Battery Health Degradation Research Articles

Real-Time Implementable Integrated Energy and Cabin Temperature Management for Battery Life Extension in Electric Vehicles

Among many emerging technologies, battery electric vehicles (BEVs) have emerged as a prominent and highly supported solution to stringent emissions regulations. However, despite their increasing popularity, key challenges that might jeopardize their further spread are the lack of charging infrastructure, battery life degradation, and the discrepancy between the actual and promised all-electric driving range. The primary focus of this paper is to formulate an integrated energy and thermal comfort management (IETM) strategy. This strategy optimally manages the electrical energy required by the heating, ventilation, and air conditioning (HVAC) unit, the most impacting auxiliary in terms of battery load, to minimize battery life degradation over any specific drive cycle while ensuring the actual cabin temperature hovers within the permissible tolerance limit from the reference cabin temperature and the driver-requested traction power is always satisfied. This work incorporates a state-of-health (SOH) estimation model, a high-fidelity cabin thermodynamics model, and an HVAC model into the forward-approach simulation model of a commercially available BEV to showcase the impact and efficacy of the proposed IETM strategy for enhancing battery longevity. The instantaneous optimization problem of IETM is solved by the golden-section search method leveraging the convexity of the objective function. Simulated results under different driving scenarios show that the improvement brought by the proposed ITEM controller can minimize battery health degradation by up to 4.5% and energy consumption by up to 2.8% while maintaining the cabin temperature deviation within permissible limits from the reference temperature.

Designing a Real-Time Implementable Optimal Adaptive Cruise Control for Improving Battery Health and Energy Consumption in EVs through V2V Communication

Battery electric vehicles (BEVs) face challenges like their limited all-electric range, the discrepancy between promised and actual energy efficiency, and battery health degradation, despite their environmental benefits. This article proposes an optimal adaptive cruise control (OACC) framework by leveraging ideal vehicle-to-vehicle communication to address these challenges. In a connected vehicle environment, where it is assumed that the Ego vehicle’s vehicle control unit (VCU) accurately knows the speed and position of the Leading vehicle, the VCU can optimally plan the acceleration trajectory for a short-term future time window through a model predictive control (MPC) framework tailored to BEVs. The primary objective of the OACC is to reduce the energy consumption and battery state-of-health degradation of a BEV. The Chevrolet Spark 2015 is chosen as the BEV platform used to validate the effectiveness of the proposed OACC. Simulations conducted under urban and highway driving conditions, as well as under communication delay and infused noise, resulted in up to a 3.7% reduction in energy consumption and a 9.7% reduction in battery state-of-health (SOH) degradation, demonstrating the effectiveness and robustness of the proposed OACC.

Ecological Driving Framework of Hybrid Electric Vehicle Based on Heterogeneous Multi-Agent Deep Reinforcement Learning

Hybrid Electric Vehicles have great potential to be discovered in terms of energy saving and emission reduction, and ecological driving provides theoretical guidance for giving full play to their advantages in real traffic scenarios. In order to implement ecological driving strategy with the lowest cost throughout life cycle in car-following scenario, the safety and comfort, fuel economy and battery health need to be considered, which is a complex nonlinear and multi-objective coupled optimization task. Therefore, a novel multi-agent deep deterministic policy gradient (MADDPG) based framework with two heterogeneous agents to optimize adaptive cruise control and energy management strategy respectively is proposed, thereby decoupling optimization objectives of different domains. Due to the asynchronous of multi agents, different learning rate schedules are analyzed to coordinate learning process to optimize training results. And an improvement on Prioritized Experience Replay technique is proposed, which improves optimization performance of original MADDPG method by more than 10%. Simulations under mixed driving cycles show that, on the premise of ensuring car-following performance, the overall driving cost including fuel consumption and battery health degradation of MADDPG-based method can reach 93.88% of that of DP. And the proposed algorithm has good adaptability to different driving conditions.

Conditional Wasserstein barycenters to predict battery health degradation at unobserved experimental conditions

Modeling the health degradation process of batteries is a fundamental need for manufacturers. It allows one to anticipate how long the battery will be useful in application and to what performance level. Since degradation strongly depends on the operating conditions (ambient temperature, charging and discharging policy...), degradation should be modeled for a full range of conditions. However, a serious obstacle in this task is the small number of tested experimental conditions, potentially separated by strong performance differences. To handle this issue, while keeping the interesting results previously obtained for a fixed experimental condition, we propose a two-step methodology, separating the time degradation from the effect of experimental factors. Time modeling is handled by previously developed methods, and the effect of experimental factors is handled by a model-based approach, allowing the inclusion of physical prior knowledge with more robust predictions. This article focuses on this second step which requires particular developments, with a regression method over complex objects. This is handled using optimal transport theory thanks to dedicated Conditional Wasserstein barycenters models.

Capacity degradation prediction of lithium-ion battery based on artificial bee colony and multi-kernel support vector regression

Lithium-ion battery (LIB) capacity degradation prediction plays an important role in the prediction of battery health degradation. Accurate prediction of its capacity can guide battery replacement and maintenance, and ensure the safety and stability of the energy storage system. In this paper, a hybrid method based on artificial bee colony (ABC) algorithm and multi-kernel support vector regression (MK-SVR) is proposed to predict the capacity degradation of LIB. Firstly, the capacity degradation prediction model of LIB is established by MK-SVR with few hyperparameters. Then, the hyperparameters of degradation prediction model are optimized by the ABC to improve the accuracy of prediction. Finally, the proposed method is verified by NASA's LIB degradation experiment. Compared with different optimization algorithms, ABC greatly improves the accuracy of capacity degradation prediction. Compared with traditional forecasting methods, the proposed method can predict the capacity degradation of LIB more accurately.

Chained Gaussian processes to estimate battery health degradation with uncertainties

Estimating the average health degradation of a new battery design is a crucial objective for manufacturers to estimate its value. Furthermore, to quantify financial risks, associated uncertainties should be modeled precisely. From a data-driven perspective, Gaussian process regression (GPR) is often a method of choice since it simultaneously learns complex models and naturally includes uncertainties. However, GPR methods generally rely on a stationarity assumption which imposes severe constraints on uncertainties. In this paper, we illustrate the limits of standard GPR and show that the Chained Gaussian processes, a more general framework introduced in the Machine learning community, is a useful alternative, allowing more accurate quantification of uncertainties.

Chained Gaussian processes with derivative information to forecast battery health degradation

In order to estimate the value of a battery design, a key objective of manufacturers is to model battery health degradation. This estimation should be paired with a precise uncertainty modeling to quantify associated financial risks. However, experimental data are often limited in practice by their cost and the necessary time to perform a complete test. Prolonging previous work on uncertainty quantification, we tackle the issue of reducing testing time. To do so we propose to improve the forecasting ability of our model by integrating the available prior knowledge on derivatives. This is done through an extension of the chained Gaussian process framework is which is introduced in this paper.

Context-aware recommendations for extended electric vehicle battery lifetime

Electric vehicles are a means of reducing CO2 emissions in transportation. However, the sustainability of electric vehicle batteries is affected by battery health degradation, which decreases their overall lifetime. This results in a substantial amount of depleted batteries due to replacements. Users have a major impact on battery health degradation through their actions while handling electric vehicles, such as the use of fast charging. To mitigate this problem, this article presents a methodology to generate user guidance for battery-friendly actions in the upcoming use. Therefore, we first identify general recommendations from related work and combine them with the vehicle context in order to define context-aware recommendations in the form of if–then rules. These context-aware recommendations are then used to generate user advice. Second, the article covers how to predict the vehicle context in order to determine necessary recommendations. Third, a prescriptive recommendation system architecture is proposed, which takes vehicle context information, and produces user guidance. Finally, we test the proposed architecture using fuzzy logic as decision system. Overall, the architecture provides satisfactory user advice.

Smart Battery Management Technology in Electric Vehicle Applications: Analytical and Technical Assessment toward Emerging Future Directions

Electric vehicles (EVs) have received widespread attention in the automotive industry as the most promising solution for lowering CO2 emissions and mitigating worldwide environmental concerns. However, the effectiveness of EVs can be affected due to battery health degradation and performance deterioration with lifespan. Therefore, an advanced and smart battery management technology is essential for accurate state estimation, charge balancing, thermal management, and fault diagnosis in enhancing safety and reliability as well as optimizing an EV’s performance effectively. This paper presents an analytical and technical evaluation of the smart battery management system (BMS) in EVs. The analytical study is based on 110 highly influential articles using the Scopus database from the year 2010 to 2020. The analytical analysis evaluates vital indicators, including current research trends, keyword assessment, publishers, research categorization, country analysis, authorship, and collaboration. The technical assessment examines the key components and functions of BMS technology as well as state-of-the-art methods, algorithms, optimization, and control surgeries used in EVs. Furthermore, various key issues and challenges along with several essential guidelines and suggestions are delivered for future improvement. The analytical analysis can guide future researchers in enhancing the technologies of battery energy storage and management for EV applications toward achieving sustainable development goals.

Battery Management, Key Technologies, Methods, Issues, and Future Trends of Electric Vehicles: A Pathway toward Achieving Sustainable Development Goals

Recently, electric vehicle (EV) technology has received massive attention worldwide due to its improved performance efficiency and significant contributions to addressing carbon emission problems. In line with that, EVs could play a vital role in achieving sustainable development goals (SDGs). However, EVs face some challenges such as battery health degradation, battery management complexities, power electronics integration, and appropriate charging strategies. Therefore, further investigation is essential to select appropriate battery storage and management system, technologies, algorithms, controllers, and optimization schemes. Although numerous studies have been carried out on EV technology, the state-of-the-art technology, progress, limitations, and their impacts on achieving SDGs have not yet been examined. Hence, this review paper comprehensively and critically describes the various technological advancements of EVs, focusing on key aspects such as storage technology, battery management system, power electronics technology, charging strategies, methods, algorithms, and optimizations. Moreover, numerous open issues, challenges, and concerns are discussed to identify the existing research gaps. Furthermore, this paper develops the relationship between EVs benefits and SDGs concerning social, economic, and environmental impacts. The analysis reveals that EVs have a substantial influence on various goals of sustainable development, such as affordable and clean energy, sustainable cities and communities, industry, economic growth, and climate actions. Lastly, this review delivers fruitful and effective suggestions for future enhancement of EV technology that would be beneficial to the EV engineers and industrialists to develop efficient battery storage, charging approaches, converters, controllers, and optimizations toward targeting SDGs.

Multisource Domain Adaption for Health Degradation Monitoring of Lithium-Ion Batteries

Battery health monitoring is crucial to reduce unexpected maintenance and realize maximum productivity. However, batteries generally work under dynamic and complex working conditions (i.e., different domains) in industrial applications. The distribution difference exists not only between source and target domain but also between multisource domains, it is a challenging problem to monitor battery health deterioration under multiple working conditions. This study proposes a novel transfer learning-based method, multisource domain adaption network (MSDAN), to address the problem of battery health degradation monitoring under different working conditions. First, a battery health degradation monitoring method is proposed based on multisource transfer learning. The knowledge learned from multiple operational conditions is transferred to the target condition. Second, multisource domain adaption is proposed to address the problem of distribution discrepancy of different domains, where unsupervised feature alignment metric and maximum mean discrepancy (MMD) are utilized to evaluate the difference between target domain and each source domain. Multisource adversarial learning is adopted to guide feature generator to provide domain-invariant features of different domains. Third, a feature generator is proposed to extract common and domain-specific features of different domains. The experimental results illustrate that MSDAN has a better performance of battery health prognostics than other single-source domain adaption methods.

Developing a real-time data-driven battery health diagnosis method, using time and frequency domain condition indicators

Lithium-ion batteries are considered as promising electric energy storage systems. However, identification of battery health is a critical issue. Furthermore, battery aging extremely depends on operating conditions. Therefore, monitoring and analysis of battery health degradation in real-time systems such as electric vehicles, in which a variety of stress factors may come into play, are demanded. This paper proposes a data-driven algorithm based on multiple condition indicator to estimate battery health using application-based load profiles. In this regard, battery cells have been cycled under a worldwide light duty driving test cycle (WLTC) load profile in laboratory to acquire real-world driving data. Time-domain and frequency-domain condition indicators are extracted from measured on-board data like voltage and current within certain time intervals, enabling real-time investigation of battery health degradation. The condition indicators have been fed into a Gaussian process estimator to track the real-time state of health (SoH). As degradation strongly depends on magnitude of input current, it is important that the proposed method can predict health of the cell regardless of current amplitude and aging pattern. Therefore, to assess accuracy and robustness of the proposed method, it is validated using a different load profile with distinct depth of discharge, current amplitude, and distinctive aging pattern. Results reveal the proposed approach is highly precise and is capable of estimating battery SoH with low computational costs and a relative error of less than 1%. The proposed technique is promising for online diagnostics of battery health thanks to its high accuracy and robustness.

Remaining useful life prediction for lithium-ion batteries based on an integrated health indicator

State of health estimation and remaining useful life prediction of lithium-ion batteries is challenging due to various health indicators characterizing battery degradation. This paper develops an integrated health indicator to predict remaining useful life by incorporating capacitance, resistance, and constant current charge time with the help of a beta distribution function, based on the correlation analysis between parameter variations and aging mechanisms. A three-order polynomial model is employed to fit the battery health degradation process, remaining useful life is predicted using a particle filter algorithm, and the probability density function for the battery remaining useful life is then provided. A case study is conducted to validate the health degradation model and battery remaining useful life prediction. The results show that the constant voltage charge time is not a good health indicator, and a threshold of 0.85 is recommended as the end-of-life criterion based on the integrated health indicator. The developed method provides a reference for battery remaining useful life prediction when sufficient energy and power are required.

A Stochastic Optimal Control Approach for Exploring Tradeoffs between Cost Savings and Battery Aging in Datacenter Demand Response

This brief paper optimizes power management for datacenters employing lithium-ion battery storage, with the specific goal of addressing the tradeoff between: 1) the cost saving achievable through the peak demand shaving and 2) the corresponding battery aging. To the best of the authors’ knowledge, this tradeoff has never been addressed using physics-based models of battery performance and degradation combined with stochastic models of datacenter demand. We build: 1) a Markov chain model of datacenter power demand; 2) a second-order model of battery diffusion/reaction dynamics; and 3) a physics-based model of battery aging via solid electrolyte interphase growth. Together, these models enable the solution of the battery health-conscious demand response problem via stochastic dynamic programming (SDP). A penalty function is used for enforcing a datacenter “power cap” within this SDP problem. By varying this power cap, we traverse the Pareto tradeoff between the cost savings due to demand response and battery health degradation.

Remaining Useful Life Prediction for Lithium-Ion Battery: A Deep Learning Approach

Accurate prediction of remaining useful life (RUL) of lithium-ion battery plays an increasingly crucial role in the intelligent battery health management systems. The advances in deep learning introduce new data-driven approaches to this problem. This paper proposes an integrated deep learning approach for RUL prediction of lithium-ion battery by integrating autoencoder with deep neural network (DNN). First, we present a multi-dimensional feature extraction method with autoencoder model to represent battery health degradation. Then, the RUL prediction model-based DNN is trained for multi-battery remaining cycle life estimation. The proposed approach is applied to the real data set of lithium-ion battery cycle life from NASA, and the experiment results show that the proposed approach can improve the accuracy of RUL prediction.

Energy Management Strategy in Consideration of Battery Health for PHEV via Stochastic Control and Particle Swarm Optimization Algorithm

This paper presents an energy management strategy for plug-in hybrid electric vehicles (PHEVs) that not only tries to minimize the energy consumption, but also considers the battery health. First, a battery model that can be applied to energy management optimization is given. In this model, battery health damage can be estimated in the different states of charge (SOC) and temperature of the battery pack. Then, because of the inevitability that limiting the battery health degradation will increase energy consumption, a Pareto energy management optimization problem is formed. This multi-objective optimal control problem is solved numerically by using stochastic dynamic programming (SDP) and particle swarm optimization (PSO) for satisfying the vehicle power demand and considering the tradeoff between energy consumption and battery health at the same time. The optimization solution is obtained offline by utilizing real historical traffic data and formed as mappings on the system operating states so as to implement online in the actual driving conditions. Finally, the simulation results carried out on the GT-SUITE-based PHEV test platform are illustrated to demonstrate that the proposed multi-objective optimal control strategy would effectively yield benefits.

Health Degradation Detection and Monitoring of Lithium-Ion Battery Based on Adaptive Learning Method

Battery health degradation detection and monitoring are crucial to realize equipment's near-zero downtime and maximum productivity. A big challenge is how to construct an effective monitoring system that can consistently exemplify the degradation propagation of battery health over its whole life. This paper proposes an on-line method based on adaptive learning scheme for monitoring of state-of-health (SOH) of battery. An adaptive Gaussian mixture model (AGMM) is developed for online learning dynamic changes of battery health in its full life. A Bayesian-inference-based probability indication is developed for detection of new health states that are online modeled by deleting and adding of components in AGMM. Furthermore, health changes are quantified by an AGMM-based health index that measures the overlap between two density distributions approximated by the adapted GMM and the historic GMM, respectively. Research results of its application in a lithium-ion battery life test illustrate that AGMM-based SOH monitoring system is effective for detection and adaptive assessment of battery health degradation. This paper provides a guidance for the development of battery SOH monitoring model based on adaptive learning scheme without too much human intervention.

Plug-in hybrid electric vehicle charge pattern optimization for energy cost and battery longevity

This paper examines the problem of optimizing the charge pattern of a plug-in hybrid electric vehicle (PHEV), defined as the timing and rate with which the PHEV obtains electricity from the power grid. The optimization goal is to simultaneously minimize (i) the total cost of fuel and electricity and (ii) the total battery health degradation over a 24-h naturalistic drive cycle. The first objective is calculated for a previously-developed stochastic optimal PHEV power management strategy, whereas the second objective is evaluated through an electrochemistry-based model of anode-side resistive film formation in lithium-ion batteries. The paper shows that these two objectives are conflicting, and trades them off using a non-dominated sorting genetic algorithm. As a result, a Pareto front of optimal charge patterns is obtained. The effects of electricity price and trip schedule on the optimal Pareto points and the PHEV charge patterns are analyzed and discussed.