Electronics Guide

Parallel Source Architectures

Parallel architectures connect multiple harvesting sources to a common output bus, typically after individual power conditioning stages. Each source operates independently with its own converter that steps up or down the harvester output voltage to match the common bus voltage. This approach provides flexibility in source selection and allows each converter to optimize power extraction from its respective harvester.

The main advantages of parallel architectures include isolation between sources preventing reverse current flow, independent maximum power point tracking for each source, and graceful degradation if individual sources or converters fail. Disadvantages include higher component count with separate converters for each source and the overhead of multiple control systems.

Series Source Architectures

Series architectures stack harvester outputs to achieve higher combined voltages before power conditioning. This approach is most effective when sources produce similar currents, as the series connection forces identical current through all sources. Series connection can reduce the voltage boost ratio required in subsequent power conditioning, potentially improving efficiency.

Challenges with series architectures include the requirement for matched source currents, reduced output when any source underperforms, and complexity in achieving optimal power extraction from sources with different operating points. Bypass mechanisms can mitigate the impact of underperforming sources but add complexity.

Hybrid Series-Parallel Architectures

Practical multi-source systems often combine series and parallel connections to balance the advantages of each approach. Sources with similar characteristics may be connected in series strings, with multiple strings connected in parallel. This approach can reduce component count compared to fully parallel architectures while maintaining better tolerance to source variation than pure series designs.

Single-Inductor Multiple-Input Converters

Single-inductor multiple-input (SIMO) converters use a shared magnetic element to process power from multiple sources, significantly reducing size and cost compared to separate converters. Time-division multiplexing allocates the inductor to different sources during successive switching cycles, with control logic managing the sequencing and timing of source connections.

SIMO converters achieve efficient power combination when sources have similar power levels and do not require simultaneous operation. The switching frequency must be high enough to sample all sources frequently, preventing significant voltage droop between sampling intervals. Cross-regulation between sources requires careful control design to maintain output stability.

Time-Multiplexed Architectures

Time-multiplexed systems periodically switch between sources, dedicating the full power conditioning path to one source at a time. This approach simplifies converter design by avoiding simultaneous multi-source operation, but requires sufficiently fast switching to capture energy from all sources without significant loss during disconnected intervals.

Intelligent source selection algorithms can optimize the time allocation among sources based on instantaneous power availability. Sources with higher power may receive longer connection intervals, while dormant sources can be skipped entirely to focus resources on active energy capture.

Modular Converter Arrays

Modular architectures use arrays of smaller converter modules that can be dynamically assigned to different sources. This approach provides flexibility to allocate conversion resources based on source availability and power levels. Modules may be identical, simplifying manufacturing and enabling hot-swapping, or specialized for different source types.

Centralized or distributed control coordinates module assignment and operation. Redundancy inherent in modular arrays improves reliability, as failed modules can be isolated without system-wide failure. The modular approach scales well from small systems with few sources to large installations with many harvesting elements.

Priority-Based Selection

Priority-based source selection assigns fixed priorities to available sources based on expected power levels, efficiency, or reliability. The system activates the highest-priority available source first, engaging lower-priority sources only when higher-priority options cannot meet power demands. This straightforward approach minimizes control complexity but may not adapt optimally to changing conditions.

Maximum Power Tracking Selection

Dynamic selection based on maximum available power from each source can optimize energy capture under varying conditions. The system periodically samples the maximum power point of each source and allocates conversion resources to maximize total harvested power. This approach requires more sophisticated control and source characterization but achieves better overall performance.

Predictive Source Management

Predictive algorithms use historical data, environmental sensors, or learned patterns to anticipate future source availability. The system can pre-configure for expected conditions, reducing the latency of source transitions and enabling proactive energy management. Machine learning approaches can identify complex patterns in source availability that simple rules cannot capture.

Threshold-Based Activation

Simple threshold-based schemes activate sources when their output exceeds minimum viable levels and deactivate them when output falls below. Hysteresis in the thresholds prevents oscillation near the activation point. This approach requires minimal control overhead but may miss optimization opportunities in the transition regions.

Scalability Principles

Scalable architectures enable systems to grow from single-source to many-source configurations without fundamental redesign. Key principles include standardized interfaces between harvesters and power management, modular power stages that can be added or removed, and distributed control that accommodates varying numbers of sources without centralized bottlenecks.

Plug-and-Play Harvester Integration

Standardized harvester interfaces enable different source types to connect to common power management platforms. Each harvester module may include basic conditioning and characterization circuitry that identifies its type and capabilities to the central power manager. This approach simplifies field configuration and enables mixed-source deployments.

Distributed vs. Centralized Control

Centralized control architectures simplify coordination but create single points of failure and may limit scalability. Distributed control assigns local decision-making to individual source modules, with minimal coordination for system-level optimization. Hybrid approaches combine local autonomy for basic operation with centralized optimization when communication resources permit.

Source-Specific Matching

Different harvester types present different optimal load impedances that vary with operating conditions. Piezoelectric harvesters present capacitive sources with voltage-dependent optimal loads, thermoelectric generators appear as voltage sources with fixed internal resistance, and photovoltaic cells exhibit nonlinear current-voltage characteristics. Each source requires appropriate matching strategy for maximum power extraction.

Adaptive Impedance Matching

Adaptive matching circuits adjust their input impedance to track changing source conditions. Perturb-and-observe algorithms incrementally adjust operating points to find maximum power, while model-based approaches calculate optimal impedance from sensed source parameters. The matching update rate must balance tracking accuracy against the power overhead of frequent adjustments.

Fractional Open-Circuit Voltage Methods

For some source types, the maximum power point occurs at a fixed fraction of the open-circuit voltage. Periodic sampling of open-circuit voltage enables simple calculation of the target operating voltage without complex tracking algorithms. This approach trades some accuracy for reduced control complexity and power consumption.

Buffer Storage Requirements

Multi-source architectures typically require buffer storage to smooth the variable power from multiple sources and match supply to load demands. The storage capacity must accommodate the worst-case mismatch between instantaneous harvested power and load consumption over the expected operating cycle. Batteries, supercapacitors, or hybrid combinations provide different trade-offs between energy density, power density, and cycle life.

Charge Management

Charging buffer storage from multiple sources requires coordination to prevent conflicts and optimize charging efficiency. Simple approaches charge from whichever source is active, while sophisticated systems may preferentially charge from sources with the highest efficiency at current power levels. Temperature monitoring and charge rate limiting protect storage elements from damage.

Storage Hierarchy

Tiered storage architectures use fast-response capacitors for instantaneous power buffering with batteries providing longer-term energy reserves. Power routing logic directs harvested energy to appropriate storage levels based on instantaneous power levels and storage state of charge. This approach optimizes both short-term power delivery and long-term energy balance.

Source Failure Handling

Robust multi-source architectures continue operating when individual sources fail or become unavailable. Isolation mechanisms prevent failed sources from loading the system, while control logic redistributes power extraction among remaining sources. Diagnostics can identify failing sources before complete failure, enabling proactive maintenance in accessible systems.

Converter Redundancy

Critical applications may incorporate redundant power conditioning paths that can take over if primary converters fail. Active redundancy keeps backup paths operating in parallel with primary paths, enabling seamless failover. Standby redundancy activates backup paths only upon primary failure, conserving power during normal operation at the cost of switching transients during failover.

Graceful Degradation

Systems designed for graceful degradation maintain reduced functionality when operating with fewer sources or degraded components. Load shedding prioritizes critical functions when harvested power is insufficient for full operation. Clear status indication informs users or monitoring systems of degraded operating conditions.

Control Processor Selection

Architecture complexity directly impacts control processing requirements. Simple architectures may use analog control or minimal digital logic, while sophisticated adaptive systems require microcontrollers with sufficient processing power for real-time optimization. The control processor's power consumption must be considered relative to harvested power levels.

Communication Requirements

Distributed multi-source systems may require communication between source modules and central power management. Low-power communication protocols such as I2C, SPI, or power-line communication minimize overhead. Wireless communication enables widely distributed harvesters but adds power and complexity.

Startup and Cold-Start

Multi-source architectures must address system startup from completely discharged states. Sources with the lowest startup threshold may be designated as primary cold-start sources, powering initial system activation before engaging additional sources. Cascaded startup sequences bring the system to full operation progressively.

System-Level Efficiency

Overall system efficiency depends on individual source extraction efficiency, power conditioning efficiency, and any losses in power combining and routing. Optimizing one stage in isolation may degrade overall performance if it increases losses elsewhere. System-level analysis and simulation guide balanced optimization across all stages.

Power Management Overhead

Control circuits, sensors, and communication consume power that reduces net output. Power management overhead must be minimized relative to harvested power levels. Duty-cycled operation, event-driven control, and power-gating of inactive circuits reduce overhead. For very low power sources, the simplest control approach that achieves acceptable performance may be optimal.