A pack-level thermal model can be built by assembling cells into modules with thermal effects and arranging modules inside a pack. You can perform thermal performance analysis on battery packs with different levels of aging to meet warranty criteria at end-of-life (EOL) time.

You can use Simscape and Simscape Fluids™ blocks in gas, liquid, and thermal domains to model active, passive, or hybrid cooling/heating solutions. You can also explore cooling/heating system architectures by drawing schematics to place pipes, valves, heat exchangers, and tanks.

In the case of a liquid loop system, you can model an expansion tank that keeps reserve fluid; cold plates that channel the working fluid near the battery cells; a motor-driven circulation system with the pump, flow path, and valves; and different types of heat exchangers such as wired heaters or radiators.

After you have created a model of the cooling/heating system, you can run simulations, refine the design by exploring component sizing and system parameters, and satisfy requirements such as heat dissipation and power consumption.

Simulink makes it easy to design closed-loop controls that combine feedforward and PID techniques for circulation system controls—such as feed stream (valve) control, mass flow (pump) control, and heat exchange path selection control. With Simscape Battery, you can use prebuilt blocks, such as Battery Coolant Control and Battery Heater Control, to build battery thermal management control algorithms. With Stateflow^®, you can also design supervisory control logic for switching between different operating modes—such as heating versus cooling—based on the environmental temperature and the battery temperature.

In contrast to a Kalman filter, using a neural network to develop an SOC estimator does not require extensive information about the battery or its nonlinear behavior. Instead, the network is trained with current, voltage, and temperature data and SOC as a response.

SOH estimation is more subjective than SOC estimation; there is no universal agreement on how SOH is to be defined. As a result, each organization may have its own specific method for quantifying an SOH estimate, making it impossible to use general-purpose, off-the-shelf solutions. Using Simulink and Simscape, you can develop and simulate custom SOH estimation algorithms that are in line with your organization’s specific interpretation of battery health.

Battery charging is controlled with a constant-current/constant-voltage (CC-CV) algorithm. This is a common approach that maintains battery life by preventing overcurrent and overvoltage when charging. Current is limited during charging until a preset voltage is reached.

Protecting the battery requires monitoring current, voltage, and temperature. Simscape Battery provides blocks that you can incorporate into your BMS algorithms.

The ability to perform the realistic simulations that are central to the development of BMS control software starts with an accurate model of the battery pack. Batteries are often designed using finite element analysis (FEA) models that account for the physical configuration of the batteries and capture their electrothermochemical properties. Although these models are excellent for designing and optimizing a battery pack’s chemistry and geometry, control engineers need models that are better suited for system-level design and software development. 

When developing BMS algorithms in Simulink, you can use equivalent circuits to simulate the thermoelectric behavior of the battery cell. The equivalent circuit typically comprises a voltage source, a series resistance, and one or more resistor-capacitor pairs in parallel. The voltage source provides the open circuit voltage while the other components model the internal resistance and time-dependent behavior of the cell. These equivalent circuit elements are, in general, temperature and state-of-charge (SOC) dependent. Because these dependencies are unique to each battery’s chemistry, they need to be determined using measurements performed on battery cells of the same type as those for which the controller is being designed. You can use optimizations in Simulink and MATLAB to parameterize an equivalent circuit via model correlation with experimental data.

In addition to the battery pack model, realistic battery system simulations require accurate models of the circuit components connecting the battery system to the power source and load. Simscape Electrical, an add-on product for Simulink, provides complete libraries of the active and passive electrical components needed to assemble a complete battery system circuit, such as the analog front end for cell balancing. The charging source can consist of a DC supply, such as a photovoltaic (PV) system, or an AC source, for which the current is rectified. 

System-level simulation with Simulink and Simscape lets you construct a sophisticated charging source around the battery and validate the battery system under various operating ranges and fault conditions. The battery pack load can be similarly modeled and simulated. For example, the battery pack may be connected through an inverter to a permanent magnet synchronous motor (PMSM) in an electric vehicle (EV). With simulation, you can vary the operation of the EV through drive cycles and evaluate the effectiveness of the BMS in changing operating conditions.

When you incorporate desktop simulation into your testing procedures, you can author and execute test cases to exercise the battery system along all possible branches of logic and closed-loop control—a level of coverage rarely available when testing with hardware. With this approach, the simulation model serves as an executable specification driving the design and testing of the battery system. Simulink supports testing via desktop simulation with a range of features that enable you to:

• Incorporate requirements, such as limits, tolerances, logical checks, and temporal conditions, into the model with traceability to the original specifications
• Construct complex sequences of simulation-based tests to perform functional, baseline, equivalence, and back-to-back testing
• Track industry-standard metrics such as decision, condition, and modified condition/decision coverage (MC/DC), as well as relational boundary coverage
• Generate test inputs to achieve complete model coverage and custom objectives
• Use formal methods to identify hidden design errors that result in integer overflow, dead logic, and division by zero

When the battery system under development must meet safety requirements, you can integrate tests based on formal methods into your software development process in accordance with standards such as IEC 61508, IEC 61851, and ISO^® 26262.

Real-time simulation—encompassing both rapid prototyping and HIL testing—provides power electronics control engineers with additional insights into how a BMS design will perform on hardware. In both RP and HIL, the objective is to emulate in hardware one aspect of the overall design: the BMS controller in RP and the balance of the battery system in HIL. Real-time simulation offers several significant advantages in BMS design, letting you:

• Conduct RP to start validating algorithms before the final controller hardware is selected.
• Exploit the flexibility of a real-time test system for rapid design iteration and testing.
• Conduct HIL testing before the battery system prototype hardware is available.
• Use a combination of RP and HIL testing to exercise BMS algorithms for test cases that may be difficult, expensive, or destructive if you were to use the actual hardware.

Because hardware prototypes for a battery system can take considerable effort to build and modify, and because they are often costly to repair, it is not always feasible to test such prototypes against the electrical system in which the battery pack will operate. Given these limitations, even small design changes can threaten development schedules, and BMS designs tend to evolve slowly because teams consider radical departures from the previous design as too risky.

With Simulink, you can generate C/C++ and HDL code from the model of the hardware in your battery system and the greater system of which it is part, including the supply and the load. Once you deploy this code to a real-time computer, you are able to run real-time simulations of the hardware against your controller code before testing the controller in a battery system prototype. As a result, you can find and correct control design errors before they potentially damage expensive and difficult-to-replace prototype hardware. You can also uncover hardware design errors, such as incorrect component sizing.

Many HIL real-time systems, including Speedgoat target hardware, incorporate battery emulators, letting you emulate portable battery power supplies, emulate battery stacks for electric vehicles, or sink current to simulate batteries under charge.

Desktop simulation, RP, HIL, and PIL simulations all enable you to verify and validate the control algorithms for the BMS. With Simulink, you can use those same algorithms as the basis for generating production-ready code—either optimized and stable C/C++ code for implementation on microcontrollers or synthesizable HDL code for FPGA programming or ASIC implementation. Automatic code generation eliminates manual algorithm translation errors and produces C/C++ and HDL code with numerical equivalence to the algorithms you validated in Simulink. By simulating your control algorithms over all possible operating and fault conditions, you increase confidence that the generated code will handle those same conditions in the real system, even if you are unable to test for all of them. If hardware tests later indicate that algorithm changes are needed, you can simply modify the algorithms in your model, rerun simulation test cases to verify the correctness of the changes, and generate new, updated code. All generated C/C++ and HDL code is fully portable, optimizable with a range of options, and bidirectionally traceable to the Simulink model.