Series battery equalisation can improve battery charge and discharge reliability and extend battery life. Inductor-based battery equalisation schemes have the advantages of simple topologies and control strategies. According to the energy transfer pathway, inductor-based battery equalisation schemes can be divided into cell-to-cell and cell-to-pack equalisation schemes. The control strategies of the cell-to-cell schemes are simple; the inductor can only transfer energy between the neighbouring cells, so the equalisation speed is low. The cell-to-pack schemes are able to accomplish energy transfer between the cells and pack by charging and discharging the inductors. The equalisation speed is high, but the control strategies may be complex. In this paper, different equalisation topologies are reviewed, then a unified control strategy which is applicable to all of the inductor-based equalisation topologies is proposed. The equalisation speeds and efficiencies of these different schemes, including the newly-proposed unified control strategy, are analysed and compared. Based on the theoretical analysis, simulations, and experimental verifications, it is concluded that this unified control strategy can perform the battery equalisation process quickly and efficiently.

With the increasing environmental concerns, plug-in electric vehicles will eventually become the main transportation tools in future smart cities. As a key component and the main power source, lithium-ion batteries have been an important object of research studies. In order to efficiently control electric vehicle powertrains, the state of charge (SOC) of lithium-ion batteries must be accurately estimated by the battery management system. This paper aims to provide a more accurate dynamic SOC estimation method for lithium-ion batteries. A dynamic Thevenin model with variable parameters affected by the temperature and SOC is established to model the battery. An unscented Kalman particle filter (UPF) algorithm is proposed based on the unscented Kalman filter (UKF) algorithm and the particle filter (PF) algorithm to generate nonlinear particle filter according to the advantages and disadvantages of various commonly used filtering algorithms. The simulation results show that the unscented Kalman particle filter algorithm based on the dynamic Thevenin model can predict the SOC in real time and it also has strong robustness against noises.

Battery equalization can increase batteries’ life cycle, utilization, and reliability. Compared with battery equalization topologies based on resistance or energy storage components, the topologies based on transformers have the advantages of high balancing current and efficiency. However, the existence of switching losses will reduce the reliability and service life span of the equalization circuit. Aiming at resolving this problem, a new battery equalization topology by fly-back transformer with an absorbing circuit is proposed in this paper. Compared with other transformer-based topologies, it can decrease switching losses because the voltage/current spike is solved by the absorbing circuit which is composed of inductance, capacitance and diode (LCD), and it can also maintain a high balancing current of about 1.8 A and high efficiency of about 89%, while the balancing current and efficiency of other topologies were usually 1.725 A/1.5 A and 80%/80.4%. The working principle of the balancing topology and the process of soft switching are analyzed and calculated in the frequency domain. Due to the addition of the LCD absorbing circuit, soft switching can be realized to reduce the switching losses while the high equalization speed and efficiency are still maintained. The corresponding control strategy of the balancing topology is also proposed and the timely balancing is achieved. The theoretical analysis is verified by simulation and experimental results.

The power distribution strategy of hybrid power sources is an important issue for fuel cell vehicles. A good power distribution control strategy can realize the optimal control of the vehicle energy, which can save energy and improve the operating conditions of the power source. Therefore, this article proposes a power distribution strategy. First, in order to solve the problems existing in the existing fuel cell model and the lithium-ion battery model, an improved hybrid power system model with better dynamic performance was established in the Simulink. Second, in order to ensure the durability of the power system, operation constraints are added to the distribution strategy. Finally, the power allocation is regarded as a nonlinear programming optimization problem and solved by a nonlinear programming algorithm. The nonlinear programming algorithm selects the BFGS algorithm. The simulation results of other control strategies in MATLAB show that the proposed power distribution strategy greatly improves the durability of the vehicle and has good adaptability under urban conditions. This distribution method can provide support for the actual application of offline control strategies.