Three Core Dimensions Empowering Scientific Research & Innovation

Technical Verification Dimension：Supports multi-scenario coupling analysis including millisecond-level dynamic response testing (supercapacitors), cross-seasonal energy storage simulation (hydrogen storage) and long-cycle durability verification (flow batteries). It enables quantitative evaluation of comprehensive performance for different hybrid energy storage combinations.
System Optimization Dimension：Modular architecture and standardized interfaces are provided to rapidly build wind-PV-storage hybrid systems and flexibly verify customized control strategies.
Engineering Application Dimension：Compatible with Hardware-in-the-Loop (HIL) test environments. It can directly connect with physical controllers such as PCS, BMS and EMS, accelerating the transformation of laboratory research results into practical engineering applications.

Full-Scenario New Energy Storage Model Library

01 Lithium-ion Battery Model

As the core unit balancing energy and power characteristics, the lithium-ion battery model simulates medium-short term energy storage scenarios (minute to hour scale) and delivers fast charge-discharge performance with high energy density. Its core modeling logic precisely reproduces battery charge-discharge characteristics, thermal effects and aging laws, providing critical data support and simulation basis for energy management of hybrid energy storage systems.
Two modeling methods are available: behavioral modeling and look-up table interpolation, supporting 2nd to 5th-order RC networks. The equivalent circuit diagram is shown below.

The encapsulated battery model in Matlab/Simulink is illustrated below. Input terminals include equalization current and thermal management control; output terminals feed battery voltage and temperature. The parameter configuration panel allows adjustment of basic battery parameters, polarization parameters, aging parameters and thermal characteristic parameters.

The charge-discharge characteristic test curve of the battery model is shown below. Due to polarization effects, battery voltage changes gradually; battery temperature keeps rising, and thermal control activates once the temperature hits 40°C, gradually cooling the battery down.

02 All-Vanadium Flow Battery Model

The all-vanadium flow battery model accurately simulates long-duration charge-discharge cycles of flow battery energy storage. It authentically reproduces electrolyte flow characteristics and electrochemical reaction kinetics, enabling performance evaluation of 4–8 hour long-duration energy storage. The flow battery model adopts equivalent circuit modeling as shown below.
Ipump stands for pump loss current, generally treated as a controllable current source determined by stack current ; Rreaction and Rresistive represent electrochemical losses inside the battery stack.

The encapsulated all-vanadium flow battery model in Matlab/Simulink is shown below. Inputs include current and initial SOC value; outputs include terminal voltage and SOC. The parameter panel configures basic equivalent circuit parameters.

The charging characteristic test curve of the model is displayed below. Terminal voltage rises along with SOC, and surges sharply in the later stage due to intensified concentration polarization.

03 Supercapacitor Model

Supercapacitors feature millisecond-level response speed (<100 ms) and ultra-high power density (10–100 kW/kg), specifically designed to mitigate instantaneous power fluctuations from wind-PV generation and electrical loads, compensating for the slow response of lithium-ion batteries. The model precisely characterizes double-layer dynamic behaviors and equivalent internal resistance variations, laying a simulation foundation for high-frequency charge-discharge cycles.ModelingTech provides a supercapacitor array module built on a classic equivalent circuit model, as shown below.

The encapsulated supercapacitor model in Matlab/Simulink is presented below, with positive/negative terminals and an observation terminal m. The parameter panel configures core parameters including capacitance, self-discharge resistance, rated voltage, and series-parallel quantity.

The charge-discharge characteristic test curve of the supercapacitor model is shown below, which demonstrates that SOC varies proportionally with terminal voltage.

04 Flywheel Energy Storage Model

Relying on unique mechanical energy storage properties, flywheel energy storage delivers fast frequency regulation capability for systems on second-to-minute timescales. Its cycle life reaches hundreds of thousands of times, far exceeding electrochemical energy storage, making it ideal for mitigating frequent grid power fluctuations.Compared with battery storage, flywheels suffer nearly no performance degradation under deep charge-discharge cycles, offering reliable power-type support for hybrid energy storage systems. ModelingTech provides a flywheel state estimation module that accurately calculates kinetic-to-electric energy conversion and evaluates its critical role in system inertia support.
The encapsulated flywheel state estimation model in Matlab/Simulink is shown below. Multiple observable variables can be calculated based on flywheel rotational speed, and mechanical parameters of the flywheel are configurable in the parameter panel.

The test output curve of the flywheel model is displayed below. SOC is positively correlated with rotational angular velocity.

05 Compressed Air Energy Storage (CAES) Model

As an ideal solution for large-scale long-duration energy storage, CAES delivers hour-to-day scale energy storage capacity, effectively compensating the deficiency of electrochemical storage in long-timescale regulation. By coupling thermodynamic equations and power conversion efficiency analysis, the model accurately simulates energy conversion characteristics during compression and expansion processes. It integrates four sub-modules: piston compressor, heat exchanger, air storage tank and piston expander.

✦ Piston Compressor
The air compressor compresses air under normal temperature and pressure into high-pressure, high-temperature air.
The encapsulated piston compressor model in Matlab/Simulink is shown below. Inputs include inlet air pressure, temperature and mass flow rate; outputs include outlet air pressure, temperature and power consumption. Configurable parameters cover mechanical and gas properties of the compressor.

Model output curves are displayed below. Ambient air is converted into high-pressure high-temperature air after compression; negative power values indicate power consumption by the compressor.

✦ Heat Exchanger
Heat exchangers are core components for heat recovery and utilization in CAES systems. During compression, the exchanger absorbs compression heat to boost compression ratio and efficiency. During expansion, it heats compressed air to improve expansion efficiency inside expanders.
The encapsulated heat exchanger model in Matlab/Simulink is shown below. Inputs include inlet air pressure and temperature; outputs include outlet air pressure and temperature.

The model output curve is shown below. High-temperature air is significantly cooled after passing through the exchanger, accompanied by minor pressure loss.

✦ Air Storage Tank
Air storage tanks serve as high-pressure air storage vessels for CAES systems, acting as key buffers to realize energy time shifting. Their capacity and pressure characteristics directly determine system storage duration and energy conversion efficiency.
The encapsulated air storage tank model in Matlab/Simulink is shown below. Inputs include inlet air temperature, inlet flow rate, leakage (outlet) flow rate and initial tank pressure; outputs include internal tank air pressure and temperature.

Dynamic storage process curves of the tank model are shown below. During air charging, internal pressure rises gradually while temperature slowly drops to ambient temperature.

✦ Piston Expander
The operating principle of expanders is the reverse of compressors. Air expands inside the expander with decreased pressure and temperature, releasing electrical power. Its dynamic mathematical model represents the inverse process of a compressor.
The encapsulated expander model in Matlab/Simulink is shown below. Inputs include inlet air pressure, temperature and flow rate; outputs include outlet air pressure, temperature and generated electrical power.

Model output curves are displayed below. During gas expansion, outlet pressure and temperature drop, and electrical power is released.

06 PEMFC (Proton Exchange Membrane Fuel Cell) Model

The PEMFC model effectively simulates efficient energy conversion from hydrogen to electricity. Integrated with electrochemical reaction and gas transport sub-modules, it dynamically adjusts input parameters such as current, hydrogen flow rate and temperature, and monitors key performance indicators in real time including output voltage, power, gas pressure and membrane water content.
A parameterized configuration panel enables flexible setup of stack structure and operating conditions, accurately reproducing dynamic responses under variable load conditions. The PEMFC model consists of four parts: voltage model, cathode flow channel, anode flow channel and proton exchange membrane humidity model.
The encapsulated PEMFC model in Matlab/Simulink is shown below. Input ports: current, hydrogen supply rate, temperature, enable signal. Output ports: hydrogen consumption rate, output voltage, output power, anode/cathode gas pressure and membrane water transport volume. The parameter panel configures stack series-parallel quantity, inlet humidity of anode/cathode, and inlet/outlet pressure of electrodes.

Model test curves are shown below. Under a 20 kW output power condition, output voltage, power, and partial pressures of hydrogen, oxygen, nitrogen and water gradually stabilize as the fuel cell operates continuously.

07 PEM Electrolyzer Model

The PEM electrolyzer model accurately simulates efficient conversion from electricity to hydrogen. It reproduces electrochemical kinetics of water electrolysis, providing critical parameters for hydrogen production system optimization. Like the fuel cell model, it includes four sub-modules: voltage model, cathode flow channel, anode flow channel and proton exchange membrane humidity model.
The encapsulated PEM electrolyzer model in Matlab/Simulink is shown below. Input ports: current, membrane water transport volume, temperature, module enable signal. Output ports: terminal voltage, hydrogen production rate, anode/cathode hydrogen pressure, membrane water transport volume and power consumption. Configurable parameters include stack series-parallel quantity, anode/cathode outlet pressure, proton exchange membrane thickness, active area and simulation time step.

Under a 20 kW power consumption condition, the electrolyzer’s output voltage, power, hydrogen/oxygen/water partial pressures and membrane water transport volume gradually stabilize during continuous operation.