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Battery Management - Hardware and Algorithms

Battery management includes the monitoring, control and protection of batteries, making the battery management an essential part of any battery system. Depending on the storage application and cell chemistry, the battery management has to meet different complex requirements. Thus, the number of tasks to be monitored and the number of regulated parameters for a traction battery in the electric vehicle is many times greater than for a mobile phone battery.



Management Scope

Exceeding the maximum permissible current or cell voltage limits can lead to cell damage or even failure. Therefore, the single cell monitoring of the electrical variables as well as the limitation of the battery current belong to the core tasks of the battery management.

Too high or too low temperatures have a negative impact on the life of the cell and can in extreme cases result in internal short circuits or a thermal runaway. The temperature monitoring of the battery system, the modules or even the individual cells by the battery management is therefore necessary. For passive systems, the battery current is more limited if necessary, and with active air conditioning, the temperature can be controlled directly.

In addition, the state determination of the battery system falls within the scope of the battery management. In particular, accurate state of charge (SOC) estimation, both for the individual cells of a pack and for the entire battery, is of great importance for the superordinate energy management and estimation of the remaining range or service life in mobile applications.

Important cell parameters, such as capacitance and internal resistance, change over the life of a cell. Therefore, the value or state of health (SOH) of the parameters is continuously estimated based on the electrical measurements and stored models.

In order to avoid a violation of the operating limits, the charge acceptance (CA, Charge Acceptance), the maximum possible performance (CC, Cold Cranking) as well as the energy reserves are calculated on the basis of the measurements and the estimated states and cell parameters. This information (SOF, State of Function) is send to the superordinate energy management.

The balancing of cells with different states of charge within a series connection falls within the scope of the battery management as well.

Battery Management Systems


To perform the tasks, the battery management system (BMS) must have circuitry for detecting cell voltages, battery current, and temperatures. Balancing circuits are used to balance the state of charge of cells within a row. There are various systems that are able to discharge individual cells (passive balancing systems), to transfer energy between the individual cells (active balancing systems) and systems that are able to provide the energy of single cells for the application or to charge cells individually (redistribution).

Because of the simpler structure, passive balancing systems are the most widely used, even though the capacity of a series connection is limited by the cell with the smallest capacity. Active systems must have inductors or capacities and a transformer for the energy transfer between single cells in addition to the pure resistors and switches. This increases the available capacity but also the price. Redistribution requires a DC-to-DC converter for each cell, so these systems are very costly, but the proportion of actual available energy of the series connected cells is greatest with this system.

Algorithms and Methods


Various methods and algorithms are used to determine the battery states and parameters. For the state of charge determination, the ampere-hour count with open circuit voltage correction or non-linear Kalman filters can be used. For the parameter tracking of the capacitance these methods are extended to e.g. Least-square fitting methods or the use of dual or joint Kalman filters. The cell impedance can be determined online during load jumps or via pulse measurements. Vehicle-suitable EIS measurements are also conceivable to detect the impedance over a wide frequency range. Moreover, self-learning algorithms and back-end based methods can be used to make a prognosis in addition to the parameter determination.

In addition to the methods for state and parameter determination, balancing algorithms have to be implemented. These algorithms divide into voltage and ampere hour based methods. There are also algorithms in development and research that allow for regenerative balancing and thus actively contribute to extending the life of the battery.

Hardware-in-the-Loop Simulation


Due to the high energy densities and potential dangers in some cell chemistries, the BMSs are subject to particularly stringent requirements and are safety-relevant systems. The functionality of newly developed BMSs must therefore always be tested according to established standards, which entails a long development and evaluation period for BMSs.

As an alternative to real cells, hardware platforms that emulate the behavior of the cells can be used in the test phase. With these battery simulators hardware-in-the-loop tests are performed to obtain reliable test results with minimal resources. The use of HiL simulators capable of delivering and receiving power allows validation of both, the circuits and the algorithms of BMSs. In addition, the behavior of BMSs outside the system boundaries can be checked without causing a fire or explosion risk by exceeding limit values ​​of real cells.

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Technische Universität Berlin
Electrical Energy Storage Technology
Institute of Energy and Automation Technology
Faculty IV
sec. EMH 2
Einsteinufer 11
D-10587 Berlin


Sec. EMH2
Room EMH 162
+49 (0)30 314-21633
+49 (0)30 314-21133