In a battery management system (BMS), battery equalizer is used to achieve voltage consistency between series connected battery cells. Recently, serious inconsistency has been founded to exist in retired batteries, and traditional equalizers are slow or inefficient to handle the situation. The multicell-to-multicell (MC2MC) topology, which can directly transfer energy from consecutive strong cells to consecutive weak cells, is promising to solve the problem, but its performance is limited by the existing converter. Therefore, this paper proposes an enhanced MC2MC equalizer based on a novel bipolar-resonant LC converter (BRLCC), which supports flexible and efficient operation modes with stable balancing power, can greatly improve the balancing speed without much sacrificing the efficiency. Mathematical analysis and comparison with typical equalizers are provided to illustrate its high balancing speed and good efficiency. An experimental prototype for 8 cells is built, and the balancing powers under different operation modes are from 1.426 W to 12.559 W with balancing efficiencies from 84.84% to 91.68%.

Lithium-ion batteries (LIBs) have been widely used in various fields. In order to ensure the safety of LIBs, it is necessary to accurately estimate of the state of health (SOH) of the LIBs. This paper proposes a SOH hybrid estimation method based on incremental capacity (IC) curve and back-propagation neural network (BPNN). The voltage and current data of the LIB during the constant current (CC) charging process are used to convert into IC curves. Taking into account the incompleteness of the actual charging process, this paper divides the IC curve into multiple voltage segments for SOH prediction. Corresponding BP neural network is established in multiple voltage segments. The experiment divides the LIBs into five groups to carry out the aging experiment under different discharge conditions. Aging experiment data are used to establish the non-linear relationship between the decline of SOH and the change of IC curve by BP neural network. Experimental results show that in all voltage segments, the maximum mean absolute error does not exceed 2%. The SOH estimation method proposed in this research makes it possible to embed the SOH estimation function in battery management system (BMS), and can realize high-precision SOH online estimation.

Even with the same voltage level, different types of battery packs have different requirements for the volume of the battery equalization circuit. However, most equalization circuits have the same problem: the volume of the equalization circuit is fixed once the voltage level of the battery pack is determined. In order to solve this problem, this paper proposes a novel lithium battery equalization circuit with any number of inductors (ECANI). It can select any number of inductors less than half the number of batteries, even when the voltage level of the battery pack is determined. Simulation and experiments are used to verify the performance of the equalization circuit. The current error and the average final voltage error in the experiment are 1.69% and 0.33% lower than those in the simulation, respectively. So the circuit can achieve equalization with good accuracy.

Many battery equalizers have been proposed to achieve voltage consistency between series connected battery cells. Among them, the multicell-to-multicell (MC2MC) equalizers, which can directly transfer energy from consecutive more-charged cells to less-charged cells, can enable fast balancing and a high efficiency. However, due to the limitations of the equalizers, it is not possible to achieve fast equalization and reduce the size of the circuit at the same time. Therefore, a MC2MC equalizer based on a full-bridge bipolar-resonant LC Converter (FBBRLCC) is proposed in this paper, which not only implements MC2MC equalization, but also greatly reduces the circuit size by reducing the number of switches by nearly half. A mathematical model and simulation comparison with conventional equalizers are used to illustrate the high-speed equalization performance of the proposed equalizer and excellent balancing efficiency. An experimental prototype for eight cells is built to verify the performance of the proposed FBBRLCC equalizer and the balancing efficiencies in different operating modes are from 85.19% to 88.77% with the average power from 1.888 W to 14.227 W.

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