Intelligent Energy Storage for India's Evolving Hybrid Inverter Ecosystem
Ganesh Moorti is a seasoned technologist and engineering leader in clean energy and advanced power solutions, with expertise in solar systems, energy storage, power electronics, and IoT platforms. He has led large R&D teams, driven innovation across PV modules and inverters, and focuses on accelerating India’s renewable energy transition through purpose-driven, data-led execution.
In a recent interview with Priyanka R, Copywriter at siliconindia, Ganesh Moorthi, Chief Technology Officer, Luminous Power Technologies, shared his insights on Intelligent Energy Storage for India’s Evolving Hybrid Inverter Ecosystem.
How are adaptive i-BMS architectures enabling real-time optimization of power flow, battery health, and grid interaction in hybrid energy storage systems?
The solar acceleration led to growth of On-Grid inverters and as there is higher rate of adoption, there is a need for storage in order to store the solar power in batteries during non-export hours as well as for backup requirements. This is enabled by hybrid inverters, which also exports the excess solar to the grid. Presently, the chemistry that is most suitable for solar storage for residential, C&I and utility applications is lithium-ion batteries. These batteries needs to be managed smartly for safety, efficiency and performance which is done by the Battery Management System.
The BMS evolved from a standalone hardware BMS to much sophisticated architectures, one such being integrated BMS. i-BMS is designed with all the hardware and firmware features including FET based PDU/Contactor drivers, gate drivers, pre-charge circuitry, current sensing, auxiliary power supplies, CAN and Modbus. There is a need for faster decision making in hybrid inverters as the switching time between battery and solar, battery and grid is less than 10ms. This can be done with only HF switching power devices and hence the i-BMS. LF hybrid inverters create ripple in charging current that requires filtering, making i-BMS essential. These factors are critical to the health and performance of hybrid inverters.
What co-optimization frameworks can improve intelligence sharing between inverter DSPs and i-BMS controllers to enhance charging accuracy, switching efficiency, and safety?
In India, we don’t see indigenized and standardized communication protocol that is needed for information/data exchange between an Inverter and Battery. We still rely on Chinese protocols, the popular one being pylontech protocol. This is not bad, however it is not taking into account of many requirements that is so specific for India market such as configuration management of the batteries from the hybrid Inverter.
Ganesh Moorti says, “The need for replacement of the BMS in the field and custom configuration through Inverter is critical for serviceability and reduces the dependency of interaction with LiB”. This is the reason, some companies like Luminous had built our own communication protocol that works between our Inverters and lithium-ion batteries.
Protocols enable data exchange between BMS and inverter controllers. Optimizing their logic can boost performance by 10 percent, double safety, and increase efficiency by 10 percent. For example, the Inverter as the master in such systems can cut-off the battery packs much before the BMS reaches its over/under voltage limits. The inverter can limit partial charge and discharge cycles to extend Li-ion battery life, while the BMS can reduce battery power output when solar is available and use the time for cell balancing.
How can system-aware i-BMS designs evolve to read inverter switching, solar variability, and dynamic loads within a unified decision-making algorithm?
The key here is to understand that the current is not smooth during charging and discharging of the batteries. The degree of smoothness is dependent on the Inverter switching topology, current sensing, component selection and filters. A 100A discharge can peak at 120A briefly, requiring impact analysis. In hybrid solar inverters, variable solar availability from shading, soiling, or weather can cause partial/deep discharges, high charging currents, and prolonged CV/float battery states. Hence, the i-BMS must be able to dynamically configure itself for SOC limits based on the mode in which inverter is operating to avoid any of the stated conditions that impacts the health of the battery.
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In similar aspect, the load on the hybrid batteries are varied such as resistive, capacitive and inductive loads and combinations of them. There is lot of reactive power component as well which could to losses and poor round trip efficiency. Therefore, an i-BMS must possess a unified algorithm that can dynamically adjust itself to varied inverter functions, solar availability and load requirements.
What advances in thermal intelligence can help i-BMS platforms dynamically adjust current limits using environmental data and inverter heat patterns to extend battery life?
i-BMS must ideally possess one NTC (Temperature Sensor) per cell. If this is not feasible, it must have an algorithm to be able to triangulate and understand the different temperature zones within the battery pack. As the LiB are sensitive to temperature variations, the difference in the DeltaT from one corner of the pack to the other could lead to imbalances. Hence, a thorough understanding of thermal management inside the battery pack is critical and this does not stop here.
If the Inverter is not reading the thermal decisions made by the i-BMS, it could continue to demand for higher power delivery while the BMS is trying to balance the cells and normalize the cell temperature. The trade-off between solar and battery by the hybrid inverters in such cases is critical and this can be further improved if the Inverter thermal data of the switching MOSFET, diodes, transformers and inductors are also considered to take the decision on the current limits.
As new chemistries such as LFP, NMC, and sodium systems emerge, what opportunities exist to build chemistry-flexible i-BMS algorithms that learn and adapt from real-time field behaviour?
The advancement in chemistries really brought in the difference in the nominal voltage in which they operate and the voltage ramp-up or ramp-down while charging and discharging. NMC batteries at 3.6V have a linear SOC relationship, making charge/discharge management easy, but they are highly sensitive to temperature, requiring SOC and SOH algorithms with extended Kalman filter compensations. LFP batteries at 3.2V have a plateaued voltage profile, making SOC estimation under varying conditions more challenging. LFP batteries at 3.2V have a plateaued voltage, making SOC estimation challenging, but they are stable and widely used.
Na-ion batteries, with a wide 1.5-5V range, require careful voltage management, balancing, and compensation, and cannot be directly swapped with LFP BMS. These are some of the challenges but the good point is that the AFE’s can already handle this wide range and they can support higher balancing and advanced AFE’s have different integrated Impedance measurements to predict and monitor SOX more accurately, but the secret lies in unified flexible logics built in the BMS.
