The development time of balancing control strategy technology for series energy storage power sources is relatively short, and there is relatively little research on this. However, the balancing control strategy for series energy storage power sources is a decisive factor in achieving successful balancing effects and is an indispensable link. The balancing control strategy of series energy storage power sources is formulated based on the balancing topology and the entire balancing system, and needs to be combined with balancing control variables to further find the optimal balancing path for imbalanced batteries. The premise for selecting balanced control variables is that they can reflect changes in battery capacity. At present, battery terminal voltage, battery SOC, battery remaining capacity, etc. are commonly used as balanced control variables. Based on the characteristics of these balanced control variables, a balanced objective function is constructed, and then the corresponding balancing action is driven by the balancing strategy of the series fed power supply to ultimately achieve the goal of battery balancing. The balancing control strategy of series energy storage power sources can be divided into four categories based on the different balancing objective functions: extreme value type, average type, monotonic approaching type, and objective type.
(1) Extreme type
The extreme value balancing strategy of series energy storage power supply is to select the maximum and minimum values of the balancing control variables for balancing action. It has the advantages of simple balancing program design, fewer parameters, and clear objectives, and is commonly used in practical engineering applications. However, this method has significant limitations. When the topology of the balancing circuit is not flexible enough, it may lead to poor balancing performance due to the long balancing path. At the same time, this method is single step execution, which means that only two balancing targets can be selected at a time for balancing action. The balancing speed is slow and not suitable for circuits with complex balancing paths.
(2) Average type
The average type balancing strategy for series energy storage power sources, as the name suggests, adds up each balancing control variable and takes its average value as the imbalance judgment condition, thereby dividing the batteries in the battery pack into three categories: rich energy batteries, normal batteries, and depleted batteries. Then, based on the specific balancing topology, the optimal balancing path is formulated for the subsequent process. The randomness of this strategy is too strong, and different imbalanced conditions require a lot of discussion on balancing action classification. As the number of series connected batteries increases, the complexity of the program sharply increases, which is not conducive to expansion. If the balancing paths of the balancing topology are not rich enough, there will also be balancing overlap, which will prolong the balancing time.
(3) Monotonic convergence type
Emphasis is placed on the planning of the equilibrium objective function and subsequent equilibrium actions, so that the process from the initial state of the battery pack to the equilibrium objective variable is monotonic, greatly reducing the phenomenon of equilibrium overlap and shortening the equilibrium time. Using it as a rule limitation for the selection of balancing strategies for series energy storage power sources is very convenient for establishing more direct balancing paths and regularizing balancing actions in more complex balancing systems. The strategy for series energy storage power sources is suitable for balancing between individual units and balancing speed in multi-layer balancing systems.
(4) Target type
In order to avoid the phenomenon of over balancing as much as possible, a series energy storage power supply balancing control strategy based on the objective type balancing objective function is proposed. This strategy calculates the balancing objective variable through balancing performance parameters, divides different imbalanced battery groups based on the relationship between the balancing control variable and the balancing objective, and then opens the corresponding balancing action. Whenever the difference between a battery cell and the balancing objective is less than a certain threshold, Then the individual cell deviates from the equilibrium action range, ultimately causing the equilibrium control variables of each battery cell to approach the equilibrium target. After classifying the imbalanced battery groups, the target type balancing strategy for series energy storage power sources also needs to be coordinated with corresponding balancing path planning to achieve battery balancing. Similarly, if the balancing path is not well formulated, there will be over balancing phenomenon.