Battery State Information Research Articles

Considering solid phase diffusion penetration depth to improve profile approximations: Towards accurate State estimations in lithium-ion batteries at low characteristic diffusion lengths

Lithium-ion batteries are typically modelled using the pseudo-2-dimensional (P2D) model, where a set of partial differential equations describing transport and electrochemical reactions are solved numerically to obtain battery state information. To improve computational efficiency and enable on-board implementation for state-estimation, reduced order models are developed, where one such model reduction technique includes using a profile approximation for solving solid phase diffusion inside electrode particles. While greatly reducing model complexity, the profile approximation also leads to reduced accuracy, especially at low characteristic diffusion lengths. We address this issue by developing an improved model, which recognizes the existence of a penetration depth and the solution is obtained by dividing the particle into diffusion-free core and a shell corresponding to the penetration depth. The time-scale for the diffusion front to reach the particle centre depends on the diffusivity and particle radius. A similarity solution for this period, leads to an expression for the State of charge (SOC) in the spatio-temporal domain. The predictions are compared to that from the benchmark P2D and experimental data, revealing accuracies >99%, while adding no computational cost. This improved model is particularly useful under high C-rate or low temperature operation, where the characteristic diffusion lengths are low.

Electrochemical Model-Based Fast Charging: Physical Constraint-Triggered PI Control

This paper proposes a new fast chargingstrategy for lithium-ion (Li-ion) batteries. The approach relies on an experimentally validated high-fidelity model describing battery electrochemical and thermal dynamics that determine the fast charging capability. Such a high-dimensional nonlinear dynamic model can be intractable to compute in real-time if it is fused with the extended Kalman filter or the unscented Kalman filter that is commonly used in the community of battery management. To significantly save computational efforts and achieve rapid convergence, the ensemble transform Kalman filter (ETKF) is selected and tailored to estimate the nonuniform Li-ion battery states. Then, a health- and safety-aware charging protocol is proposed based on successively applied proportional-integral (PI) control actions. The controller regulates charging rates using online battery state information and the imposed constraints, in which each PI control action automatically comes into play when its corresponding constraint is triggered. The proposed physical constraint-triggered PI charging control strategy with the ETKF is evaluated and compared with several prevalent alternatives. It shows that the derived controller can achieve close to the optimal solution in terms of charging time and trajectory, as determined by a nonlinear model predictive controller, but at a drastically reduced computational cost.

Distributed Deep Reinforcement Learning for Functional Split Control in Energy Harvesting Virtualized Small Cells

To meet the growing quest for enhanced network capacity, mobile network operators (MNOs) are deploying dense infrastructures of small cells. This, in turn, increases the power consumption of mobile networks, thus impacting the environment. As a result, we have seen a recent trend of powering mobile networks with harvested ambient energy to achieve both environmental and cost benefits. In this paper, we consider a network of virtualized small cells (vSCs) powered by energy harvesters and equipped with rechargeable batteries, which can opportunistically offload baseband (BB) functions to a grid-connected edge server depending on their energy availability. We formulate the corresponding grid energy and traffic drop rate minimization problem, and propose a distributed deep reinforcement learning (DDRL) solution. Coordination among vSCs is enabled via the exchange of battery state information. The evaluation of the network performance in terms of grid energy consumption and traffic drop rate confirms that enabling coordination among the vSCs via knowledge exchange achieves a performance close to the optimal. Numerical results also confirm that the proposed DDRL solution provides higher network performance, better adaptation to the changing environment, and higher cost savings with respect to a tabular multi-agent reinforcement learning (MRL) solution used as a benchmark.

A robust approach to state of charge assessment based on moving horizon optimal estimation considering battery system uncertainty and aging condition

Accurate battery state information is essential for battery management system application and safety monitoring. However, it is a challenge task to obtain satisfied estimation results due to the uncertainties and inconsistencies of battery packs caused by aging. To solve this challenge, an optimization-based moving horizon estimation approach is presented in this paper for battery state and parameter online estimation. The dynamic battery parameters including open circuit voltage and internal resistance in equivalent circuit model are described by polynomial function of state of charge and input current for estimation algorithm design. And the intrinsic connection and difference between extended Kalman filter and moving horizon estimation algorithm are explicitly explained. Both of them are least square based estimation approach, and Kalman filter is a special form of moving horizon estimation, while moving horizon estimation relax Markov assumption compared with extended Kalman method. And then the optimization-based moving horizon estimation is designed for parameters and state of charge online assessment for battery dynamic system. To reduce computing time, the software framework CasADi is used for differential-algebraic calculation and nonlinear optimization. Three mismatch working conditions are studied for estimation performance validation, including mismatched initial guess values and battery dynamic characteristics difference caused by aging condition. The experimental results demonstrate that optimization-based moving horizon estimation performs better than Kalman filter-based approaches in terms of estimation precision, convergence time and robust. The proposed optimization-based moving horizon estimation is a promising approach for state of charge estimation in commercial battery management system applications.

Distributed Charging Control in Broadband Wireless Power Transfer Networks

Wireless power transfer (WPT) technology provides a cost-effective solution to achieve sustainable energy supply in wireless networks, where WPT-enabled energy nodes (ENs) can charge wireless devices (WDs) remotely without interruption to the use. However, in a heterogeneous WPT network with distributed ENs and WDs, some WDs may quickly deplete their batteries due to the lack of timely wireless power supply by the ENs, thus resulting in short network operating lifetime. In this paper, we exploit frequency diversity in a broadband WPT network and study the distributed charging control by ENs to maximize network lifetime. In particular, we propose a practical voting-based distributed charging control framework where each WD simply estimates the broadband channel, casts its vote(s) for some strong sub-channel(s) and sends to the ENs along with its battery state information, based on which the ENs independently allocate their transmit power over the sub-channels without the need of centralized control. Under this framework, we aim to design lifetime-maximizing power allocation and efficient voting-based feedback methods. Towards this end, we first derive the general expression of the expected lifetime of a WPT network and draw the general design principles for lifetime-maximizing charging control. Based on the analysis, we then propose a distributed charging control protocol with voting-based feedback, where the power allocated to sub-channels at each EN is a function of the weighted sum vote received from all WDs. Besides, the number of votes cast by a WD and the weight of each vote are related to its current battery state. Simulation results show that the proposed distributed charging control protocol could significantly increase the network lifetime under stringent transmit power constraint in a broadband WPT network.

Battery-aware adaptive modulation with QoS constraints

We consider the problem of joint battery management and modulation optimization for maximizing the lifetime of a battery-powered autonomic wireless system while satisfying certain quality-of-service constraints. Our objective is to design the optimal transmission schemes adapting to the battery state, the delay constraints, and channel conditions. A delay-constrained data-transmission scenario is considered, where the battery state information is only available at the beginning of the entire transmission duration. The corresponding problem is formulated as a multiobjective stochastic optimization problem, which is hard to solve. We approximately transform the original problem to a multiobjective deterministic optimization problem by using a lower bound to replace the objective function of the original problem. The optimal, as well as efficient suboptimal solutions are developed. Simulation results are provided to demonstrate that the proposed algorithms can significantly increase the lifetime of the battery-powered autonomic wireless systems