Energy management is critical to reducing the size and operating cost of hybrid energy systems, so as to expedite on-the-move electric energy technologies. This article proposes a novel ...knowledge-based, multiphysics-constrained energy management strategy for hybrid electric buses, with an emphasized consciousness of both thermal safety and degradation of onboard lithium-ion battery (LIB) system. Particularly, a multiconstrained least costly formulation is proposed by augmenting the overtemperature penalty and multistress-driven degradation cost of LIB into the existing indicators. Further, a soft actor-critic deep reinforcement learning strategy is innovatively exploited to make an intelligent balance over conflicting objectives and virtually optimize the power allocation with accelerated iterative convergence. The proposed strategy is tested under different road missions to validate its superiority over existing methods in terms of the converging effort, as well as the enforcement of LIB thermal safety and the reduction of overall driving cost.
Energy management is an enabling technique to guarantee the reliability and economy of hybrid electric systems. This paper proposes a novel machine learning-based energy management strategy for a ...hybrid electric bus (HEB), with an emphasized consciousness of both thermal safety and degradation of the onboard lithium-ion battery (LIB) system. Firstly, the deep deterministic policy gradient (DDPG) algorithm is combined with an expert-assistance system, for the first time, to enhance the "cold start" performance and optimize the power allocation of HEB. Secondly, in the framework of the proposed algorithm, the penalties to over-temperature and LIB degradation are embedded to improve the management quality in terms of the thermal safety enforcement and overall driving cost reduction. The proposed strategy is tested under different road missions to validate its superiority over state-of-the-art techniques in terms of training efficiency and optimization performance.
The accurate diagnostic of internal short circuit (ISC) is critical to the safety of lithium-ion battery (LIB), considering its consequence to disastrous thermal runaway. Motivated by this, this ...article proposes a novel ISC diagnostic method with a high robustness to measurement disturbances and the capacity fading. Particularly, a multistate-fusion ISC diagnostic method leveraging polarization dynamics instead of the conventional charge depletion is proposed within a model-switching framework. This is well-proven to eliminate the vulnerability of diagnostic to battery aging. Within this framework, the recursive total least squares method with variant forgetting is exploited, for the first time, to mitigate the adverse effect of measurement disturbances, which contributes to an unbiased estimation of the ISC resistance. The proposed method is validated both theoretically and experimentally for high diagnostic accuracy as well as the strong robustness to battery degradation and disturbance.
Fast charging strategies have gained an increasing interest toward the convenience of battery applications but may unduly degrade or damage the batteries. To harness these competing objectives, ...including safety, lifetime, and charging time, this paper proposes a health-aware fast charging strategy synthesized from electrochemical system modeling and advanced control theory. The battery charging problem is formulated in a linear time-varying model predictive control algorithm. In this algorithm, a control-oriented electrochemical-thermal model is developed to predict the system dynamics. Constraints are explicitly imposed on physically meaningful state variables to protect the battery from hazardous operations. A moving horizon estimation algorithm is employed to monitor battery internal state information. Illustrative results demonstrate that the proposed charging strategy is able to largely reduce the charging time from its benchmarks while ensuring the satisfaction of health-related constraints.
Accurate monitoring of the internal statuses is highly valuable for the management of the lithium-ion battery (LIB). This article proposes a thermal-model-based method for multistate joint ...observation, enabled by a novel smart battery design with an embedded and distributed temperature sensor. In particular, a novel smart battery is designed by implanting the distributed fiber optical sensor internally and externally. This promises a real-time distributed measurement of LIB internal and surface temperature with a high space resolution. Following this endeavor, a low-order joint observer is proposed to coestimate the thermal parameters, heat generation rate, state of charge, and maximum capacity. Experimental results disclose that the smart battery has space-resolved self-monitoring capability with high reproducibility. With the new sensing data, the heat generation rate, state of charge, and maximum capacity of LIB can be observed precisely in real time. The proposed method validates to outperform the commonly-used electrical-model-based method regarding the accuracy and the robustness to battery aging.
The accurate estimation of the state-of-health (SOH) is vital to the life management of lithium-ion batteries (LIBs). In this article, we propose a fusion-type SOH estimation method by combining the ...model-based feature extraction and data-based state estimate. Particularly, a novel model-based voltage construction method is proposed to eliminate the unfavorable numerical condition and reshape the disturbance-free incremental capacity (IC) curves. Leveraging the modified IC curves, a set of informative features-of-interest is extracted and evaluated, while eventually several cautiously selected ones are used to estimate the SOH of LIBs accurately. Furthermore, the impact of model order on the estimation performance is scrutinized to give insights into the parameterization in practical applications. Long-term cycling tests on different types of LIB cells are used for evaluation. The proposed method is validated with a good robustness to the cell inconsistency, temperature uncertainty, noise corruption, and a satisfied generality to different battery chemistries.
The state of health (SOH) is a vital parameter enabling the reliability and life diagnostic of lithium-ion batteries. A novel fusion-based SOH estimator is proposed in this study, which combines an ...open circuit voltage (OCV) model and the incremental capacity analysis. Specifically, a novel OCV model is developed to extract the OCV curve and the associated features-of-interest (FOIs) from the measured terminal voltage during constant-current charge. With the determined OCV model, the disturbance-free incremental capacity (IC) curves can be derived, which enables the extraction of a set of IC morphological FOIs. The extracted model FOI and IC morphological FOIs are further fused for SOH estimation through an artificial neural network. Long-term degradation data obtained from different battery chemistries are used for validation. Results suggest that the proposed fusion-based method manifests itself with high estimation accuracy and high robustness.
Available capacity of lithium-ion batteries is directly linked to the mileage of the electric vehicle. The cell imbalance is recognized as a significant concern hindering the full utilization of pack ...capacity. Following the emerging concept of battery reconfiguration, this article proposes a dual-scale hierarchical equalization scheme enabled by a novel four-switch reconfigurable topology. In particular, a four-switch reconfigurable topology is proposed, for the first time, which enjoys the benefits of flexible reconfigurability, moderate complexity, and high fault tolerance. Relying on the new topology, a hierarchical equalization strategy is proposed incorporating the intramodule time-sharing intervention and inter-module splitting recombination. This endeavor contributes to achieving all-cell flexibility, which further promises the all-cell equalization and maximum capacity utilization. Hardware-in-the-loop results validate that the proposed reconfigurable topology-enabled hierarchical equalization strategy can improve the pack capacity utilizing rate by 11.3%.
•Three-dimensional CFD model with forced air cooling are developed for battery modules.•Impact of different air cooling strategies on module thermal characteristics are investigated.•Impact of ...different model structures on module thermal responses are investigated.•Effect of inter-cell spacing on cell thermal characteristics are also studied.•The optimal battery module structure and air cooling strategy is recommended.
Thermal management needs to be carefully considered in the lithium-ion battery module design to guarantee the temperature of batteries in operation within a narrow optimal range. This article firstly explores the thermal performance of battery module under different cell arrangement structures, which includes: 1×24, 3×8 and 5×5 arrays rectangular arrangement, 19 cells hexagonal arrangement and 28 cells circular arrangement. In addition, air-cooling strategies are also investigated by installing the fans in the different locations of the battery module to improve the temperature uniformity. Factors that influence the cooling capability of forced air cooling are discussed based on the simulations. The three-dimensional computational fluid dynamics (CFD) method and lumped model of single cell have been applied in the simulation. The temperature distributions of batteries are quantitatively described based on different module patterns, fan locations as well as inter-cell distance, and the conclusions are arrived as follows: when the fan locates on top of the module, the best cooling performance is achieved; the most desired structure with forced air cooling is cubic arrangement concerning the cooling effect and cost, while hexagonal structure is optimal when focus on the space utilization of battery module. Besides, the optimized inter-cell distance in battery module structure has been recommended.
Fast charging is an enabling technique for the large-scale penetration of electric vehicles. This article proposes a knowledge-based, multiphysics-constrained fast charging strategy for lithium-ion ...battery (LIB), with a consciousness of the thermal safety and degradation. A universal algorithmic framework combining model-based state observer and a deep reinforcement learning (DRL)-based optimizer is proposed, for the first time, to provide a LIB fast charging solution. Within the DRL framework, a multiobjective optimization problem is formulated by penalizing the over-temperature and degradation. An improved environmental perceptive deep deterministic policy gradient (DDPG) algorithm with priority experience replay is exploited to tradeoff smartly the charging rapidity and the compliance of physical constraints. The proposed DDPG-DRL strategy is compared experimentally with the rule-based strategies and the state-of-the-art model predictive controller to validate its superiority in terms of charging rapidity, enforcement of LIB thermal safety and life extension, as well as the computational tractability.
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