Automotive Expert

Fleet Management System

from dataclasses import dataclass
from datetime import datetime, timedelta
from typing import List, Optional
from decimal import Decimal
from enum import Enum
import numpy as np

class VehicleStatus(Enum):
    ACTIVE = "active"
    IDLE = "idle"
    MAINTENANCE = "maintenance"
    OUT_OF_SERVICE = "out_of_service"

class FuelType(Enum):
    GASOLINE = "gasoline"
    DIESEL = "diesel"
    ELECTRIC = "electric"
    HYBRID = "hybrid"
    CNG = "cng"

@dataclass
class Vehicle:
    """Fleet vehicle information"""
    vehicle_id: str
    vin: str  # Vehicle Identification Number
    make: str
    model: str
    year: int
    license_plate: str
    fuel_type: FuelType
    status: VehicleStatus
    odometer_km: int
    last_service_km: int
    next_service_km: int
    assigned_driver_id: Optional[str]
    location: tuple  # (latitude, longitude)
    fuel_level_percent: float

@dataclass
class Trip:
    """Vehicle trip record"""
    trip_id: str
    vehicle_id: str
    driver_id: str
    start_time: datetime
    end_time: Optional[datetime]
    start_location: tuple
    end_location: Optional[tuple]
    distance_km: float
    fuel_consumed_liters: float
    average_speed_kmh: float
    max_speed_kmh: float
    harsh_braking_count: int
    harsh_acceleration_count: int

class FleetManagementSystem:
    """Fleet management and telematics system"""

    def __init__(self):
        self.vehicles = {}
        self.trips = []
        self.maintenance_schedules = []

    def track_vehicle_location(self, vehicle_id: str) -> dict:
        """Track real-time vehicle location"""
        vehicle = self.vehicles.get(vehicle_id)
        if not vehicle:
            return {'error': 'Vehicle not found'}

        # Get GPS data from telematics device
        location = self._get_gps_location(vehicle_id)
        speed = self._get_current_speed(vehicle_id)
        heading = self._get_heading(vehicle_id)

        vehicle.location = location

        return {
            'vehicle_id': vehicle_id,
            'location': {
                'latitude': location[0],
                'longitude': location[1]
            },
            'speed_kmh': speed,
            'heading': heading,
            'timestamp': datetime.now().isoformat(),
            'status': vehicle.status.value
        }

    def start_trip(self, vehicle_id: str, driver_id: str) -> Trip:
        """Start a new trip"""
        vehicle = self.vehicles.get(vehicle_id)
        if not vehicle:
            raise ValueError("Vehicle not found")

        trip = Trip(
            trip_id=self._generate_trip_id(),
            vehicle_id=vehicle_id,
            driver_id=driver_id,
            start_time=datetime.now(),
            end_time=None,
            start_location=vehicle.location,
            end_location=None,
            distance_km=0.0,
            fuel_consumed_liters=0.0,
            average_speed_kmh=0.0,
            max_speed_kmh=0.0,
            harsh_braking_count=0,
            harsh_acceleration_count=0
        )

        vehicle.status = VehicleStatus.ACTIVE
        self.trips.append(trip)

        return trip

    def end_trip(self, trip_id: str) -> dict:
        """End trip and calculate metrics"""
        trip = next((t for t in self.trips if t.trip_id == trip_id), None)
        if not trip:
            return {'error': 'Trip not found'}

        vehicle = self.vehicles.get(trip.vehicle_id)

        trip.end_time = datetime.now()
        trip.end_location = vehicle.location

        # Calculate trip metrics
        duration_hours = (trip.end_time - trip.start_time).total_seconds() / 3600
        trip.average_speed_kmh = trip.distance_km / duration_hours if duration_hours > 0 else 0

        # Calculate fuel efficiency
        fuel_efficiency = trip.distance_km / trip.fuel_consumed_liters if trip.fuel_consumed_liters > 0 else 0

        # Calculate driver score
        driver_score = self._calculate_driver_score(trip)

        vehicle.status = VehicleStatus.IDLE

        return {
            'trip_id': trip_id,
            'duration_hours': duration_hours,
            'distance_km': trip.distance_km,
            'fuel_consumed': trip.fuel_consumed_liters,
            'fuel_efficiency_km_per_liter': fuel_efficiency,
            'average_speed': trip.average_speed_kmh,
            'max_speed': trip.max_speed_kmh,
            'harsh_events': trip.harsh_braking_count + trip.harsh_acceleration_count,
            'driver_score': driver_score
        }

    def _calculate_driver_score(self, trip: Trip) -> float:
        """Calculate driver safety score"""
        score = 100.0

        # Penalize harsh events
        score -= trip.harsh_braking_count * 5
        score -= trip.harsh_acceleration_count * 5

        # Penalize speeding
        if trip.max_speed_kmh > 120:
            score -= (trip.max_speed_kmh - 120) * 0.5

        # Penalize low fuel efficiency
        # Implementation would compare to vehicle baseline

        return max(0.0, min(100.0, score))

    def schedule_maintenance(self, vehicle_id: str) -> dict:
        """Schedule vehicle maintenance"""
        vehicle = self.vehicles.get(vehicle_id)
        if not vehicle:
            return {'error': 'Vehicle not found'}

        # Check if maintenance is due
        km_since_service = vehicle.odometer_km - vehicle.last_service_km
        km_until_service = vehicle.next_service_km - vehicle.odometer_km

        if km_until_service <= 1000:  # Within 1000km of service
            maintenance_type = self._determine_maintenance_type(km_since_service)

            schedule = {
                'vehicle_id': vehicle_id,
                'maintenance_type': maintenance_type,
                'current_odometer': vehicle.odometer_km,
                'recommended_by_odometer': vehicle.next_service_km,
                'urgency': 'high' if km_until_service <= 500 else 'medium',
                'estimated_cost': self._estimate_maintenance_cost(maintenance_type)
            }

            self.maintenance_schedules.append(schedule)

            return schedule

        return {
            'vehicle_id': vehicle_id,
            'maintenance_required': False,
            'km_until_service': km_until_service
        }

    def optimize_routes(self, deliveries: List[dict]) -> dict:
        """Optimize delivery routes for fleet"""
        # Simplified route optimization
        # In production, would use sophisticated algorithms (TSP, VRP)

        available_vehicles = [
            v for v in self.vehicles.values()
            if v.status == VehicleStatus.IDLE
        ]

        if not available_vehicles:
            return {'error': 'No available vehicles'}

        # Assign deliveries to vehicles
        assignments = []
        for i, delivery in enumerate(deliveries):
            vehicle = available_vehicles[i % len(available_vehicles)]

            route = self._calculate_route(
                vehicle.location,
                delivery['destination']
            )

            assignments.append({
                'vehicle_id': vehicle.vehicle_id,
                'delivery_id': delivery['delivery_id'],
                'route': route,
                'estimated_distance_km': route['distance'],
                'estimated_time_minutes': route['duration'],
                'estimated_fuel_cost': self._estimate_fuel_cost(
                    route['distance'],
                    vehicle.fuel_type
                )
            })

        return {
            'total_deliveries': len(deliveries),
            'vehicles_assigned': len(set(a['vehicle_id'] for a in assignments)),
            'assignments': assignments,
            'total_distance_km': sum(a['estimated_distance_km'] for a in assignments),
            'total_estimated_cost': sum(a['estimated_fuel_cost'] for a in assignments)
        }

    def analyze_fleet_utilization(self) -> dict:
        """Analyze fleet utilization and efficiency"""
        total_vehicles = len(self.vehicles)

        active = sum(1 for v in self.vehicles.values() if v.status == VehicleStatus.ACTIVE)
        idle = sum(1 for v in self.vehicles.values() if v.status == VehicleStatus.IDLE)
        maintenance = sum(1 for v in self.vehicles.values() if v.status == VehicleStatus.MAINTENANCE)

        utilization_rate = (active / total_vehicles * 100) if total_vehicles > 0 else 0

        # Calculate average fuel efficiency
        recent_trips = self.trips[-100:]  # Last 100 trips
        if recent_trips:
            avg_fuel_efficiency = np.mean([
                t.distance_km / t.fuel_consumed_liters
                for t in recent_trips
                if t.fuel_consumed_liters > 0
            ])
        else:
            avg_fuel_efficiency = 0

        return {
            'total_vehicles': total_vehicles,
            'status_breakdown': {
                'active': active,
                'idle': idle,
                'maintenance': maintenance,
                'out_of_service': total_vehicles - active - idle - maintenance
            },
            'utilization_rate': utilization_rate,
            'average_fuel_efficiency': avg_fuel_efficiency,
            'recommendation': 'Reduce fleet size' if utilization_rate < 60 else
                           'Expand fleet' if utilization_rate > 90 else
                           'Optimal'
        }

    def _determine_maintenance_type(self, km_since_service: int) -> str:
        """Determine type of maintenance required"""
        if km_since_service >= 100000:
            return "major_service"
        elif km_since_service >= 50000:
            return "intermediate_service"
        else:
            return "routine_service"

    def _estimate_maintenance_cost(self, maintenance_type: str) -> Decimal:
        """Estimate maintenance cost"""
        costs = {
            'routine_service': Decimal('150'),
            'intermediate_service': Decimal('500'),
            'major_service': Decimal('1500')
        }
        return costs.get(maintenance_type, Decimal('200'))

    def _calculate_route(self, start: tuple, end: tuple) -> dict:
        """Calculate route between two points"""
        # Would use routing API (Google Maps, Mapbox, etc.)
        # Simplified calculation
        from math import radians, sin, cos, sqrt, atan2

        lat1, lon1 = radians(start[0]), radians(start[1])
        lat2, lon2 = radians(end[0]), radians(end[1])

        dlat = lat2 - lat1
        dlon = lon2 - lon1

        a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
        c = 2 * atan2(sqrt(a), sqrt(1-a))

        distance_km = 6371 * c  # Earth radius in km

        return {
            'distance': distance_km,
            'duration': distance_km / 60 * 60  # Assume 60 km/h average, return minutes
        }

    def _estimate_fuel_cost(self, distance_km: float, fuel_type: FuelType) -> Decimal:
        """Estimate fuel cost for trip"""
        fuel_prices = {
            FuelType.GASOLINE: Decimal('1.50'),  # per liter
            FuelType.DIESEL: Decimal('1.40'),
            FuelType.ELECTRIC: Decimal('0.30'),  # per kWh equivalent
            FuelType.HYBRID: Decimal('1.20'),
            FuelType.CNG: Decimal('1.00')
        }

        fuel_efficiency = 8.0  # km per liter (average)
        fuel_needed = distance_km / fuel_efficiency
        fuel_price = fuel_prices.get(fuel_type, Decimal('1.50'))

        return Decimal(str(fuel_needed)) * fuel_price

    def _get_gps_location(self, vehicle_id: str) -> tuple:
        """Get GPS location from telematics device"""
        # Implementation would connect to telematics API
        return (40.7128, -74.0060)  # Placeholder

    def _get_current_speed(self, vehicle_id: str) -> float:
        """Get current vehicle speed"""
        return np.random.uniform(0, 100)  # Placeholder

    def _get_heading(self, vehicle_id: str) -> float:
        """Get vehicle heading in degrees"""
        return np.random.uniform(0, 360)  # Placeholder

    def _generate_trip_id(self) -> str:
        import uuid
        return f"TRIP-{uuid.uuid4().hex[:10].upper()}"

Connected Vehicle Platform

@dataclass
class VehicleTelemetry:
    """Real-time vehicle telemetry data"""
    vehicle_id: str
    timestamp: datetime
    location: tuple
    speed_kmh: float
    rpm: int
    engine_temp_c: float
    battery_voltage: float
    fuel_level_percent: float
    odometer_km: int
    dtc_codes: List[str]  # Diagnostic Trouble Codes

class ConnectedVehiclePlatform:
    """Connected car platform with OTA updates"""

    def __init__(self):
        self.vehicles = {}
        self.telemetry_buffer = []
        self.ota_updates = {}

    def process_telemetry(self, telemetry: VehicleTelemetry) -> dict:
        """Process incoming telemetry data"""
        self.telemetry_buffer.append(telemetry)

        # Analyze telemetry for anomalies
        alerts = []

        # Check engine temperature
        if telemetry.engine_temp_c > 110:
            alerts.append({
                'type': 'high_engine_temp',
                'severity': 'warning',
                'value': telemetry.engine_temp_c,
                'message': 'Engine temperature above normal'
            })

        # Check battery voltage
        if telemetry.battery_voltage < 12.0:
            alerts.append({
                'type': 'low_battery',
                'severity': 'warning',
                'value': telemetry.battery_voltage,
                'message': 'Battery voltage low'
            })

        # Check for diagnostic trouble codes
        if telemetry.dtc_codes:
            alerts.append({
                'type': 'dtc_codes',
                'severity': 'critical',
                'codes': telemetry.dtc_codes,
                'message': f'{len(telemetry.dtc_codes)} diagnostic code(s) detected'
            })

        # Check for harsh driving
        if len(self.telemetry_buffer) >= 2:
            prev = self.telemetry_buffer[-2]
            if telemetry.vehicle_id == prev.vehicle_id:
                time_diff = (telemetry.timestamp - prev.timestamp).total_seconds()
                if time_diff > 0:
                    acceleration = (telemetry.speed_kmh - prev.speed_kmh) / time_diff

                    if abs(acceleration) > 5:  # > 5 km/h per second
                        alerts.append({
                            'type': 'harsh_driving',
                            'severity': 'info',
                            'acceleration': acceleration,
                            'message': 'Harsh acceleration/braking detected'
                        })

        return {
            'vehicle_id': telemetry.vehicle_id,
            'timestamp': telemetry.timestamp.isoformat(),
            'alerts': alerts,
            'health_score': self._calculate_vehicle_health(telemetry)
        }

    def deploy_ota_update(self,
                         vehicle_ids: List[str],
                         update_package: dict) -> dict:
        """Deploy over-the-air software update"""
        update_id = self._generate_update_id()

        ota_update = {
            'update_id': update_id,
            'version': update_package['version'],
            'description': update_package['description'],
            'package_size_mb': update_package['size_mb'],
            'target_vehicles': vehicle_ids,
            'deployed_at': datetime.now(),
            'status_by_vehicle': {}
        }

        for vehicle_id in vehicle_ids:
            # Schedule update for vehicle
            ota_update['status_by_vehicle'][vehicle_id] = {
                'status': 'scheduled',
                'download_progress': 0,
                'install_progress': 0
            }

        self.ota_updates[update_id] = ota_update

        return {
            'update_id': update_id,
            'vehicles_targeted': len(vehicle_ids),
            'estimated_completion': 'Within 48 hours'
        }

    def diagnose_vehicle(self, vehicle_id: str, dtc_codes: List[str]) -> dict:
        """Diagnose vehicle issues from DTC codes"""
        diagnoses = []

        for code in dtc_codes:
            diagnosis = self._lookup_dtc_code(code)
            diagnoses.append(diagnosis)

        # Calculate severity
        max_severity = max(d['severity'] for d in diagnoses)

        return {
            'vehicle_id': vehicle_id,
            'dtc_codes': dtc_codes,
            'diagnoses': diagnoses,
            'overall_severity': max_severity,
            'service_recommended': max_severity in ['high', 'critical']
        }

    def _calculate_vehicle_health(self, telemetry: VehicleTelemetry) -> float:
        """Calculate overall vehicle health score"""
        score = 100.0

        # Engine temperature
        if telemetry.engine_temp_c > 110:
            score -= 15
        elif telemetry.engine_temp_c > 100:
            score -= 5

        # Battery voltage
        if telemetry.battery_voltage < 11.5:
            score -= 20
        elif telemetry.battery_voltage < 12.0:
            score -= 10

        # DTC codes
        score -= len(telemetry.dtc_codes) * 15

        return max(0.0, score)

    def _lookup_dtc_code(self, code: str) -> dict:
        """Lookup diagnostic trouble code"""
        # Simplified DTC lookup
        # In production, would use comprehensive OBD-II code database

        dtc_database = {
            'P0171': {
                'description': 'System Too Lean (Bank 1)',
                'severity': 'medium',
                'possible_causes': ['Vacuum leak', 'Faulty MAF sensor', 'Fuel filter clogged']
            },
            'P0300': {
                'description': 'Random/Multiple Cylinder Misfire Detected',
                'severity': 'high',
                'possible_causes': ['Faulty spark plugs', 'Ignition coil failure', 'Fuel injector issue']
            }
        }

        return dtc_database.get(code, {
            'description': f'Unknown code: {code}',
            'severity': 'medium',
            'possible_causes': ['Requires diagnostic scan']
        })

    def _generate_update_id(self) -> str:
        import uuid
        return f"OTA-{uuid.uuid4().hex[:8].upper()}"

Electric Vehicle Management

class ElectricVehicleManagement:
    """EV-specific management functions"""

    def __init__(self):
        self.charging_stations = {}
        self.charging_sessions = []

    def calculate_range(self,
                       battery_capacity_kwh: float,
                       battery_soc_percent: float,
                       consumption_kwh_per_km: float) -> dict:
        """Calculate remaining range for EV"""
        available_energy = battery_capacity_kwh * (battery_soc_percent / 100)
        range_km = available_energy / consumption_kwh_per_km

        # Adjust for temperature (simplified)
        # Cold weather reduces range by up to 40%
        temperature_factor = 0.8  # Assume moderate conditions

        adjusted_range = range_km * temperature_factor

        return {
            'nominal_range_km': range_km,
            'adjusted_range_km': adjusted_range,
            'battery_soc_percent': battery_soc_percent,
            'available_energy_kwh': available_energy
        }

    def find_charging_stations(self,
                              current_location: tuple,
                              max_distance_km: float) -> List[dict]:
        """Find nearby charging stations"""
        nearby_stations = []

        for station_id, station in self.charging_stations.items():
            distance = self._calculate_distance(current_location, station['location'])

            if distance <= max_distance_km:
                nearby_stations.append({
                    'station_id': station_id,
                    'name': station['name'],
                    'location': station['location'],
                    'distance_km': distance,
                    'available_chargers': station['available_chargers'],
                    'charging_speed_kw': station['max_power_kw'],
                    'cost_per_kwh': station['cost_per_kwh']
                })

        # Sort by distance
        nearby_stations.sort(key=lambda x: x['distance_km'])

        return nearby_stations

    def optimize_charging_schedule(self,
                                  battery_capacity_kwh: float,
                                  current_soc_percent: float,
                                  target_soc_percent: float,
                                  departure_time: datetime) -> dict:
        """Optimize EV charging schedule based on electricity rates"""
        energy_needed = battery_capacity_kwh * ((target_soc_percent - current_soc_percent) / 100)

        # Get electricity rate schedule
        rate_schedule = self._get_electricity_rates(departure_time)

        # Find lowest rate period
        optimal_period = min(rate_schedule, key=lambda x: x['rate'])

        charging_duration_hours = energy_needed / 7.0  # Assume 7kW home charger

        return {
            'energy_needed_kwh': energy_needed,
            'optimal_start_time': optimal_period['start_time'].isoformat(),
            'charging_duration_hours': charging_duration_hours,
            'estimated_cost': energy_needed * float(optimal_period['rate']),
            'will_complete_by': (optimal_period['start_time'] +
                               timedelta(hours=charging_duration_hours)).isoformat()
        }

    def _calculate_distance(self, point1: tuple, point2: tuple) -> float:
        """Calculate distance between two points"""
        from math import radians, sin, cos, sqrt, atan2

        lat1, lon1 = radians(point1[0]), radians(point1[1])
        lat2, lon2 = radians(point2[0]), radians(point2[1])

        dlat = lat2 - lat1
        dlon = lon2 - lon1

        a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
        c = 2 * atan2(sqrt(a), sqrt(1-a))

        return 6371 * c  # Earth radius in km

    def _get_electricity_rates(self, date: datetime) -> List[dict]:
        """Get time-of-use electricity rates"""
        # Simplified rate schedule
        # Off-peak: 11 PM - 7 AM
        # Peak: 2 PM - 8 PM
        # Mid-peak: all other times

        return [
            {
                'start_time': date.replace(hour=23, minute=0),
                'end_time': date.replace(hour=7, minute=0) + timedelta(days=1),
                'rate': Decimal('0.08')  # $0.08/kWh
            },
            {
                'start_time': date.replace(hour=14, minute=0),
                'end_time': date.replace(hour=20, minute=0),
                'rate': Decimal('0.25')  # $0.25/kWh
            }
        ]

Best Practices

Fleet Management

• Track all vehicle metrics in real-time
• Implement predictive maintenance
• Optimize routes for fuel efficiency
• Monitor driver behavior
• Use telematics for theft prevention
• Maintain detailed service records
• Implement fuel management systems

Connected Vehicles

• Ensure secure V2X communication
• Implement robust cybersecurity
• Use encrypted data transmission
• Support OTA updates
• Monitor vehicle health continuously
• Provide driver assistance features
• Enable remote diagnostics

EV Management

• Optimize charging schedules
• Monitor battery health
• Provide range prediction
• Support multiple charging networks
• Implement thermal management
• Track total cost of ownership
• Enable smart grid integration

Safety and Compliance

• Follow ISO 26262 for safety-critical systems
• Implement fail-safe mechanisms
• Conduct regular safety audits
• Maintain compliance with emissions standards
• Support vehicle recall management
• Implement driver identification
• Provide emergency response features

Anti-Patterns

❌ No telematics or GPS tracking ❌ Reactive maintenance only ❌ Manual route planning ❌ Ignoring driver behavior data ❌ No vehicle diagnostics ❌ Poor fuel management ❌ Inadequate cybersecurity ❌ No OTA update capability ❌ Inefficient EV charging

Resources

• AUTOSAR: https://www.autosar.org/
• ISO 26262: https://www.iso.org/standard/68383.html
• SAE International: https://www.sae.org/
• OBD-II Standards: https://www.obdii.com/
• CAN Bus Specification: https://www.can-cia.org/
• Automotive Edge Computing Consortium: https://aecc.org/
• CharIN (EV Charging): https://www.charin.global/