Depth of Discharge (DoD)

Depth of Discharge (DoD) is expressed as a percentage representing the fraction of a battery's total capacity that has been consumed. It is calculated as:

DoD (%) = (1 - (Current Capacity / Total Capacity)) * 100

• Total Capacity is the maximum energy the battery can hold when fully charged, which degrades over time (see State of Health).
• Current Capacity is the present energy level, often derived from State of Charge (SoC) readings.
• A DoD of 80% means 80% of the battery's usable energy has been withdrawn, leaving 20% remaining.

DoD is the primary driver of battery degradation. Each charge-discharge cycle causes incremental wear, but the depth of each cycle is more critical than frequency.

• Shallow Cycling (e.g., 20-30% DoD) causes minimal stress, dramatically extending total cycle life.
• Deep Cycling (e.g., 80-100% DoD) accelerates chemical breakdown, leading to rapid capacity fade and increased internal resistance.
• Battery Degradation Models use DoD history as a key input to predict Remaining Useful Life (RUL). Orchestration platforms use these models to schedule agents for maintenance or replacement.

Fleet orchestration defines permissible DoD ranges to balance task completion with battery preservation.

• Maximum DoD: A hard safety limit (e.g., 90%) to prevent over-discharge, which can cause permanent damage. This defines the Minimum Charge Threshold.
• Optimal DoD Range: A narrower band (e.g., 20-80%) where the battery operates most efficiently with minimal degradation impact. Battery-aware scheduling aims to keep agents within this window.
• Energy Buffer: A reserved portion of capacity (e.g., the bottom 10%) maintained for emergency maneuvers or unexpected operational delays, ensuring agents always have a contingency reserve.

DoD is a dynamic constraint in real-time replanning engines and spatial-temporal scheduling.

• Energy-Aware Routing: Path planning algorithms evaluate estimated energy consumption for candidate routes, predicting the resulting DoD to avoid exceeding the maximum threshold.
• Battery-Aware Task Sequencing: The order of tasks is optimized to cluster high-energy activities when DoD is low, or to ensure an agent's route passes near charging stations as DoD approaches its limit.
• Charge Scheduling Algorithms use predicted DoD from assigned tasks to preemptively book charging slots, preventing operational downtime.

DoD and State of Charge (SoC) are complementary metrics describing the same physical state from opposite perspectives.

• SoC measures energy remaining: SoC = 100% - DoD.
• Operational Focus: DoD is often used for degradation modeling and long-term health planning, as it directly quantifies the stress applied per cycle.
• Tactical Focus: SoC is typically used for real-time fleet state estimation and immediate task allocation decisions, as it answers "how much runtime is left?"
• A unified Battery Management System (BMS) API provides both metrics to the orchestration middleware.

Charging logic is triggered and optimized based on DoD thresholds.

• Opportunity Charging: Agents with moderate DoD (e.g., 60%) may use short idle periods for partial recharges, keeping the average cycle depth shallow.
• Scheduled Charging: Deep-discharge events (e.g., DoD > 80%) are planned for periods of low energy cost or high renewable availability (load shifting).
• Charge Discharge Cycle Optimization: The platform strategically avoids consistently taking batteries to high DoD, instead mixing deep and shallow cycles to minimize overall degradation. This is a key output of a battery constraint solver.

State of Charge (SoC) is the percentage of a battery's total usable capacity that is currently available as electrical energy. It is the primary real-time metric for operational planning.

• Relationship to DoD: DoD = 100% - SoC. If a battery has a 60% SoC, its DoD for that cycle is 40%.
• Use in Scheduling: The orchestration platform uses real-time SoC to make dispatch decisions, preventing agents from being assigned tasks that would violate their minimum charge threshold.

State of Health (SoH) is a percentage representing a battery's current condition relative to its original factory capacity. A 70% SoH means the battery can only hold 70% of its original energy.

• Impact on DoD: As SoH degrades, the absolute energy represented by a given DoD percentage decreases. A 50% DoD on a degraded battery withdraws less total energy than the same DoD on a new battery.
• Planning Implication: Battery degradation models use historical DoD and cycle data to predict future SoH, informing long-term asset replacement schedules.

A battery degradation model is a predictive algorithm that estimates capacity fade and resistance increase over time. It is foundational for charge discharge cycle optimization.

• Key Inputs: These models are primarily driven by Depth of Discharge (DoD) per cycle, cumulative cycle count, average State of Charge (SoC), and operating temperature.
• Output for Scheduling: The model informs the energy cost function within the scheduler, assigning a higher 'cost' to deep discharge cycles to incentivize shallower DoD and extend Remaining Useful Life (RUL).

Remaining Useful Life (RUL) is a forecast of the time or number of cycles until a battery fails to meet a critical performance threshold, such as holding 80% of its rated capacity.

• Calculation: RUL is predicted by feeding current State of Health (SoH) and projected future usage (estimated DoD cycles) into a battery degradation model.
• Operational Strategy: To maximize RUL, battery-aware scheduling algorithms aim to minimize stress factors, notably avoiding consistently high DoD and optimizing for opportunity charging to keep cycles shallow.

A charge scheduling algorithm is an optimization routine that determines the optimal timing, duration, and location for fleet recharge events. DoD is a central constraint.

• Objective: Minimize total cost (energy + degradation) while ensuring all agents meet their minimum charge threshold for upcoming tasks.
• How it uses DoD: The algorithm projects future DoD based on assigned tasks. It then schedules charging to interrupt deep discharge cycles (e.g., via opportunity charging) or to initiate a full charge after a planned deep cycle, directly managing the Depth of Discharge experienced by each battery.

Energy-aware routing is a path-planning method that selects a route minimizing total energy consumption, which directly influences the Depth of Discharge required for a task.

• Factors: Considers terrain gradient, required acceleration/deceleration (and potential regenerative braking), payload, and ambient temperature.
• Integration with DoD: By choosing a more energy-efficient route, the agent completes its task with a lower final DoD. This leaves more energy buffer for unexpected work and reduces the depth of the discharge cycle, decreasing degradation.