Electronics Guide

Single-Inductor Multiple-Input Architectures

Single-inductor multiple-input (SIMI) converters share a single magnetic element among multiple harvesting sources, dramatically reducing the size, cost, and complexity of hybrid power management. These converters use time-division multiplexing to allocate the inductor to different sources during successive switching cycles. Advanced control sequences can achieve simultaneous energy extraction from multiple sources through interleaved operation.

The primary challenge in SIMI design is managing cross-regulation, where changes in one source's operating point affect the power extracted from other sources. Careful control loop design, appropriate switching sequences, and adequate output capacitance help maintain stable operation. The switching frequency must be high enough to sample all sources frequently while maintaining reasonable efficiency.

Multiple-Input Buck Converters

When multiple sources produce voltages higher than the desired output, multiple-input buck converters can efficiently step down and combine the sources. Each input has its own high-side switch, with a shared inductor and output stage. Time-multiplexed control activates one input at a time, or more sophisticated control enables simultaneous conduction from multiple sources with appropriate current sharing.

Multiple-Input Boost Converters

Low-voltage harvesting sources such as thermoelectric generators and single photovoltaic cells typically require boost conversion. Multiple-input boost topologies share the boost inductor and output diode among sources, with separate low-side switches for each input. The topology naturally supports maximum power point tracking through duty cycle adjustment for each source.

Buck-Boost and SEPIC Topologies

When source voltages vary above and below the desired output voltage, buck-boost or SEPIC (Single-Ended Primary-Inductor Converter) topologies provide the flexibility to handle the full range. Multiple-input versions of these converters can accommodate sources with widely varying voltage levels, though the additional complexity and component count may reduce efficiency compared to simpler topologies.

Resonant and Soft-Switching Converters

High-frequency operation benefits from resonant or soft-switching techniques that reduce switching losses. Zero-voltage switching (ZVS) and zero-current switching (ZCS) topologies minimize the power dissipated during transistor transitions. These techniques become increasingly important as switching frequencies increase to reduce magnetic component sizes in space-constrained hybrid harvesting systems.

Independent MPPT for Each Source

The most straightforward approach to maximizing power from multiple sources implements independent MPPT controllers for each harvester. Each controller tracks its source's optimal operating point without regard to other sources. This approach guarantees optimal power extraction from each source but requires separate control loops and may require separate power stages for each source.

Perturb-and-Observe Algorithms

Perturb-and-observe (P&O) algorithms incrementally adjust the operating point and measure the resulting power change. If power increases, the algorithm continues in the same direction; if power decreases, it reverses direction. P&O is simple to implement and works across source types, but suffers from oscillation around the maximum power point and may track incorrectly under rapidly changing conditions.

Incremental Conductance Methods

Incremental conductance methods compare the instantaneous conductance to the incremental conductance to determine the position relative to the maximum power point. This approach can theoretically find the exact MPP without oscillation, though practical implementations still exhibit some hunting behavior due to measurement noise and discrete adjustments.

Fractional Open-Circuit Voltage Tracking

For certain source types, the maximum power point voltage is approximately a fixed fraction of the open-circuit voltage. Periodically measuring open-circuit voltage and setting the operating point to the appropriate fraction provides simple, low-overhead MPPT. The required momentary disconnection from load and the approximation error are acceptable trade-offs for many applications.

Model-Based MPPT

If the source characteristics are well-known, model-based algorithms can calculate the optimal operating point from measured parameters such as temperature, illumination, or vibration amplitude. This approach avoids the oscillation and convergence time of iterative methods but requires accurate models and appropriate sensors. Hybrid approaches combine model-based initialization with iterative refinement.

Multi-Source MPPT Coordination

When sources share power conditioning resources, MPPT must be coordinated to avoid conflicts. Time-multiplexed converters may track each source during its connection interval, maintaining operating point estimates between intervals. Coordinated control can also exploit correlations between sources, using conditions measured at one source to improve tracking at another.

Resistive Source Matching

Sources with predominantly resistive internal impedance, such as thermoelectric generators, achieve maximum power transfer when the load impedance equals the source impedance. DC-DC converters present adjustable effective input resistance through duty cycle control, enabling continuous matching as source conditions change. The effective input resistance of a boost converter, for example, equals the duty cycle squared times the load resistance.

Capacitive Source Matching

Piezoelectric harvesters present capacitive sources that require different matching strategies. Optimal power extraction involves transferring charge from the piezoelectric capacitance at the optimal phase relative to mechanical excitation. Synchronized switch harvesting techniques and other specialized interfaces maximize power from capacitive sources.

Nonlinear Source Matching

Photovoltaic cells and RF rectifiers exhibit strongly nonlinear current-voltage characteristics. The optimal operating point varies with input conditions, requiring adaptive matching that tracks the changing maximum power point. The steep current roll-off beyond the maximum power point demands precise control to avoid operating in the current-limited region.

Reactive Power Considerations

Some harvesting sources exhibit significant reactive impedance components at their operating frequencies. Conjugate matching that cancels the reactive component maximizes real power transfer. Passive matching networks or active impedance synthesis can implement the required reactive compensation, with active approaches enabling adaptive matching as conditions change.

Voltage Bus Combining

Individual source converters can output to a common voltage bus, with currents from each source combining naturally. This approach requires each converter to regulate its output to the bus voltage while maximizing input power extraction. Output current limiting and reverse-current blocking prevent sources from loading each other.

Current Summing

Current-output converters can sum their outputs into a common node that feeds a final voltage regulation stage. This approach naturally accommodates sources with different power levels and avoids circulating currents between sources. The final regulator determines the system output voltage.

Direct Charge Combining

For battery or supercapacitor charging applications, sources can directly contribute charge to the storage element with minimal intermediate regulation. Each source converter implements current limiting and end-of-charge detection. This approach maximizes efficiency by avoiding unnecessary conversion stages but provides less output voltage regulation.

Priority-Based Power Routing

Intelligent power routing directs energy flow based on source availability, efficiency, and load requirements. Primary sources may feed loads directly, with secondary sources charging storage for later use. Dynamic adjustment of routing decisions optimizes overall system efficiency while ensuring load requirements are met.

Source Prioritization

Power routing algorithms assign priorities to available sources based on instantaneous power levels, efficiency, or other criteria. Higher-priority sources receive first allocation of conversion resources, with lower-priority sources engaged as capacity permits or primary sources become unavailable. Priorities may be fixed or dynamically adjusted based on operating conditions.

Load Matching

Intelligent routing can match source characteristics to load requirements. Steady loads may be served by stable sources such as thermoelectric generators, while intermittent high-power demands draw from storage that has been charged by variable sources. This approach optimizes both energy capture and delivery efficiency.

Storage Management Integration

Power routing decisions integrate with storage management to maintain appropriate state of charge while meeting load demands. Excess harvested energy charges storage, while storage supplements harvesting during high-demand periods. Coordinated control prevents storage overcharge or deep discharge while maximizing energy throughput.

Predictive Energy Management

Learning algorithms can predict future source availability based on time-of-day, historical patterns, or correlated environmental measurements. Predictive knowledge enables proactive routing decisions, such as charging storage before an anticipated low-energy period or deferring non-critical loads until energy availability improves.

Minimum Operating Voltage Challenges

Power management circuits require minimum supply voltage to begin operation, typically several hundred millivolts for CMOS control logic. Starting from a completely discharged state with only microwatts of available power presents a bootstrapping challenge. The system must accumulate sufficient energy to start the power management electronics before it can efficiently harvest additional energy.

Ultra-Low-Voltage Startup Circuits

Specialized startup circuits can operate from sub-100-millivolt sources using techniques such as mechanical switches, depletion-mode transistors, or transformer-coupled oscillators. Once started, these circuits boost the source voltage enough to power conventional CMOS circuitry. The startup circuit then hands off to the main power management system for efficient operation.

Multi-Source Cold Start Strategy

In hybrid systems, the source with the best cold-start capability may serve as the primary startup source. Once the power management system is running, it can engage additional sources with higher power but more demanding startup requirements. Sequencing logic ensures orderly initialization without overloading the nascent power supply.

Auxiliary Power Elements

Small primary cells or charged supercapacitors can provide guaranteed cold-start capability when harvested energy may be insufficient. These auxiliary elements provide the initial energy to start the power management system, which then maintains itself from harvested energy under normal conditions. The auxiliary element requires replacement or recharge only after extended periods without sufficient harvested energy.

Digital vs. Analog Control

Simple hybrid systems may use analog control for low overhead, while complex systems benefit from digital control's flexibility. Mixed-signal approaches use analog circuits for fast inner loops and digital processing for slower optimization functions. The control approach must balance performance requirements against the power budget allocated to control functions.

Control Processor Selection

Ultra-low-power microcontrollers designed for energy harvesting applications provide the processing capability for sophisticated power management algorithms while consuming only microwatts during active operation and nanowatts in sleep modes. Appropriate processor selection matches control requirements to available power budget.

Real-Time Constraints

Power management control loops must respond fast enough to track changing source conditions and maintain output regulation. Inner current and voltage loops typically require microsecond response, while MPPT and source selection can operate on millisecond timescales. Hierarchical control structures separate fast and slow functions appropriately.

Stability Analysis

Multi-input converters present complex stability challenges due to interactions between sources and control loops. Small-signal modeling and analysis techniques adapted for multi-source systems help ensure stable operation across the range of operating conditions. Worst-case analysis considers stability with various combinations of active and inactive sources.

Light-Load Efficiency

Energy harvesting systems often operate at light loads relative to converter ratings, where switching and quiescent losses dominate. Pulse-frequency modulation, burst-mode operation, and dynamic voltage scaling help maintain efficiency at light loads. Some systems include separate high-efficiency paths for light-load operation.

Conversion Loss Minimization

Each power conversion stage introduces losses that reduce net harvested energy. Minimizing the number of conversion stages, using high-efficiency topologies, and selecting appropriate components for the power levels involved all contribute to maximizing net output. Direct paths from sources to loads or storage, bypassing conversion stages when voltage matching permits, can significantly improve efficiency.

Quiescent Current Reduction

The power consumed by control circuits, reference generators, and bias currents must be minimized relative to harvested power. Specialized ultra-low-quiescent-current regulators and power management ICs designed for energy harvesting applications achieve sub-microampere quiescent consumption. Aggressive power gating of unused circuit blocks further reduces overhead.