Battery aging-minimal speed control of autonomous heavy-duty electric trucks in adaptation to highway topography and traffic

The development of battery electric(BE)heavy-duty trucks(HDTs)is highly limited to the short cycling life of batteries.In this paper,we propose a battery aging-conscious control strategy for extended battery life by optimizing the speed trajectory of BE HDT.A state-space model is constructed by connecting the vehicle dynamics and battery state of charge,and a mechanism-based aging model of battery is then introduced to formulate the optimization problem for minimal battery aging and energy consumption.The optimization problem is solved within a model predictive control framework for the real-time speed control of the vehicle.A non-cooperative platooning controller is further developed for the vehicle in adaptation to the traffic,where the intervehicular distance is controlled for reducing the air drag coefficient so that both the energy consumption and battery aging are improved.Simulation results show that for the single-vehicle controller,the battery degradation and energy consumption are,respectively,reduced by up to 25.7%and 3.2%compared with the cruise control strategy.Based on the non-cooperative controller,the HDT is able to follow preceding vehicles with different parameters with battery aging and energy consumption further,respectively,reduced by 2%–5%and 9%–10%compared with those of the single-vehicle controller.

Whole-lifetime Coordinated Service Strategy for Battery Energy Storage System Considering Multi-stage Battery Aging Characteristics

One battery energy storage system(BESS)can be used to provide different services,such as energy arbitrage(EA)and frequency regulation(FR)support,etc.,which have different revenues and lead to different battery degradation profiles.This paper proposes a whole-lifetime coordinated service strategy to maximize the total operation profit of BESS.A multi-stage battery aging model is developed to characterize the battery aging rates during the whole lifetime.Considering the uncertainty of electricity price in EA service and frequency deviation in FR service,the whole problem is formulated as a twostage stochastic programming problem.At the first stage,the optimal service switching scheme between the EA and FR services are formulated to maximize the expected value of the whole-lifetime operation profit.At the second stage,the output power of BESS in EA service is optimized according to the electricity price in the hourly timescale,whereas the output power of BESS in FR service is directly determined according to the frequency deviation in the second timescale.The above optimization problem is then converted as a deterministic mixed-integer nonlinear programming(MINLP)model with bilinear items.Mc Cormick envelopes and a bound tightening algorithm are used to solve it.Numerical simulation is carried out to validate the effectiveness and advantages of the proposed strategy.

Development of optimal speed trajectory control strategy for electric vehicles to suppress battery aging

In order to suppress the battery aging of electric vehicles(EVs),a multi-objective optimization function is established to describe the battery aging behavior based on a high-precision battery aging model,and the state–space equation is then constructed to reveal the intrinsic relationship between vehicle speed,acceleration,and battery state-of-charge(SOC).The constructed optimization model is solved by using a sequential quadratic programming(SQP)algorithm,and based on the model predictive control(MPC)theory,the efficient real-time control of vehicle speed is achieved.Simulation results show that the developed strategy extends the battery life by 10.33%compared to the baseline strategy when the traffic flow is not involved.In the case of involving the traffic flow,the optimization results of battery aging improves as the look-ahead time period increases,while the computational burden increases.The results show that the developed strategy reduces the battery aging of the target vehicle by 33.02%compared to the preceding vehicle while meeting the real-time requirement.

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.

Deep learning enhanced lithium-ion battery nonlinear fading prognosis

With the assistance of artificial intelligence,advanced health prognosis technique plays a critical role in the lithium-ion(Li-ion) batteries management system.However,conventional data-driven early aging prediction exhibited dramatic drawbacks,i.e.,volatile capacity nonlinear fading trajectories create obstacles to the accurate multistep ahead prediction due to the complex working conditions of batteries.Herein,a novel deep learning model is proposed to achieve a universal and accurate Li-ion battery aging prognosis.Two battery datasets with various electrode types and cycling conditions are developed to validate the proposed approaches.Knee-point probability(KPP),extracted from the capacity loss curve,is first proposed to detect knee points and improve state-of-health(SOH) predictive accuracy,especially during periods of rapid capacity decline.Using one-cycle data of partial raw voltage as the model input,the SOH and KPP can be simultaneously predicted at multistep ahead,whereas the conventional method showed worse accuracy.Furthermore,to explore the underlying characteristics among various degradation tendencies,an online model update strategy is developed by leveraging the adversarial adaptationinduced transfer learning technique.This work gains new sights into the comprehensive Li-ion battery management and prognosis framework through decomposing capacity degradation trajectories and adversarial learning on the unlabeled samples.

Two-stage aging trajectory prediction of LFP lithium-ion battery based on transfer learning with the cycle life prediction

With the wide application of the LFP lithium-ion batteries,more attention is paid to the battery life and future aging behaviors as the safety and performance of the battery are guaranteed by accurate battery aging monitoring.In recent years,long-term aging trajectory prediction of the lithium-ion battery is always a challenge due to its complex nonlinear aging behaviors especially the aging behaviors in the two aging stages are quite different when the battery experiences the two-stage aging process under fast-charging conditions.Thus,it is harder to achieve accurate long-term aging trajectory prediction of the LFP lithium-ion batteries on the condition of the two-stage aging process.To address it,a novel transfer learning strategy combined with the cycle life prediction technology is presented in this paper.Specifically,a new cycle life prediction method is proposed based on feature extraction and deep learning technology and achieves accurate cycle life prediction.The transfer learning is started by developing a base aging model offline to learn the information of the two-stage aging process.Then,taking the predicted cycle life as its prior information,the Bayesian model migration technology is employed to predict the aging trajectory accurately,and the uncertainty of the aging trajectory is quantified.Two batches of the battery datasets are used for performance evaluation and comparison with two benchmarks.It is novel to combine the cycle life prediction and transfer learning technique to achieve accurate two-stage aging trajectory prediction with only a few data available(first 30%).

Performance metrics for the comparison of lithium ion cell aging experiments

The increasing use of lithium-ion cells in large-scale,long-term applications drives a need for design methods that considers aging and accurate state of health estimation.A common approach is to rely on an empirical or semiempirical aging model fit to experimental data to estimate the evolution of capacity and power fade.Because aging data are costly to collect,pack designers either use Design of Experiment(DOE)techniques to define a set of efficient tests,or use existing aging data to calibrate aging models.Given the increasing quantity of available aging data,the question arises:how can experimental aging campaigns be quickly compared?However,a methodology for the comparison of sets of aging experiments is not discussed in the literature.As a result,pack designers usually rely on intuition to select between multiple aging studies proposed by DOE techniques or in the literature.This work proposes metrics to quantitatively capture the alignment between a set of aging experiments and a target application.These metrics allow pack designers to quickly compare many sets of aging experiments to evaluate those which have tested conditions relevant to the application.Case studies are presented to illustrate the application of these metrics using aging campaign data from the literature.To validate these metrics,this work examines the relationship between these metric values and aging model validation error for calendar aging data for 18650 NMC battery cells.It is demonstrated that greater metric values correspond to reduced model error for an empirical capacity fade model.

State of health based battery reconfiguration for improved energy efficiency

This paper analyzes the system-level state of health(SOH)and its dependence on the SOHs of its component battery modules.Due to stochastic natures of battery aging processes and their dependence on charge/discharge rate and depth,operating temperature,and environment conditions,capacities of battery modules decay unevenly and randomly.Based on estimated SOHs of battery modules during battery operation,we analyze how the SOH of the entire system deteriorates when battery modules age and become increasingly diverse in their capacities.A rigorous mathematical analysis of system-level capacity utilization is conducted.It is shown that for large battery strings with uniformly distributed capacities,the average string capacity approaches the minimum,implying an asymptotically near worst-case capacity utility without reorganization.It is demonstrated that the overall battery usable capacities can be more efficiently utilized to achieve extended operational ranges by using battery reconfiguration.An optimal regrouping algorithm is introduced.Analysis methods,simulation examples,and a case study using real-world battery data are presented.

Model Predictive Control Based Real-time Energy Management for Hybrid Energy Storage System

An accurate driving cycle prediction is a vital function of an onboard energy management strategy(EMS)for a battery/ultracapacitor hybrid energy storage system(HESS)in electric vehicles.In this paper,we address the requirements to achieve better EMS performances for a HESS.First,a long short-term niemory・based method is proposed to predict driving cycles under the framework of a model predictive control(MPC)algorithm.Secondly,the performances of three EMSs based on fuzzy logic,MPC,and dynamic programming are systematically evaluated and analyzed.For online implementation,the MPC-based EMS can alleviate the stress on the battery in the HESS and significantly reduce energy dissipation by up to 15.3% in comparison with the fuzzy logic-based EMS.Thirdly,the impact of battery aging on EMS performances is explored;it indicates that battery aging consciousness can slightly extend battery life.Finally,a hardware-in-the-loop test platform is established to verify the effectiveness of the MPC-based EMS for the power allocation of a HESS in electric vehicles.