Performed 3Delectrochemical-thermal modeling of four battery cooling methods 2)Thermal performance of direct air cooling, direct liquid cooling, indirect (jacket) liquid and fin coolingare compared 3) Merits and limitations of each cooling method for occupying a fixed volume are summarized 4) Temperaturerise for a fixed load is lower with direct or indirect liquid cooling lower than air and fin cooling. Abstract Choosing a proper cooling method for a lithium-ion (Li-ion) battery pack for electric drive vehicles (EDVs) and making an optimal cooling control strategy to keep the temperature at a optimal range of 15°C to 35°C is essential to increasing safety, extending the pack service life, and reducing costs. When choosing a cooling method and developing strategies, trade-offs need to be made among many facets such as costs, complexity, weight, cooling effects, temperature uniformity, and parasitic power. This paper considers four cell-cooling methods: air cooling, direct liquid cooling, indirect liquid cooling, and fin cooling. To evaluate their effectiveness, these methods are assessed using a typical large capacity Li-ion pouch cell designed for EDVs from the perspective of coolant parasitic power consumption, maximum temperature rise, temperature difference in a cell, and additional weight used for the cooling system. We use a state-of-the-art Li-ion battery electro-chemical thermal model. The results show that under our assumption an air-cooling system needs 2 to 3 more energy than other methods to keep the same average temperature; an indirect liquid cooling system has the lowest maximum temperature rise; and a fin cooling system adds about 40% extra weight of cell, which weighs most, when the four kinds cooling methods have the same volume. Indirect liquid cooling is more practical form than direct liquid cooling though it has slightly lower cooling performance.

 Abstract-Battery health monitoring and management is of extreme importance for the performance and cost of electric vehicles. This paper is concerned with machine learning enabled battery State-of-Health (SOH) indication and prognosis. The sample entropy of short voltage sequence is used as an effective signature of capacity loss. Advanced sparse Bayesian predictive modeling (SBPM) methodology is employed to capture the underlying correspondence between the capacity loss and sample entropy. The SBPM-based SOH monitor is compared with a polynomial model developed in our prior work. The proposed approach allows for an analytical integration of temperature effects such that an explicitly temperature-perspective SOH estimator is established, whose performance and complexity is contrasted to the support vector machine (SVM) scheme. The forecast of remaining useful life (RUL) is also performed via a combination of SBPM and bootstrap sampling concepts. Large amounts of experimental data from multiple lithium-ion battery cells at three different temperatures are deployed for model construction, verification, and comparison. Such a multi-cell setting is more useful and valuable than only considering a single cell (a common scenario). This is the first known application of combined sample entropy and SBPM to battery health prognosis.

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Abstract:In the past five years, there have been numerous cases of Li-ion battery fires and explosions, resulting in property damage and bodily injuries. This paper discusses the thermal runaway mechanism and presents various thermal runaway mitigation approaches, including separators, flame retardants, and safety vents. The paper then overviews measures for extinguishing fires, and concludes with a set of recommendations for future research and development.

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Due to the widespread deployment of distributed energy resources (DERs) and the liberalization of electricity market, traditional distribution networks are undergoing a transition to active distribution systems (ADSs), and the traditional deterministic planning methods have become unsuitable under the high penetration of DERs. Aiming to develop appropriate models and methodologies for the planning of ADSs, the key features of ADS planning problem are analyzed from the different perspectives, such as the allocation of DGs and ESS, coupling of operation and planning, and high-level uncertainties. Based on these analyses, this comprehensive literature review summarizes the latest research and development associated with ADS planning. The planning models and methods proposed in these research works are analyzed and categorized from different perspectives including objectives, decision variables, constraint conditions, and solving algorithms. The key theoretical issues and challenges of ADS planning are extracted and discussed. Meanwhile, emphasis is also given to the suitable suggestions to deal with these abovementioned issues based on the available literature and comparisons between them. Finally, several important research prospects are recommended for further research in ADS planning field, such as planning with multiple micro-grids (MGs), collaborative planning between ADSs and information communication system (ICS), and planning from different perspectives of multi-stakeholders.