
    stageCost = H2_rate

    # Add cooling system power penalty (quadratic)
    m_dot_total = m_dot_fc + m_dot_batt
    stageCost += 1e-3 * m_dot_total ** 2

    # Add control smoothness penalty (Δu²)
    if isinstance(w, dict) and "u_prev" in w:
        u_prev = w["u_prev"][0]  # 之前的fcRatio
        fcRatio = u[0]
        delta_u = fcRatio - u_prev
        stageCost += 1e-4 * delta_u ** 2

    # === Temperature feasibility check (hard constraint) ===
    T_ref_fc = 60.0
    T_th_fc = 80.0
    k_fc = 8.0

    # --- Battery degradation penalty ---
    T_ref_batt = 35.0
    T_th_batt = 45.0
    k_batt = 6.0
    if T_core >= 100 or T_fc >= 100:
        T_core = np.clip(T_core, 0, 100)  # 或 120 上限
        T_fc = np.clip(T_fc, 0, 100)
        TempUnfeas = True
    else:
        TempUnfeas = False

    # === Lifetime degradation penalties (soft constraint) ===

    # Fuel cell degradation penalty
    fc_life_penalty = np.where(
        T_fc > T_th_fc,
        1e-2 * np.exp((T_fc - T_ref_fc) / k_fc),
        0.0
    )

    # Battery degradation penalty
    batt_life_penalty = np.where(
        T_core > T_th_batt,
        1e-2 * np.exp((T_core - T_ref_batt) / k_batt),
        0.0
    )


    stageCost += fc_life_penalty + batt_life_penalty

    # === Output new states ===
    x_new = [
        SOC_new,
        T_fc_new,
        T_core_new,
        T_surf_new
    ]

    # Combine feasibility flags
    unfeas = battUnfeas or TempUnfeas

    if unfeas:
        print(battUnfeas, f" {SOC} || {T_core} || {T_surf} || {T_fc} || f{Ibatt} || f{batt.maxCurr(SOC)} || f{batt.minCurr(SOC)}")

    return x_new, stageCost, unfeas, H2_rate, dT_core, dT_fc


@dataclass
class Vehicle:
    mass: float = 1920  # kg
    gravity: float = 9.81
    wh_radius: float = 0.32  # m
    first_rrc: float = 0.009
    second_rrc: float = 0.001
    aero_coeff: float = field(init=False)
    axle_loss: float = 100

    def __post_init__(self):
        Cd = 0.29
        A = 2.3
        rho = 1.225
        self.aero_coeff = 0.5 * Cd * A * rho


@dataclass
class FinalDrive:
    spdRatio: float = 9.0
    loss: float = 50


@dataclass
class FuelCell:
    minPwr: float = 0
    maxPwr: float = 114000
    pwrBrk: np.ndarray = field(default_factory=lambda: np.linspace(0, 114000, 20))
    etaBrk: np.ndarray = field(default_factory=lambda: np.array([
        0.0, 0.25, 0.4, 0.48, 0.53, 0.56, 0.59, 0.6,
        0.605, 0.61, 0.6, 0.59, 0.58, 0.57, 0.56, 0.54,
        0.52, 0.5, 0.48, 0.46
    ]))
    thermEff: float = 0.4
    coolingCoeff: float = 40
    heatCap: float = 20000
    effMap: callable = field(init=False)
    h2Map: callable = field(init=False)

    def __post_init__(self):
        eff_interp = interp1d(self.pwrBrk, self.etaBrk, kind='linear', fill_value='extrapolate')
        self.effMap = lambda P: np.minimum(1, np.maximum(eff_interp(P), np.finfo(float).eps))

        LHV_H2 = 33.3e6  # J/kg
        self.h2Map = lambda P: 1e3 * P / np.maximum(self.effMap(P) * LHV_H2, np.finfo(float).eps)


@dataclass
class Battery:
    maxCap: float = 10  # Ah
    minPwr: float = -20000  # W
    maxPwr: float = 25000  # W
    socBrk: np.ndarray = field(default_factory=lambda: np.linspace(0, 1, 11))
    ocvData: np.ndarray = field(init=False)
    chrgResData: np.ndarray = field(init=False)
    dischrgResData: np.ndarray = field(init=False)
    minCurrData: np.ndarray = field(init=False)
    maxCurrData: np.ndarray = field(init=False)
    ocv: callable = field(init=False)
    chrgRes: callable = field(init=False)
    dischrgRes: callable = field(init=False)
    minCurr: callable = field(init=False)
    maxCurr: callable = field(init=False)
    coolingCoeff: float = 10
    heatCap: float = 6000

    def __post_init__(self):
        self.ocvData = 320 + 20 * self.socBrk
        self.chrgResData = 0.3 - 0.1 * self.socBrk
        self.dischrgResData = 0.2 + 0.05 * (1 - self.socBrk)
        self.minCurrData = -120 + np.zeros_like(self.socBrk)
        self.maxCurrData = 180 + np.zeros_like(self.socBrk)

        self.ocv = interp1d(self.socBrk, self.ocvData, kind='linear', fill_value='extrapolate')
        self.chrgRes = interp1d(self.socBrk, self.chrgResData, kind='linear', fill_value='extrapolate')
        self.dischrgRes = interp1d(self.socBrk, self.dischrgResData, kind='linear', fill_value='extrapolate')
        self.minCurr = interp1d(self.socBrk, self.minCurrData, kind='linear', fill_value='extrapolate')
        self.maxCurr = interp1d(self.socBrk, self.maxCurrData, kind='linear', fill_value='extrapolate')


def fcev_fcdom_data():
    veh = Vehicle()
    fd = FinalDrive()
    fc = FuelCell()
    batt = Battery()
    return veh, fd, fc, batt

def reward_function(stage_cost, speed, beta=0.01, c=10):
    if stage_cost == 0 and speed == 0:
        return 0.0
    return 1 / (1 + np.exp(beta * (stage_cost - c)))

class FCEVEnv(gym.Env):
    """
    Custom OpenAI Gym environment for a fuel cell electric vehicle (FCEV) with
    thermal-electric hybrid modeling. The environment simulates internal vehicle
    dynamics and evaluates control actions based on energy efficiency and constraints.

    Observations:
        [vehicle_speed, acceleration, SOC, T_fc, T_core, T_surf]

    Actions:
        [fuel_cell_output_ratio (0~1), cooling_level (0.2~1.0), coolant_split_ratio (0~1)]

    Reward:
        Based on custom reward function applied to stage cost.
    """
    def __init__(self, drive_cycle):
        super(FCEVEnv, self).__init__()
        self.dt = 1.0

        # Load vehicle, powertrain, battery, and fuel cell parameters
        self.veh, self.fd, self.fc, self.batt = fcev_fcdom_data()

        # Load and interpolate driving cycle to match simulation timestep
        self.speed_profile = np.interp(
            np.arange(0, drive_cycle['Time'][-1] + 1),
            drive_cycle['Time'] - drive_cycle['Time'][0],
            drive_cycle['Speed']
        )
        self.steps = len(self.speed_profile)
        self.step_count = 0

        # Obs Space：[SOC, T_fc, T_core, T_surf, speed, acceleration]
        self.observation_space = Box(low=np.array([-np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf]),
                                            high=np.array([np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]),
                                            dtype=np.float32)

        # Act Space：[fc_ratio, cooling_level, split_ratio]
        self.action_space = Box(low=np.array([0, 0, 0]),
                                       high=np.array([1.0, 1.0, 1.0]),
                                       dtype=np.float32)

        self.reset()

    def reset(self):
        """Reset the simulation to initial state."""
        self.state = [0.5, 25, 25, 25]  # [SOC, T_fc, T_core, T_surf]
        self.step_count = 0
        return self._get_obs(), None

    def _get_obs(self):
        """Get the current observation including vehicle dynamics and system state."""
        speed = self.speed_profile[self.step_count] if self.step_count < self.steps else self.speed_profile[self.step_count - 1]
        if self.step_count + 1 < self.steps:
            next_speed = self.speed_profile[self.step_count + 1]
        else:
            next_speed = self.speed_profile[self.step_count]
        acc = (next_speed - speed) / self.dt
        return np.array([speed, acc] + self.state, dtype=np.float32)

    def step(self, action):
        """
        Apply a control action and update the environment state.

        Args:
            action (list or ndarray): Action input [fc_ratio, cooling_level, split_ratio]

        Returns:
            tuple: (observation, reward, done, unfeasible_flag, info_dict)
        """
        u = [float(a) for a in action]
        speed = self.speed_profile[self.step_count]
        if self.step_count + 1 < self.steps:
            next_speed = self.speed_profile[self.step_count + 1]
        else:
            next_speed = self.speed_profile[self.step_count]

        acc = (next_speed - speed) / self.dt

        w = [speed, acc]

        # 执行外部动力学
        intVar, _ = fcev_ext(u, w, self.veh, self.fd, self.fc, self.batt)

        # 内部状态更新
        x_new, stageCost, unfeas, h2_rate, dT_core, dT_fc = fcev_int(self.state, w, u, intVar, self.batt, self.fc)

        self.state = x_new
        self.step_count += 1
        done = self.step_count >= self.steps - 1 or unfeas
        # print(f"{self.step_count >= self.steps} or {unfeas}")
        obs = self._get_obs()
        reward = reward_function(stageCost, speed) if not unfeas else -99


        return obs, reward, done, unfeas, {"H2_rate": h2_rate * self.dt, "dT_core": dT_core * self.dt, "dT_fc": dT_fc * self.dt}

    def render(self, mode='human'):
        """Render the current step's state to console."""
        print(f"Step {self.step_count}, State: {self.state}")

    def get_logs(self):
        """Return final state and step count for logging."""
        return {
            "final_state": self.state,
            "step_count": self.step_count
        }


if __name__ == '__main__':
    veh, fd, fc, batt = fcev_fcdom_data()

    # Demo
    P_test = np.array([0, 20000, 60000, 110000])
    eff = fc.effMap(P_test)
    h2 = fc.h2Map(P_test)

    print("H2 Efficiency:", eff)
    print("Hydrogen Consumption (g/s):", h2)