Large battery arrays can be used as backup and continuous power supply energy storage systems. This usage is gaining more and more attention, as evidenced by the home and commercial Powerwall systems recently launched by Tesla Motors. The batteries in this type of system are continuously charged by the grid or other energy sources, and then provide alternating current (AC) power to users through a DC/AC inverter.

　　Using batteries as a backup power source is nothing new. There are already many types of battery backup power systems, such as basic 120/240VAC and hundreds of watt short-term backup power systems for desktop PCs, ships, hybrid vehicles or all-electric vehicles. Thousands of kilowatts of special vehicle and ship backup power supply systems used, grid-level hundreds of kilowatts of backup power supply systems used in telecommunications systems and data centers (see Figure 1)...and so on. While advances in battery chemistries and battery technology get a lot of attention, there is an equally critical component to a viable and battery-based backup system: the battery management system (BMS).

　　The battery-based backup power supply is ideal for fixed and mobile use with power ranging from several kilowatts to hundreds of kW, and can reliably and effectively power a variety of uses.

　　There are many challenges when implementing a battery management system for energy storage applications, and the solution is by no means a simple "scaler" from the management system of a small, lower-capacity battery pack. Instead, new, more complex strategies and critical supporting components are required.

　　The starting point of the challenge is the requirement for high accuracy and reliability in the measurement of many key battery parameters. Furthermore, the planning of the subsystems must be modular so that the configuration can be customized to the specific needs of use, taking into account possible expansion requirements, overall management issues, and necessary maintenance.

　　The working environment of larger storage arrays presents other significant challenges. The BMS must also provide accurate, consistent data in a noisy electrical environment and often a very hot environment where the inverter voltage is very high/current and therefore current spikes occur. In addition, the BMS must provide a wide range of "fine" data on internal module and system temperature measurements, rather than a limited number of rough aggregate data, because these data are critical for charging, monitoring and discharging.

　　Because of the important role of these power systems, their operational reliability is inherently critical. To make this easily stated goal a reality, the BMS must ensure data accuracy and completeness as well as continuous health assessment so that the BMS can continue to take the required actions. Achieving solid planning and reliable security is a multi-level process. The BMS must anticipate possible problems in all subsystems, perform self-tests and provide fault detection, and then take appropriate actions in standby and working modes. The last requirement is that because of high voltage, high current and high power, the BMS must meet many strict regulatory standards.

　　System planning turns concepts into real-world outcomes

　　While monitoring a rechargeable battery is conceptually simple, simply placing voltage and current measurement circuitry at the battery terminals, the reality of a BMS is very different and much more complex.

　　Robust planning begins with comprehensive supervision of each cell, which imposes some important requirements on analog circuit performance. Battery readings need to be accurate at millivolt and milliampere levels, and voltage and current measurements must be time-synchronized to calculate power. The BMS must evaluate the validity of each measurement because it needs to maximize data integrity, and the BMS must also identify erroneous or questionable readings. The BMS cannot ignore unusual readings that may indicate a potential problem, but at the same time, the BMS cannot take action based on erroneous data.

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