Battery Cycle Aging Research Articles

Frontier ocean thermal/power and solar PV systems for transformation towards net-zero communities

Ocean thermal and power energy systems are promising driving forces for seashore coastal communities to achieve net-zero energy/emission target, whereas energy planning and management on ocean thermal/power and distributed building integrated photovoltaic (BIPV) systems are critical, in terms of serving scale sizing and planning on geographical locations of district building community, and cycling aging of battery storages. However, the current literature provides insufficient studies on this topic. This study aims to address this research gap by transforming towards zero-energy coastal communities from the district level in subtropical regions, including centralised seawater-based chiller systems, distributed BIPVs and coastal oscillating water column technologies, as well as multi-directional Vehicle-to-Building energy interaction paradigms. Advanced energy management strategies were explored to enhance renewable penetration, import cost-saving, and deceleration of battery cycling aging, in response to relative renewable-to-demand difference, off-peak grid information with low price, and real-time battery cycling aging. Furthermore, in accordance with the power generation characteristic of two wave stations (i.e., Kau Yi Chau (KYC) and West Lamma Channel (WLC)) in Hong Kong, energy system planning and structural configurations of the coastal community were proposed and comparatively studied for the multi-criteria performance improvement. Research results showed that, compared to an air-cooled chiller, the water-cooled chiller with a much higher Coefficient of Performance (COP) will reduce the energy consumption of cooling systems, leading to a decrease in total electric demand from 134 to 126.5 kWh/m2·a. The scale for the net-zero energy district community with distributed BIPVs and oscillating water column was identified as 5 high-rise office buildings, 5 high-rise hotel buildings, 150 private cars and 120 public shuttle buses. Furthermore, the geographical location planning scheme on the Case 1 (office buildings close to KYC, and hotel buildings close to WLC) was identified as the most economically and environmentally feasible scheme, whereas the Case 3 (only office buildings are planned close to all power supply with oscillating water column) showed the highest flexibility in grid electricity shifting, together with the highest value of equivalent battery relative capacity. This study demonstrates techno-economic performances and energy flexibility of frontier ocean energy technologies in a coastal community under advanced energy management strategies, together with technical guidance for serving scale sizing and planning on geographical locations. The research results highlight the prospects and promote frontier ocean energy techniques in subtropical coastal regions.

Power Battery Scheduling Optimization Based on Double DQN Algorithm with Constraints

Power battery scheduling optimization can improve the service life of the battery, but the existing heuristic algorithm has poor adaptability, and the capacity fluctuates significantly in the cycle aging process, which makes it easy to fall into the local optimal. To overcome these problems, we take the battery cycle life maximization as the goal, propose a reinforcement learning scheduling optimization model with temperature and internal resistance difference constraints, so as to determine whether to charge or discharge during battery cycle aging. We do this using the deep−learning−based battery capacity estimation model as the learning environment for the agent, using the Double DQN algorithm to train the agent, and proposing the principal component analysis method to reduce the dimension of the state space. These experiments, using multiple publicly available battery aging data sets, show that the principal component analysis method and the constraint functions reduce the computational time to find the optimal solution, providing the possibility of obtaining larger reward values. Meanwhile, the trained model effectively extends the cycle life of the battery, and has good adaptivity. It can automatically adjust parameters with the battery aging process to develop optimal charging and discharging protocols for power batteries with different chemical compositions.

Multi-Kernel Relevance Vector Machine With Parameter Optimization for Cycling Aging Prediction of Lithium-Ion Batteries

Aging prediction plays a vital role in battery prognostics and health management, which contributes to preventing unexpected failure and evaluating battery systems’ residual value. In this study, a reliable cycling aging prediction based on a data-driven model is proposed to address the urgent issue of adaptive and early prediction of lithium-ion battery remaining useful life (RUL). First, to enhance the learning and generalization abilities of standard relevance vector machines (RVMs), a multi-kernel RVM model containing two kernel functions with different characteristics is constructed, followed by the particle swarm optimization (PSO) algorithm for determining the kernel and weight parameters. Then, a similarity criterion of the battery capacity curves is proposed to screen battery offline data for model training to achieve early life prediction. Battery cycling aging data from two types of batteries under different aging conditions are used for model training and verification. Quantitative experimental results demonstrate that the proposed multi-kernel RVM model can realize the accurate prediction of the failure cycle and capacity attenuation trajectory of different types of batteries. Moreover, the proposed method has also been proved to be able to learn the general fading characteristics from other types of batteries.

Lithium-Ion Battery Life Prediction Method under Thermal Gradient Conditions

Thermal gradient is inevitable in a lithium-ion battery pack because of uneven heat generation and dissipation, which will affect battery aging. In this paper, an experimental platform for a battery cycle aging test is built that can simulate practical thermal gradient conditions. Experimental results indicate a high nonlinear degree of battery degradation. Considering the nonlinearity of Li-ion batteries aging, the extreme learning machine (ELM), which has good learning and fitting ability for highly nonlinear, highly nonstationary, and time-varying data, is adopted for prediction. A battery life prediction model based on the sparrow search algorithm (SSA) is proposed in this paper to optimize the random weights and bias of the ELM network and verified by experimental data. The results show that compared with traditional ELM and back-propagation neural networks, the prediction results of ELM optimized by SSA have lower mean absolute error percentages and root mean square errors, indicating that the SSA-ELM model has higher prediction accuracy and better stability and has obvious advantages in processing data with a high nonlinear degree.

A dynamic self-learning grid-responsive strategy for battery sharing economy—multi-objective optimisation and posteriori multi-criteria decision making

District building-vehicle interactive energy network with systematic energy interactions can improve techno-economic performances and enhance cleaner power penetration, whereas the battery cycling aging will be accelerated due to an increase in frequency of charging/discharging cycles. In this study, a dynamic self-learning grid-responsive strategy was proposed to improve techno-economic performances, energy flexibility, and the relative capacity (RC) of electrochemical batteries, through dynamically adjustable off-peak grid charging power. A collaborative framework on an energy district with building-integrated photovoltaics and electric vehicles has been formulated, through synergic energy interactions and integrations. Cycling aging of both static batteries in buildings and mobile vehicle batteries has been considered. In respect to intrinsic contradiction characteristics of each objective, trade-off solutions have been proposed through multi-objective optimisation. Furthermore, regarding the multiple optimal solutions along the Pareto front, a weighted Eulerian distance-based methodology was adopted to identify the ‘best of best’ solution and then assist multi-criteria decision-making. Research results show that, with the proposed dynamic self-learning grid-responsive strategy, the grid can provide a more flexible grid-to-battery charging power with a higher RC of batteries, compared to the strategy with dynamic renewable-demand signals. Through the multi-objective optimisation, the import cost can be reduced from 213.2 to 203.8 HK$/m2·a, and the RC can be improved from 0.800 to 0.999. This study also demonstrates a weighted Eulerian distance-based methodology to identify the ‘best of best’ solution to multi-criteria decision-makers. Research results are useful to promote district interactive energy networks in smart cities, with multivariant optimal design, Pareto optimal solutions, and weighted Eulerian distance-based ‘best of best’ solution.

An integration and selection scheme for capacity estimation of Li-ion battery based on different state-of-charge intervals

Much research has been done to predict batteries' cyclic aging state-of-health (SOH) over the entire state-of-charge (SOC) range in constant-voltage and constant-current charge-discharge mode. Considering that the battery cycle is usually carried out in part of the SOC range in the actual use process, this paper further studies the cycle degradation characteristics of Lithium-ion (Li-ion) batteries in the partial SOC ranges. First, to clarify the factors that affect battery cycle aging, the changes in battery degradation characteristics caused by various stresses are analyzed. Then, a scheme to construct the state of charge - voltage (SOC-V) curve based on the first-order equivalent circuit and lumped thermal model is introduced for accurately extracting health indexes (HIs) on incremental capacity (IC) and differential voltage (DV) curves. Meanwhile, the least-squares method is used to identify the offline parameters of the proposed model. Perform a sensitivity analysis on the equation complexity and fitting effect, which can determine the exact model order. Multiple regression models are employed, which map the relationship between HIs fluctuations and capacity degradation for real-time estimation of SOH in different SOC intervals. Finally, different SOC intervals screen out the most accurate prediction models to realize the SOH prediction in all SOC cycle ranges. In this framework, the results show that SOH prediction based on SOC interval is desirable. Analysis results reveal the above measures' trustworthiness, with the average predicted error being within 1 % under the cross-validation test. What's more, a set of different types of batteries are used to verify the robustness of this method.

A novel method of battery state-of-health estimation and remaining useful life prediction based on grid search-support vector regression

The online estimation of battery SOH (state of health) and prediction of RUL (remaining useful life) are a prerequisite of ensuring its safe and reliable operation, optimal balance and battery SOC estimation. Considering the actual situation, a relatively stable charging environment is more suitable for accurate estimation and prediction of SOH and RUL compared to uncertain discharge conditions. A health indicator based on charging characteristics is selected to predict battery degradation trends through Pearson and Spearman correlation analysis. The grid search optimisation (GS) algorithm and support vector regression (SVR) are integrated to extract the intrinsic relationship between the health indicator and capacity, combining GS algorithm to optimise SVR kernel parameters. With the Lithium-ion data set provided by NASA, it’s proved that the GS-SVR fusion model is more effective in estimating battery life decay than the SVR model, with a reduction of 52.64% in mean absolute error and 68.51% mean square error. Besides, the R 2 of the GS-SVR model is 97.3%, and the robustness of the model is stronger. The RUL prediction results demonstrate the effectiveness and advantage. Highlights The methods of obtaining indirect health indicators were discussed in detail. Data-driven algorithm based on small samples to realise online model estimation. The estimation results based on GS-SVR model and SVR model were compared. Application to data taken from the Lithium-ion battery cycle aging test of the NASA’s Prognostics Center of Excellence.

Regenerative braking-based hierarchical model predictive cabin thermal management for battery life extension of autonomous electric vehicles

This paper conducts a regenerative braking-based cabin thermal management by developing a hierarchical model predictive control (MPC) strategy. At the higher layer, an MPC controller is developed for optimally planning the vehicle speed. The lower layer implements cabin thermal management based on the planned regenerative braking behavior from the upper layer, where the recuperative energy can be used to directly power the air conditioning (AC) system. The state-space equation of a re-constructed power flow of vehicle is constructed, and based on which, an optimization problem is formulated to minimize energy consumption for battery aging-conscious control. Experiments are conducted to verify the performance of the hierarchical MPC strategy. Simulation results show that the vehicle drives in adaptation to the road topographies, the traffic flow and traffic signals in an energy-efficient way. The shifts of AC power load and cabin thermal load are caused so that the recuperative energy is fully used and the battery charging/discharging (aging) is minimized. Compared to the benchmark method, the energy consumption is reduced by 3.38% and the battery cycling aging is reduced by up to 29.15%.

Optimal battery schedule for grid-connected photovoltaic-battery systems of office buildings based on a dynamic programming algorithm

The optimal schedule of energy storage systems is an effective way to improve the economy and stability of grid connected photovoltaic-battery energy storage systems (PV-BESS). This study presents an operation strategy considering economic feasibility and photovoltaic self-consumption rate (SCR) for the energy management of office buildings under time-of-use (ToU) electricity price. The strategy aims to optimize FiT revenue streams for the PV-BESS by scheduling the overall energy flow in real time based on a dynamic programming algorithm. The battery control strategy based on a dynamic programming algorithm can control the energy flow in a flexible way to minimize net present value (NPV) in a typical year, while taking such factors as dynamic electricity price, the battery cycling aging, and demand response characteristics into account. An existing PV-BESS used for a middle-size office building in Beijing was taken as a case study to evaluate the optimization model. It is shown that the dispatch strategy could achieve superior performance in the cold region in China. Additionally, the indices affecting economic performance were figured out to validate the feasibility of the proposed algorithm. The results show that while the unit cost of energy storage is dropped to about 100$/kWh, the system could gain revenue under different electricity prices. Lastly, it is concluded that electricity price is the most sensitive parameter to the system's economy through sensitivity analysis.

Improved Cycle Aging Cost Model for Battery Energy Storage Systems Considering More Accurate Battery Life Degradation

Battery energy storage systems (BESSs) have been widely used in power grids to improve their flexibility and reliability. However, the inevitable battery life degradation is the main cost in BESS operations. Thus, an accurate estimation of battery aging cost is strongly needed to cover the actual cost of BESSs. The existing models of battery life degradation either are not fully accurate to estimate the actual cost or are not solved easily because of their computation nonlinearity. In this paper, a piece-wise linear battery aging cost model with an accurate estimate of battery life degradation for BESSs is proposed to extend battery life and improve battery profits. In our method, the widely-used Arrhenius law is modified to quantify the battery life degradation affected by the depth of cycle. Further, a nonlinear battery cycle aging cost model is developed by finding the derivative of battery life degradation with respect to discharging power, which indicates the battery life degradation rate due to depth of cycle. To reduce the complexity of computation, a piece-wise linearization method is proposed to simplify the battery cycle aging cost model. Finally, the cycle aging cost model with an accurate estimation of battery life degradation is applied to the optimization dispatch in the day-ahead energy and auxiliary service market. The results show that the error of estimating the battery cycle aging cost of BESSs is less than 5% under proper piece-wise segment numbers. The profits are increased by 27% and the battery life is extended by 11% than the fixed cost method.

Prognostics for lithium-ion batteries using a two-phase gamma degradation process model

To address the degradation of rechargeable batteries, this paper presents a two-phase gamma process model with a fixed change-point for modeling the voltage-discharge curves of battery cycle aging under a constant current. The model can be applied to estimate the state of charge (SOC) and the remaining useful discharge time (RUT) in a cycle with consideration of the effect of cycle aging, and can also be applied to estimate the state of life (SOL) and the remaining useful life (RUL) across cycles. The applications of the proposed model are demonstrated using the experimental cycle aging data of a lithium iron phosphate battery. A comparison shows that the proposed model generates a more accurate prediction than the conventional two-term exponential model with capacity data under a particle filter framework, and this reveals the superiority of modeling with voltage over modeling with capacity. The analytical expression of mean useful discharge time in a cycle (or mean time to failure) is developed with approximation by a Taylor expansion and the Birnbaum-Saunders distribution, and the result is shown to be in good agreement with the true mean of a gamma process.

An energy paradigm transition framework from negative towards positive district energy sharing networks—Battery cycling aging, advanced battery management strategies, flexible vehicles-to-buildings interactions, uncertainty and sensitivity analysis

In response to the clean power production with large-scale deployment of renewable systems, technical challenges are proposed, including the resilient and smart building system design, cycling aging of battery storages, energy congestion between renewable and flexible grid energy, the flexible micro-grids to energy supply fluctuations in multi-energy systems, and so on. In this study, a series of technical solutions, including the integration of plug-in vehicles (under multi-directional energy interaction paradigms), the Vehicles-to-Buildings interaction levels, and the grid-responsive control, were proposed, studied, and discussed to promote the resilient and smart energy systems from district levels in subtropical regions. Dynamic battery cycling aging and advanced modelling tool in this study outperforms traditional rough battery cycling aging approaches, and correct conclusions on battery and vehicle-to-building interaction, to avoid the battery performance overestimation. Novel energy management strategies advance traditional power flow strategy, in terms of the off-peak grid electricity shifting, the enhancement of renewable penetration, and the deceleration of battery cycling aging. Techno-economic performances have been investigated, including net present value (NPV), the discounted payback time (DPT), and the net direct energy consumption (DEC). Furthermore, the energy paradigm transition from negative to positive building–vehicle systems outperforms single case study in academia for carbon-neural transition. The research results showed that, with the energy paradigm transition from the negative to the positive system, the net present value increases from −7.182 × 107 to 5.164 × 108 HK$, and the average annual net DEC decreased from 249.1 to −343.3 kWh/m2.a. Furthermore, compared to Control Strategy 2 (grid-responsive control strategy), the proposed Control Strategy 3 (battery-protective control strategy) can improve the net present value, and the increasing magnitude is dependent on the specific energy paradigm. Furthermore, compared to the positive buildings-vehicle system, the impact of the vehicle-to-building interaction on the decreasing magnitude of the net present value is more prominent for the negative system. The uncertainty and sensitivity analyses indicate that the net present value of the positive energy paradigm as compared to the negative energy paradigm is more sensitive to the battery cost. This study demonstrates the techno-economic performance of district building–vehicle systems with the energy paradigm transition from the negative to the positive, together with a series of effective solutions. The research results can provide multi-dimensional effective approaches for district energy sharing systems in subtropical regions.

A Novel Capacity Estimation Approach for Lithium-Ion Batteries Combining Three-Parameter Capacity Fade Model With Constant Current Charging Curves

Accurate and reliable capacity estimation for lithium-ion batteries adapting to uncertain environment plays a significant role in the fields of electric vehicles and renewable energy systems. This paper proposes a novel capacity fusion estimation approach based on three-parameter capacity fade model and constant current charging curves using dual unscented Kalman filters. In view of the battery cycle aging problem under variable temperature situation, a three-parameter capacity fade model is introduced to continuously estimate the battery cell capacity for unknown changes of ambient temperature. Nevertheless, the model parameters may mismatch in practical applications, so the first unscented Kalman filter views the discrete capacity based on constant current charging curves as observations to periodically update the parameters. Thereafter, the second one performs a fusion operation depending on the capacity estimated by constant current charging curves and the three-parameter capacity fade model with the corrected parameters, so as to improve the capacity estimation accuracy online. The results show that the fusion estimation errors of capacity are controlled within 2% by updating the model parameters, and the robustness of the proposed method is also validated.

Deep reinforcement learning‐based optimal data‐driven control of battery energy storage for power system frequency support

A battery energy storage system (BESS) is an effective solution to mitigate real-time power imbalance by participating in power system frequency control. However, battery aging resulted from intensive charge–discharge cycles will inevitably lead to lifetime degradation, which eventually incurs high-operating costs. This study proposes a deep reinforcement learning-based data-driven approach for optimal control of BESS for frequency support considering the battery lifetime degradation. A cost model considering battery cycle aging cost, unscheduled interchange price, and generation cost is proposed to estimate the total operational cost of BESS for power system frequency support, and an actor–critic model is designed for optimising the BESS controller performance. The effectiveness of the proposed optimal BESS control method is verified in a three-area power system.

Coordinated multi-criteria framework for cycling aging-based battery storage management strategies for positive building–vehicle system with renewable depreciation: Life-cycle based techno-economic feasibility study

The battery-protective control strategy, vehicle-to-building (V2B) interactions, and economic policy regarding grid feed-in tariff are promising techniques for enhancing the lifecycle-based techno-economic performance. However, the current literature indicates limited progress in the techno-economic feasibility of positive building–vehicle systems, with considerations on battery cycling aging, degradation of hybrid renewable generations, and dynamic grid-responsive control strategy. In this study, an interactive building–vehicle system was mathematically formulated with multidirectional energy interaction paradigms, such as building-to-vehicle, grid-to-building, building-to-grid, and grid-to-vehicle. The roles of the V2B interaction levels, grid feed-in schemes, and energy management strategies in techno-economic performances were quantitatively characterised, from the perspectives of the net present value (NPV), discounted payback time (DPT), and net direct energy consumption. Novel energy management strategies were proposed for battery relative capacity improvement in response to the dynamic renewable-demand signal and grid information. Furthermore, uncertainty and sensitivity analyses were performed on the renewable system cost, battery cost, interest rate, and escalation rate of the electricity price. The results indicated that the ignorance of battery cycling aging results in an overestimation on NPV from 2.404 × 108 (0.310 × 108 US$) to 3.005 × 108 HK$ (0.388 × 108 US$). Furthermore, compared to traditional rough estimation on battery aging (with the NPV at 2.446 × 108 HK$ and the DPT at 4 years), the dynamic model in this study can provide more accurate and reliable battery relative capacity, together with conservative assessment on techno-economic feasibility (the NPV at 1.860 × 108 HK$ (0.240 × 108 US$) and the DPT at 5 years). In comparison with the grid feed-in tariff policies in most European countries, the implementation of the latest grid feed-in tariff in Hong Kong can increase the NPV from −7.633 × 107 (−0.985 × 108 US$) to 2.303 × 108 HK$ (0.297 × 108 US$) for the positive building–vehicle system. An interactive building–vehicle system was formulated, together with advanced management strategies for lifecycle-based techno-economic performance enhancement. Comparative analyses among scenarios with different control strategies, V2B interaction levels, and grid feed-in tariff schemes were performed to verify the reliability of the proposed technique for positive building–vehicle systems.

Electric Vehicle Battery Cell Cycle Aging in Vehicle to Grid Operations: A Review

Electric vehicle (EV) owners need vehicle to grid (V2G) operations information before being able to participate in power grid support. Specifically, the lack of understanding of the effect of battery charge and discharge on the lifetime of battery cells inhibits full participation. Batteries are expensive, but increasing their lifetime is one solution to reduce costs. Several battery aging models have been developed to calculate the lifetime and cost of V2G operations. This review contributes to what is already known by connecting measurement data, driving data, and V2G operations to the battery cycle aging model. The review identified time, temperature, and state of charge influence calendar aging. In addition, a number of cycles, depth of discharge and charging rate influence cycle aging. Results indicated the battery’s naturally decreasing lifetime can be reduced and scheduled charging can extend lifetime. The implications of V2G operations on battery durability play an important role in the convenience for EV owners to support the power grid with ancillary service contributions.

Energy storage and management system design optimization for a photovoltaic integrated low-energy building

This study aims to analyze and optimize the photovoltaic-battery energy storage (PV-BES) system installed in a low-energy building in China. A novel energy management strategy considering the battery cycling aging, grid relief and local time-of-use pricing is proposed based on TRNSYS. Both single-criterion and multi-criterion optimizations are conducted by comprehensively considering technical, economic and environmental performances of the system based on decision-making strategies including the weighted sum and minimum distance to the utopia point methods. The single-criterion optimizations achieve superior performances in the energy supply, battery storage, utility grid and whole system aspect respectively over the existing scenario of the target building. The multi-criterion optimization considering all performance indicators shows that the PV self-consumption and PV efficiency can be increased by 15.0% and 48.6% while the standard deviation of net grid power, battery cycling aging and CO2 emission can be reduced by 3.4%, 78.5% and 34.7% respectively. The significance and impact of design parameters are further quantified by both local and global sensitivity analyses. This study can provide references for the optimum energy management of PV-BES systems in low-energy buildings and guide the renewable energy and energy storage system design to achieve higher penetration of renewable applications into urban areas.

Optimal Battery Control Under Cycle Aging Mechanisms in Pay for Performance Settings

We study the optimal control of battery energy storage under a general "pay-for-performance" setup such as providing frequency regulation and renewable integration. In these settings, batteries need to carefully balance the trade-off between following the instruction signals and their degradation costs in real-time. Existing battery control strategies either do not consider the uncertainty of future signals, or cannot accurately account for battery cycle aging mechanism during operation. In this work, we take a different approach to the optimal battery control problem. Instead of attacking the complexity of battery degradation function or the lack of future information one at a time, we address these two challenges together in a joint fashion. In particular, we present an electrochemically accurate and trackable battery degradation model called the rainflow cycle-based model. We prove the degradation cost is convex. Then we propose an online control policy with a simple threshold structure and show it achieve near-optimal performance with respect to an offline controller that has complete future information. We explicitly characterize the optimality gap and show it is independent to the duration of operation. Simulation results with both synthetic and real regulation traces are conducted to illustrate the theoretical results.

Stochastic operation of home energy management systems including battery cycling

The present work proposes a stochastic approach for Day-Ahead operation of Home Energy Management Systems when batteries, solar photovoltaic resources and Electric Water Heaters are considered. The optimization problem minimizes the operation costs formed by energy procurement in the wholesale market and the equivalent cycling aging cost of the batteries, and also includes the uncertainty of the PV production and the load. The complete two-stage stochastic formulation results in a Mixed-Integer Nonlinear Programming problem that is decomposed using a Competitive Swarm Optimizer to handle the calculation of the battery cycling aging cost. A Storage Disaggregation Algorithm based on Lagrangian relaxation is used to reduce the problem size and to allocate individual State of Charge for the batteries. In addition, the advantages of considering a stochastic approach are shown by means of the Value of the Stochastic Solution. This methodology has been developed in the context of the Horizon 2020 project SENSIBLE as part of the tasks related to a use case that considers an aggregator that participates in the electricity market with a portfolio of prosumers with active demand capability.

Predictive Multi-Objective Operation Strategy Considering Battery Cycle Aging for Hybrid Electric Vehicles

Predictive Multi-Objective Operation Strategy Considering Battery Cycle Aging for Hybrid Electric Vehicles