Regenerative Braking Model

The core function of the model is to calculate the recoverable kinetic energy during deceleration. It uses the formula E = 0.5 * m * (v_i² - v_f²), where m is the agent's mass and v is velocity. The model then applies an efficiency factor (typically 60-70%) to account for losses in the motor-generator, power electronics, and battery charging circuit. This estimate is fed directly into the Energy Consumption Model to predict net energy use over a route.

The model must integrate with the agent's planned velocity profile to identify deceleration events. It analyzes the commanded deceleration rate (m/s²) to determine if regenerative braking is feasible. - Service braking: Moderate deceleration ideal for energy recovery. - Emergency braking: High deceleration exceeds regenerative capacity; friction brakes engage. - Coasting: Zero deceleration; no energy recovery. The model distinguishes these states to avoid overestimating recovered energy.

A critical constraint within the model is the battery's current State of Charge (SoC). The algorithm includes a gating function that prevents energy recovery if the battery is near full capacity to avoid overcharging and thermal runaway. - SoC > 95%: Regenerative braking is disabled; energy is dissipated as heat. - SoC < 80%: Full regenerative capacity is available. This logic protects the Battery Management System (BMS) and preserves long-term State of Health (SoH).

Advanced models pre-compute energy recovery potential along a planned route by analyzing its topological features. The algorithm ingests a digital map with elevation data and planned stop points to forecast net energy gain. - Downhill segments: Predict sustained regeneration. - Stop sign intersections: Predict discrete recovery events. This pre-computation allows the Energy-Aware Routing engine to select paths that maximize regeneration, not just minimize distance.

Regenerative braking generates heat in the motor windings, inverter, and battery cells. The model incorporates a battery thermal model to derate recovery current if component temperatures exceed safe thresholds. During aggressive, repeated deceleration (e.g., a loaded agent descending a ramp), the model will progressively limit regeneration power to prevent thermal derating or damage. This ensures the Battery Health Index (BHI) is not degraded by aggressive energy recovery.

The regenerative braking model's output is a direct input to the Battery-Aware Task Sequencing and Charge Scheduling Algorithm. By accurately predicting energy recovery, the orchestrator can: - Reduce the required Energy Buffer for a mission. - Delay or shorten Opportunity Charging sessions. - Assign heavier payloads to routes with high regeneration potential. This creates a closed-loop system where the physics of energy recovery directly optimizes fleet productivity.

The foundational metric for any energy recovery calculation. SoC represents the current electrical energy stored in a battery as a percentage of its fully charged capacity. A regenerative braking model must know the precise SoC to determine if recovered energy can be accepted without overcharging. Key characteristics:

• Directly limits the amount of recoverable kinetic energy
• Requires high-precision estimation via coulomb counting or Kalman filtering
• A battery at 95% SoC may reject regenerative current to prevent damage

A predictive algorithm that estimates the power draw of a mobile agent based on its planned route, speed, payload, and operational state. This model provides the baseline against which regenerative braking savings are measured. Core inputs include:

• Terrain gradient and surface friction coefficients
• Acceleration and deceleration profiles
• Payload mass and aerodynamic drag
• Auxiliary system loads (sensors, compute, lighting)

A mathematical or data-driven representation that predicts the loss of battery capacity and performance over time. Regenerative braking events, while beneficial for range, contribute to cycle life consumption. This model quantifies the trade-off. Critical factors tracked:

• Incremental charge/discharge cycles from micro-regen events
• Depth of Discharge (DoD) patterns
• Temperature spikes during high-current regen
• Calendar aging vs. cycle aging contributions

A path planning algorithm that selects routes by optimizing for minimal net energy consumption, not just shortest distance. It integrates the regenerative braking model to favor routes with downhill segments where kinetic energy can be recovered. Optimization variables:

• Elevation changes and slope angles
• Stop-and-go traffic patterns vs. steady-state cruising
• Predicted deceleration events at intersections
• Total energy cost including regen credits

A predictive simulation of a battery's temperature changes during operation and charging. High-current regenerative braking generates significant heat, which must be managed to prevent accelerated degradation or safety limits. Thermal constraints on regen:

• Maximum regen current may be derated at high temperatures
• Active cooling system power draw offsets net energy gain
• Cold batteries accept regen current less efficiently
• Thermal runaway prevention overrides energy recovery goals

A mathematical component within a scheduling optimizer that assigns a cost value to energy consumption, incorporating time-of-use electricity rates and battery degradation. This function monetizes the value of regenerative braking. Cost components:

• Grid electricity price at time of charging ($/kWh)
• Battery cycle life amortization cost ($/cycle)
• Carbon intensity of marginal grid power (gCO2/kWh)
• Regenerative energy treated as negative cost (credit)