Alternating current heating techniques for lithium-ion batteries in electric vehicles:Recent advances and perspectives

The significant decrease in battery performance at low temperatures is one of the critical challenges that electric vehicles(EVs)face,thereby affecting the penetration rate in cold regions.Alternating current(AC)heating has attracted widespread attention due to its low energy consumption and uniform heating advantages.This paper introduces the recent advances in AC heating from the perspective of practical EV applications.First,the performance degradation of EVs in low-temperature environments is introduced briefly.The concept of AC heating and its research methods are provided.Then,the effects of various AC heating methods on battery heating performance are reviewed.Based on existing studies,the main factors that affect AC heating performance are analyzed.Moreover,various heating circuits based on EVs are categorized,and their cost,size,complexity,efficiency,reliability,and heating rate are elaborated and compared.The evolution of AC heaters is presented,and the heaters used in brand vehicles are sorted out.Finally,the perspectives and challenges of AC heating are discussed.This paper can guide the selection of heater implementation methods and the optimization of heating effects for future EV applications.

高效率中点钳位型光伏逆变器拓扑比较

光伏逆变器是太阳能光伏发电系统的核心部件之一。为了提高太阳辐射能的利用效率,降低光伏发电的成本,高效率的光伏逆变器拓扑已成为近年来的研究热点。中点钳位型(NPC)三电平电路拓扑由于具有开关损耗小、电磁干扰(EMI)小、光伏阵列对地杂散电容上无共模漏电流、所需滤波电感小等优点,非常适用于单相光伏逆变器。本文对三种典型的三电平NPC拓扑(二极管NPC、有源NPC(ANPC)和Conergy NPC)进行了全面深入的比较分析,包括调制原理、功率损耗分布、器件成本和性能特点,并通过实验测试了三种拓扑的效率。研究结果表明:①Conergy NPC的效率在三种拓扑中最高,且所用器件数最少;②有源NPC的功率损耗分布可以灵活调节,有利于大功率应用场合的散热设计,但其控制比较复杂,且器件成本在三种拓扑中最高;③二极管NPC拓扑的功率损耗分布不易调节,不利于大功率应用场合的散热设计。

Towards Long Lifetime Battery:AI-Based Manufacturing and Management

Technologies that accelerate the delivery of reliable battery-based energy storage will not only contribute to decarbonization such as transportation electrification,smart grid,but also strengthen the battery supply chain.As battery inevitably ages with time,losing its capacity to store charge and deliver it efficiently.This directly affects battery safety and efficiency,making related health management necessary.Recent advancements in automation science and engineering raised interest in AI-based solutions to prolong battery lifetime from both manufacturing and management perspectives.This paper aims at presenting a critical review of the state-of-the-art AI-based manufacturing and management strategies towards long lifetime battery.First,AI-based battery manufacturing and smart battery to benefit battery health are showcased.Then the most adopted AI solutions for battery life diagnostic including state-of-health estimation and ageing prediction are reviewed with a discussion of their advantages and drawbacks.Efforts through designing suitable AI solutions to enhance battery longevity are also presented.Finally,the main challenges involved and potential strategies in this field are suggested.This work will inform insights into the feasible,advanced AI for the health-conscious manufacturing,control and optimization of battery on different technology readiness levels.

Boosting battery state of health estimation based on self-supervised learning

State of health(SoH) estimation plays a key role in smart battery health prognostic and management.However,poor generalization,lack of labeled data,and unused measurements during aging are still major challenges to accurate SoH estimation.Toward this end,this paper proposes a self-supervised learning framework to boost the performance of battery SoH estimation.Different from traditional data-driven methods which rely on a considerable training dataset obtained from numerous battery cells,the proposed method achieves accurate and robust estimations using limited labeled data.A filter-based data preprocessing technique,which enables the extraction of partial capacity-voltage curves under dynamic charging profiles,is applied at first.Unsupervised learning is then used to learn the aging characteristics from the unlabeled data through an auto-encoder-decoder.The learned network parameters are transferred to the downstream SoH estimation task and are fine-tuned with very few sparsely labeled data,which boosts the performance of the estimation framework.The proposed method has been validated under different battery chemistries,formats,operating conditions,and ambient.The estimation accuracy can be guaranteed by using only three labeled data from the initial 20% life cycles,with overall errors less than 1.14% and error distribution of all testing scenarios maintaining less than 4%,and robustness increases with aging.Comparisons with other pure supervised machine learning methods demonstrate the superiority of the proposed method.This simple and data-efficient estimation framework is promising in real-world applications under a variety of scenarios.

Knowledge-Guided Data-Driven Model With Transfer Concept for Battery Calendar Ageing Trajectory Prediction

Dear Editor, Lithium-ion(Li-ion) battery has become a promising source to supply and absorb energy/power for many energy-transportation applications. However, Li-ion battery capacity would inevitably degrade over time, making its related ageing prediction necessary.

An Enhanced Data-Driven Model for Lithium-Ion Battery State-of-Health Estimation with Optimized Features and Prior Knowledge

In the long-term prediction of battery degradation,the data-driven method has great potential with historical data recorded by the battery management system.This paper proposes an enhanced data-driven model for Lithium-ion(Li-ion)battery state of health(SOH)estimation with a superior modeling procedure and optimized features.The Gaussian process regression(GPR)method is adopted to establish the data-driven estimator,which enables Li-ion battery SOH estimation with the uncertainty level.A novel kernel function,with the prior knowledge of Li-ion battery degradation,is then introduced to improve the mod-eling capability of the GPR.As for the features,a two-stage processing structure is proposed to find a suitable partial charging voltage profile with high efficiency.In the first stage,an optimal partial charging voltage is selected by the grid search;while in the second stage,the principal component analysis is conducted to increase both estimation accuracy and computing efficiency.Advantages of the proposed method are validated on two datasets from different Li-ion batteries:Compared with other methods,the proposed method can achieve the same accuracy level in the Oxford dataset;while in Maryland dataset,the mean absolute error,the root-mean-squared error,and the maximum error are at least improved by 16.36%,32.43%,and 45.46%,respectively.