Electric vehicles (EVs) have many benefits over internal combustion engine vehicles, including superior performance, high energy density, less pollution, excellent acceleration, and more. But EVs aren't perfect. A major disadvantage is the need for an expensive battery system with specific maintenance requirements, including a long charging time.
One of the main components of EVs is the battery management system (BMS). To meet higher power and voltage requirements, EVs use battery packs with hundreds of battery cells connected in series or parallel – this forms a complex battery system.
Any less than ideal battery condition – such as overcurrent, overvoltage, overcharging, or overdischarging – leads to damage and aging. In the worst case scenarios, there is a risk of fire and explosion. For these reasons, a BMS is needed to provide a “safety lock” to ensure proper battery performance.
However, BMS features (such as current and voltage protection during charging and discharging processes) depend on battery operating conditions (charge, lifespan, temperature, etc.). This is partially done through battery modeling, which provides a mathematical model of a virtual cell that verifies that the BMS will function properly for the corresponding battery.
Battery modeling includes the battery:
- State monitoring
- Real-time controller design
- Failure analysis
- Thermal management
- General interpretation of behavior
Monitoring
Battery health monitoring is necessary to optimize the safety and performance of a battery, as well as its lifespan predictions and aging diagnostics. Faded batteries accumulate a robust electrolyte interface at the negative electrode. Cell design, battery performance, and environmental circumstances are among the many factors that affect a battery's lifespan.
Battery State of Charge (SoC) assessment provides information about the battery's remaining capacity as a percentage of its total capacity. SoC estimation has two commonly used approaches: direct estimation and model-based assessment.
Direct estimation is based on the primary measurement of electric battery parameters (voltage and current). The two calculation methods used are systems based on Ampere-hours (Ah) and open circuit voltage (OCV). However, planning the initial SoC and measurement accuracy can be a challenging process when tuning the Ah method for the SOC estimation algorithm.
This approach is highly dependent on the measured current, where errors accumulated over time significantly influence the accuracy of SoC estimation. It is also challenging to determine the precise starting SoC in real-world situations (for example, in the case where a battery is only charged within an insufficient range, say, 10 to 90 percent).
On the other hand, the OCV-based method produces high estimation accuracy and has been accepted as an efficient and popular method for SoC calculation. There is a non-linear relationship between the SoC and OCV of a battery. The procedure requires sufficient battery rest (battery needs to be disconnected from chargers and loads). The main weakness of this method is the quiet hours. It generally takes a long time to reach stability after disconnecting the battery from the load (it may take more than two hours under low temperature circumstances).
The OCV-SoC relationship also depends on battery life and temperature.
Temperature
Battery temperature is a fundamental factor that affects battery performance, lifespan, performance and safety. Thermal sensors are suitable for measuring the external temperature of a battery.
However, this information alone is not adequate because the battery's internal temperature is a critical parameter for proper battery management. High internal temperature encourages battery aging and causes safety issues (e.g. fire). The internal temperature of the battery is often significantly changed relative to the surface temperature (up to 12°C in high power applications).
Producing a suitable approach for battery internal temperature assessment prevents accelerated aging of batteries and supports the BMS algorithm in optimizing battery energy discharge.
Ratings
In general, battery models can be classified into three main types:
1. Electric
2. Thermal
3. Coupled models (other models such as kinetic models are rarely used in BMS design).
The battery electrical model involves the electrochemical model, the reduced order model, the proportional circuit model and the data-driven model. The electrochemical model presents information about the electrochemical behavior of the battery. This model can be extremely accurate, but requires advanced simulation and computational effort. As a result, it is challenging to fully employ this model in a real-time application.
Consequently, the reduced order electrical model is produced as a simplified physics-based electrochemical model to determine the state of charge (SoC) of the lithium-ion battery. Uncomplicated, low-order electrical models provide less information but are convenient for real-time battery applications.
The key is to monitor battery temperature as part of a successful BMS. A battery's performance may deteriorate if it is operated at higher or lower temperatures. Separate cooling systems are typically used to maintain the proper battery temperature. For example, Tesla uses a patented battery configuration with a plate-based cooling system to dissipate heat and monitor battery temperature.
The coupled battery The electrothermal model captures the electrical (current, voltage, SoC) and thermal (surface and internal temperature) operations of the battery – simultaneously. Several coupled electrothermal models have already been developed.
For example, a 3D electrothermal model measures the battery's SoC and calculates heat generation and distribution under direct and dynamic currents. This model contains a 2D potential delivery model and a 3D temperature distribution model. The batteries validated a reduced low-temperature electrothermal model with three cathode materials. This model is ideal for developing rapid heating and optimal loading requests in low temperature conditions.
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