Batteries are widely applied to the energy storage and power supply in portable electronics, transportation, power systems, communication networks, and so forth. They are particularly demanded in the emerging technologies of vehicle electrification and renewable energy integration for a green and sustainable society. To meet various voltage, power, and energy requirements in large-scale applications, multiple battery cells have to be connected in series and/or parallel. While battery technology has advanced significantly during the past decade, existing battery management systems (BMSs) mainly focus on the state monitoring and control of battery systems packed in fixed configurations. In fixed configurations, though, battery system performance is, in principle, limited by the weakest cells, which can leave large parts severely underutilized.

Battery prognostics and health management (PHM) are essential for lithium-ion batteries in electric vehicles. In the battery PHM, accurate estimation of the battery state of health (SOH) and prediction of the remaining useful life (RUL) are crucial to ensure safe and efficient battery operation. This paper presents a probabilistic method for the battery degradation modeling and health prognosis based on the features extracted from the charging process using the dynamic Bayesian network (DBN). First, an aggregated feature, combining the incremental capacity analysis (ICA) of constant-current (CC) charging and the time constant of constant-voltage (CV) charging, is developed to characterize the battery degradation dynamics in case some CC or CV charging information is absent. The DBN is then employed to explore the underlying correlation between the battery aging and the extracted features. The proposed model treats the degradation dynamics as a rich family of probability distributions to model real-world battery operation more accurately. Moreover, the battery SOH estimation and RUL prediction are carried out using the particle filtering (PF) inference algorithm. Experimental tests are conducted on two different battery cells and the results show that the proposed methods can provide accurate and robust battery SOH estimation and reliable RUL prediction.

Large-scale battery systems have been applied to a number of grid-level energy storage services such as microgrid capability and distribution upgrade due to the penetration of solar/wind energy. In these battery applications, charge imbalance among battery cells/modules/packs becomes a common issue, which can reduce available battery capacity, accelerate battery degradation, and even cause some safety hazards. To tackle this issue, various battery charge equalization (BCE) systems have been proposed in recent decades, among which the module-based BCE system is widely viewed as a promising solution and has drawn increasing attention. In this paper, we study the module-based BCE systems by presenting a mathematical model that can characterize the charge transfer behavior in such systems, and then proposes computationally efficient algorithms to estimate the instantaneous battery cell state of charge (SOC), charge equalization time, and charging/discharging time, based on given system parameters and various initial battery cell SOCs. In addition, the conditions are derived to ensure that all battery cells can reach charge equalization and then get fully charged/discharged together without overcharging/overdischarging. All theoretical results are illustrated and justified through extensive numerical experiments.

Effective cell equalization is of extreme importance to extract the maximum capacity of a battery pack. In this paper, two cell balancing objectives, including balancing time reduction and cells' temperature rise suppression, are taken into consideration simultaneously. Furthermore, hard constraints are imposed on the cells' state-of-charge levels, currents, and equalizing currents. Based on a developed module-based cell-to-cell balancing system model, a multi-objective constrained optimization problem is formulated, which aims at the coordinated control of all equalizers rather than individually controlling the equalizer for its two adjacent cells' equalization. Next, a hierarchical cell equalizing control approach is proposed, where the module-level controlled equalizing currents are first designed at the top layer and then the cell-level equalizers are controlled for each battery module in parallel at the bottom layer. The designed hierarchical structure significantly reduces the computational burden, making the cell equalizing algorithm more implementable in real-time. Following the Lyapunov stability analysis, the convergence of the designed cell equalizing control algorithm is proved. Illustrative results demonstrate that the balancing time can be reduced by up to 29.8% compared with the decentralized equalizing control.

Charge imbalance is a very common issue in multi-cell/module/pack battery systems due to manufacturing variations, inconsistent charging/discharging, and uneven thermal distribution. As a consequence, the deliverable charge capacity, battery lifespan, and system reliability may all decrease over time. To tackle this issue, various external circuit designs can be attached for charge balance, and the internal battery cell/module/pack connection can also significantly affect the charge balance performance. This paper focuses on minimizing the battery charge equalization (BCE) time by battery cell/module reconfiguration. Specifically, for the reconfigurable module-based battery charge equalization system, we propose reconfiguration algorithms for fast charge equalization under different levels of system reconfigurability. For battery systems allowing module reconfiguration and intra-module cell reconfiguration, the proposed module-based bounded reconfiguration algorithm can reach or get very close to the minimum BCE times obtained by exhaustive search. When the reconfigurability level is extended by allowing inter-module cell reconfiguration, the proposed module-based complete reconfiguration algorithm can achieve similar optimality to that of the genetic algorithm (GA). Moreover, as compared to the circuit experiments, exhaustive search, and GA, the proposed algorithms take much less computation time. The optimality and computational efficiency of the proposed algorithms are demonstrated by both circuit and numerical experiments.

To meet the ever-increasing demand for energy storage and power supply, battery systems are being vastly applied to, e.g., grid-level energy storage and automotive traction electrification. In pursuit of safe, efficient, and cost-effective operation, it is critical to predict the maximum acceptable battery power on the fly, commonly referred to as the battery system’s state of power (SoP). Compared to the SoP prediction at the battery cell level, predicting the SoP of a multi-battery system, especially including parallel-connected cells/modules/packs, is much more complicated and far less investigated. To solve this problem, a system-model-based SoP prediction method is first proposed in this work. Specifically, based on the formulated system model and generic state-space representation, the challenge of nonmonotonic system state evolution, arising from the dynamic parallel current distribution, is identified and systematically addressed by the proposed method. As demonstrated by tests on a battery system set up with experimentally verified parameter values, the proposed method outperforms the commonly applied cell-SoP-based methods for providing a more accurate and reliable prediction of the battery system SoP. Moreover, the proposed prediction framework presented in generic forms can be readily applied to other system structures.