Recovery effect

Abstract: Accurate information on battery state-of-charge, expected battery lifetime, and expected battery cycle life is essential for many practical applications. In this paper, we develop a nonchemically based partially linearized (in battery power) input-output battery model, initially developed for lead-acid batteries in a hybrid electric vehicle. We show that with properly tuned parameter values, the model can be extended to different battery types, such as lithium-ion, nickel-metal hydride, and alkaline. The validation results of the model against measured data in terms of power and efficiency at different temperatures are then presented. The model is incorporated with the recovery effect for accurate lifetime estimation. The obtained lifetime estimation results using the proposed model are similar to the ones predicted by the Rakhmatov and Virudhula battery model on a given set of typical loads at room temperature. A possible incorporation of the cycling effect, which determines the battery cycle life, in terms of the maximum available energy approximated at charge/discharge nominal power level is also suggested. The usage of the proposed model is computationally inexpensive, hence implementable in many applications, such as low-power system design, real-time energy management in distributed sensor network, etc.

Abstract: This paper proposes a new distributed battery energy architecture based on the microbank module (MBM) for dc microgrids. The benefits of the proposed architecture include: 1) no voltage sharing problem and no overcharge/overdischarge problem; 2) high compatibility and reliability; 3) high energy utilization efficiency; 4) reduced volume and weight of the battery management system (BMS). The proposed MBM consists of a microbidirectional dc/dc converter, a micro-BMS and a cell bank. Moreover, taking advantage of the battery recovery effect, a self-reconfiguration discharge strategy is also proposed to further enhance the battery performance and discharge efficiency of the new battery energy storage system (BESS). To optimize the proposed control, an efficiency analytical model considering the battery recovery effect is proposed using the curve fitting method. Owing to the bidirectional capability, soft switching capability and high efficiency, the dual active bridge (DAB) converters are chosen as the microbidirectional dc/dc converters. A hybrid modulation strategy with variable switching frequency combining the conventional phase-shift modulation and triangular current modulation is proposed for the DAB converter to reduce the dominant loss and improve the efficiency in wide load range based on the minimum loss model. A 1.5-kW experimental testing platform consisting of four MBMs and four 12 V/100 Ah lithium battery modules was built to verify the proposed architecture with the control and the proposed model. The experimental results show that the discharge time of the proposed distributed BESS is increased significantly under wide operation condition with the self-reconfiguration control. The discharge efficiency of the BESS is improved by 7.1% with the idling time of 5 min under the power level of 1.5 kW.

Abstract: Battery performance prediction is crucial for battery-aware power management, battery maintenance, and multi-cell battery design. However, the existing battery models cannot capture the circuit characteristics and nonlinear battery effects, especially recovery effect. This paper aims to fill this gap by developing an enhanced circuit-based model for single-cell battery. The proposed model is validated by comparing simulation results with experimental data collected through battery testbed. The comparison shows that the proposed model can accurately characterize and predict the single-cell battery performance with considerations of various nonlinear battery effects under both constant and variable loads.

Abstract: -Many applications of wireless sensor networks rely on batteries. But most batteries are not simple energy reservoirs, and can exhibit battery recovery effect. That is, the deliverable energy in a battery can be self-replenished, if left idling for sufficient time. As a viable approach for energy optimisation, we made several contributions towards harnessing battery recovery effect in sensor networks. 1) We empirically examine the gain of battery runtime of sensor devices due to battery recovery effect, and affirm its significant benefit in sensor networks. \Ve also observe a saturation threshold, beyond which more idle time will contribute only little to battery recovery. 2) Based on our experiments, we propose a Markov chain model to capture battery recovery considering saturation threshold and random sensing activities, by which we can study the effectiveness of duty cycling and buffering, 3) We devise a simple distributed duty cycle scheme to take advantage of battery recovery using pseudo-random sequences, and analyse its trade-oil" between the induced latency of data delivery and duty cycle rates,