Extend Battery Lifetime Research Articles

Lithium-Ion Battery Lifetime Extension With Positive Pulsed Current Charging

Some studies verified that the pulsed current charging technique could extend the battery lifetime and improve the charging performance of lithium-ion batteries. However, some researchers are skeptical of this opinion because their studies have not found any advantages of pulsed current charging over conventional Constant Current (CC) charging. Positive Pulsed Current (PPC), the most common pulsed current mode, was selected for the investigation in this work. The effect of the PPC with various parameters, including the duty cycle, amplitude, and frequency, on the performance and lifetime of lithium-ion batteries, are investigated by experiments. According to the experimental results, the charging speed, charging capacity, and maximum rising temperature are mainly determined by the duty cycle and amplitude of the PPC. The battery lifetime with PPC under the frequency range from 0.05 Hz to 2 kHz has been verified to be extendable. Moreover, the battery lifetime extension can even be over 100% under a specific frequency PPC compared with CC charging. This is the first work that summarizes the law of the pulse frequency on battery lifetime extension. In the future, the pulsed current is expected to be used in fast charging technologies without compromising battery lifetime.

Experimental validation of a real-time energy management system using four frequency-based approaches

An innovative approach is introduced in this study for optimizing power distribution within a hybrid energy storage system, which encompasses batteries and supercapacitors. The method involves utilizing an adaptive wavelet technique within the energy management system dedicated to the EV. This technique customizes the wavelet decomposition level based on the supercapacitor's state of charge. As a result, optimal power sharing between the two real sources incorporated in a small-scale EV emulator is achieved. Notably, the approach adjusts the wavelet decomposition level during transient high peaks and when the supercapacitor has high reserves to alleviate battery workload. Through experimental validation and comparative analysis with three other frequency-based techniques, it is demonstrated that the adaptive wavelet control strategy significantly extends battery lifetime. This extension is achieved by reducing the root mean square (RMS) current by 34 % compared to fixed cut-off frequency approaches. Consequently, this minimizes losses and safeguards the battery from high peaks, resulting in a substantial decrease of 44.53 %. These results underscore the substantial potential of the adaptive wavelet approach as a suitable solution for effective energy management in hybrid electric vehicles.

The Trade-Off between Extended Lifetime and Resource Utilization Efficiency - a Critical Analysis of Battery Second-Use Concepts Using Materials Flow Analysis

Sustainability and environmental concerns have recently led to heavy investment in the electrification of transport. A major implication of this transition is a surge in demand for batteries, the state-of-the-art energy technology for electric vehicles. While electric vehicles are locally free of emissions, battery production and end-of-life management still challenge its status as an economically, socially, and environmentally sustainable technology. Among these challenges are the globally unequal distribution of raw materials and associated geopolitical, supply and trade-related concerns as well as the high costs and environmental risks of batteries’ waste management. Furthermore, landscape disruption, biodiversity loss, and concerns over child labor and other human rights abuses haunt the mining of raw materials.The circular economy promises to remedy many of these issues, by essentially allowing economic growth without the consumption of raw materials. It has enormous potential for the European Union in particular, as it has a growing trade deficit of raw materials due to a lack of resource deposits on the continent. Therefore, independence from them is expected to boost its international competitiveness. Besides reducing raw material dependence and thus supply risks, the circular economy promises to stimulate business model and product innovation, to meet the demands for recyclable and more durable products as well as recycling goals, further strengthening competitiveness.In the case of electric vehicles, recycling processes have been established to allow the regeneration of battery-grade raw materials from batteries. Furthermore, models of extending the battery’s life cycle by applying it in a secondary application with less demanding energy and power requirements have recently been proposed, termed “second use”. This delays the point in time when a battery is recycled. Materials-break-even-points (BEP), the points in time at which an economy can achieve independence from raw materials and thus reach circularity, can be a tool of analysis for the overall effect of this delay. Second use increases the duration until the BEP, while at the same time reducing the annual demand for primary raw materials. Despite the shift in BEP, it is frequently argued that the extended battery lifetime and the reduction in primary materials demand improve resource utilization. We argue, however, that second use could have undesirable side effects. This is because due to the capacity and voltage fade occurring over a battery’s lifespan and the improvements in material efficiency through advances in research and development over time, the average amount of raw material required for the storage of one unit of energy increases relative to a scenario where the battery is recycled earlier. Hence, the overall amount of material invested in the circular economy would be larger in a scenario with second use compared to a scenario without second use. Depending on the battery technology in question and thus the materials involved, this trade-off between the amount of material invested in the economy and the use span of a battery could play out differently with respect to economic, social, and environmental effects.In this work, this trade-off is investigated for a scenario involving different shares of LFP-based lithium-ion batteries, NCX-based lithium-ion batteries, and other technologies, with a focus on the raw materials Ni, Co and Li. Technology-specific scenarios are investigated, as different technologies require different raw materials, impacting the material flow rate over time. Furthermore, different technologies exhibit a varying degree of voltage and capacity fade over their lifetime, thus affecting the resource utilization efficiency in a second-use scenario. To this end, dynamic material flow analysis is applied, and the societal, economic, and ecological consequences of our findings are discussed from a critical perspective.The insights provided by this analysis are first and foremost a contribution toward understanding the principles that determine the long-term resource efficiency of a battery circular economy. These principles are vital for policymakers when envisaging political goals and incentive schemes for the transition toward a circular economy. The findings are also vital for actors from the battery and automotive industry when developing end-of-life strategies for electric vehicle batteries, as ensuring the most efficient use of battery raw materials improves resource availability and minimizes their products' environmental and societal impact.

Aging-Sensitive Fast-Charging for Heavy-Duty Electric Vehicles

To use lithium-ion cells on-board electric vehicles (EVs) safely and efficiently, a battery management system (BMS) is essential. It monitors the battery’s states and controls utilization by limiting e.g., current and temperature. Advanced BMS increasingly use electrochemical models to assess internal states of battery cells and apply usage constraints based on these [1]. The performance of an electrochemical BMS is closely tied to the performance of the underlying model and the accuracy of its parameters [2]. We employ a novel re-parametrization method to update electrochemically constrained optimal fast-charging procedure to minimize battery degradation. The parameters of electrochemical models are typically identified through a combination of physico-chemical characterization techniques and curve fitting [3]. Since model parameters change with aging [4], there is a need to re-parametrize electrochemical models on-board to conserve model accuracy. As basis for this re-parametrization, we propose a novel reference performance test that can be incorporated into a conventional over-night charge and performed intermittently.Optimal control has previously been demonstrated for fast-charging in EV applications [1]. However, electrochemical, and mechanical degradation need to be considered to allow optimal fast charging throughout batteries’ lifetimes. Khalik et al. [5] demonstrated how a charging procedure could be designed a-priori for the entire cycle life, considering a direct implementation of the solid-electrolyte-interphase growth and capacity fade due to the associated lithium consumption. Such approaches can extend battery lifetime, but neither yield optimal charging-procedures at every state-of-health nor handle unknown aging modes on the fly. Our proposed approach involves intermittent identification of electrochemical model parameters and subsequent update of the fast-charging protocol such that it adheres to electrochemical constraints throughout the entire battery lifetime.In this work, summarized in Figure 1, we present this methodology and show results from an experimental study testing the proposed approach vs. conventional charging procedures. The experimental study is performed on state-of-the art automotive cells used in heavy-duty electric vehicles.Figure caption: Comparison of a conventional electrochemically constrained fast-charging procedure with the aging-sensitive approach proposed in the present work.

Battery longevity-conscious energy management predictive control strategy optimized by using deep reinforcement learning algorithm for a fuel cell hybrid electric vehicle

Energy management strategies play an essential role in improving fuel economy and extending battery lifetime for fuel cell hybrid electric vehicles. However, the traditional energy management strategy ignores the lifetime of the battery for good fuel economy. To overcome this drawback, a battery longevity-conscious energy management predictive control strategy is proposed based on the deep reinforcement learning algorithm predictive equivalent consumption minimization strategy (DRL-PECMS) in this study. To begin with, the back-propagation neural network is devised for predicting demand power, and the predictive equivalent consumption minimum strategy (PECMS) is proposed to improve the hydrogen consumption. Then, in order to improve the battery durability performance, the deep reinforcement learning algorithm is utilized to optimize the battery power and improve battery lifetime. Finally, numerical verification and hard-ware in the loop experiments are conducted to validate hydrogen consumption and battery durability performance of the proposed strategy. The validation results show that, compared with CD/CS and SQP(Sequential Quadratic Programming), the PECMS combined can achieve better fuel economy with the fuel consumption reduction by 55.6 % and 5.27 %, which effectively improves the fuel economy. The DRL-PECMS can reduce the effective Ah-throughput by 3.1 % compared with the PECMS. The numerous validations and comparisons demonstrate that the proposed strategy effectively accomplishes the trade-off optimization between energy consumption and battery durability performance.

Adaptive energy management strategy based on a model predictive control with real-time tuning weight for hybrid energy storage system

The effective utilization of ultra-capacitor (UC) in the energy allocation process is crucial for improving the efficiency of the energy management strategy (EMS) for hybrid energy storage system (HESS). In this paper, we present an adaptive energy management strategy framework based on a model predictive control (MPC) with real-time tuning weight to optimize UC utilization and extend battery lifetime. Firstly, the adaptive autoregressive integrated moving average model (AARIMA) is developed to accurately predict vehicle velocity and road gradient for online power demand computation, based on local historical information. The differencing order and lags of the model in AARIMA are updated using the augmented Dickey-Fuller (ADF) test and Bayesian Information Criterion (BIC). Secondly, the MPC is developed to optimize power allocation using quadratic programming. Finally, the weight in the cost function is real-time tuned via the fuzzy logic rule according to the predicted power. Furthermore, the proposed strategy is validated using a scale-down hardware-in-the-loop verification platform. Simulation results demonstrate that the UC utilization ratio has a 9.91% improvement, and the battery lifetime has a prolonged by 24.38% across two tested actual and two typical driving cycles. Consequently, the proposed EMS achieves optimization of both energy management efficiency and battery lifetime.

Optimization of speed trajectory for electric wheel loaders: Battery lifetime extension

The electrification of wheel loaders is considered a leading trend due to its advantage of zero‑carbon emissions. However, the inevitable phenomenon of battery degradation has led to increased battery usage and maintenance costs. This study first extends the battery lifetime by optimizing the speed trajectory based on the typical loading cycle of the wheel loader. The optimal control problem is formulated by systematically modeling the wheel loader's powertrain and using a precise semi-empirical battery aging model. To reduce computational costs, the modified optimal control problem includes a weighted penalty on travel time. Unlike the conventional dynamic programming method that takes substantial computation time, a combined algorithm of dynamic programming and Brent's method (DP-BM) is first introduced to provide a numerical solution to the optimization problem with a reduced computational burden. Simulation results demonstrate that the optimized trajectory can decrease the average power consumption of the battery and reduce the number of full equivalent cycles, resulting in a 4.48% improvement in the average battery lifetime compared to the typical trajectory. Modeling errors are analyzed to make sure the results are reliable. Furthermore, the proposed approach significantly reduces computation time compared to the conventional dynamic programming method, with an average reduction of 95%. The total cost for each year is finally summarized, and the results indicate that the optimized trajectory offers a distinct advantage in terms of cost-effectiveness.

A Low-Cost Battery-Balancing Auxiliary Power Module With Dual-Active Half Bridge Links and Coreless Transformers

Redistributive balancing brings many benefits to electric vehicles, such as increased range and more uniform cell degradation. Despite previous analyses suggesting a 17-36% extension of battery lifetime, the cost of such systems has prevented the technology from being adopted in practice. This study proposes a simplified topology for battery-balancing auxiliary power modules with reduced magnetic material to achieve cost-friendly solutions without sacrificing functionality or balancing modes. The proposed system consists of a dual-active half bridge, halving the number of switches needed compared with the conventional dual-active bridge. In addition, the transformer core material is removed and a coreless transformer is used to provide isolation and energy transfer. Both changes reduce the cost of redistributive balancing. The topological modeling differs from cored transformer circuits as the coupling of the coreless transformer is weaker. The coupling coefficient is now used in an updated model that includes transformer currents and output powers. These, along with the balancing modes, are analyzed then experimentally verified. The proposed models can guide the selection of MOSFETs and the design of the coreless transformer. An analytic projection shows up to a 22% cost reduction compared with similar topologies.

Operating strategy optimization considering battery aging for a sector coupling system providing frequency containment reserve

Battery storage systems are increasingly used in the frequency containment reserve (FCR) application. Coupling the battery with a power-to-heat module can further increase the system’s profitability. Different studies optimized the hybrid system’s operation by considering country-specific regulations. However, lithium-ion battery aging has not yet been included. This work analyses the impact of system dimension and operation on net present value, considering battery aging. The operation strategy consists of adjusting the target SoE and using the deadband degree of freedom, which allows to suspend provision of FCR for minor frequency deviations. Battery lifetime is simulated using a semi-empirical aging model describing graphite anode aging mechanisms. The highest NPV is obtained with a battery dimension of 0.5 Wh/W, 90% target SoE, and normal deadband utilization. For the considered NMC battery cells, deadband utilization contributes to battery lifetime extension when operating at high SoE. Without this cell chemistry-specific lifetime gain, a lower target SoE of 60% and a larger system size of 0.6 is economically more advantageous. In a sensitivity analysis, battery aging was detected as the most crucial parameter influencing system profitability at high FCR prices. The results highlight the importance of including reliable battery aging simulations in future FCR operation optimizations.

Integrating scenario-based stochastic-model predictive control and load forecasting for energy management of grid-connected hybrid energy storage systems

In the context of renewable energy systems, microgrids (MG) are a solution to enhance the reliability of power systems. In the last few years, there has been a growing use of energy storage systems (ESSs), such as hydrogen and battery storage systems, because of their environmentally-friendly nature as power converter devices. However, their short lifespan represents a major challenge to their commercialization on a large scale. To address this issue, the control strategy proposed in this paper includes cost functions that consider the degradation of both hydrogen devices and batteries. Moreover, the proposed controller uses scenarios to reflect the stochastic nature of renewable energy resources (RESs) and load demand. The objective of this paper is to integrate a stochastic model predictive control (SMPC) strategy for an economical/environmental MG coupled with hydrogen and battery ESSs, which interacts with the main grid and external consumers. The system's participation in the electricity market is also managed. Numerical analyses are conducted using RESs profiles, and spot prices of solar panels and wind farms in Abu Dhabi, UAE, to demonstrate the effectiveness of the proposed controller in the presence of uncertainties. Based on the results, the developed control has been proven to effectively manage the integrated system by meeting overall constraints and energy demands, while also reducing the operational cost of hydrogen devices and extending battery lifetime.

SamurAI: A Versatile IoT Node With Event-Driven Wake-Up and Embedded ML Acceleration

Increased capabilities such as recognition and self-adaptability are now required from IoT applications. While IoT node power consumption is a major concern for these applications, cloud-based processing is becoming unsustainable due to continuous sensor or image data transmission over the wireless network. Thus optimized ML capabilities and data transfers should be integrated in the IoT node. Moreover, IoT applications are torn between sporadic data-logging and energy-hungry data processing (e.g. image classification). Thus, the versatility of the node is key in addressing this wide diversity of energy and processing needs. This paper presents SamurAI, a versatile IoT node bridging this gap in processing and in energy by leveraging two on-chip sub-systems: a low power, clock-less, event-driven Always-Responsive (AR) part and an energy-efficient On-Demand (OD) part. AR contains a 1.7MOPS event-driven, asynchronous Wake-up Controller (WuC) with a 207ns wake-up time optimized for sporadic computing, while OD combines a deep-sleep RISC-V CPU and 1.3TOPS/W Machine Learning (ML) for more complex tasks up to 36GOPS. This architecture partitioning achieves best in class versatility metrics such as peak performance to idle power ratio. On an applicative classification scenario, it demonstrates system power gains, up to 3.5x compared to cloud-based processing, and thus extended battery lifetime.

Lightweight electrocardiogram signal compression

Cardiovascular diseases are the primary cause of death around the world. Cardiovascular ailments can be monitored continuously using wearable devices, which are resource-constrained and battery-operated. Efficient data compression is a promising method to reduce transmission energy cost and extend battery lifetime. In this paper, we first propose two novel pruning methods with pruning matrix’s entries taken from the set {−1, 0, 1}, which achieve similar reconstruction quality as compared to DCT pruning but with far less computational cost due to multiplierless operation. Reconstruction for these pruning methods is proposed using Gaussian elimination. Then we design two lightweight compression algorithms based on the pruning methods, i.e., sign compression algorithm and binary compression algorithm. The proposed compression algorithms and pruning methods are evaluated using performance metrics such as compression ratio, percentage root mean square difference, quality score, etc. Furthermore, The pruning methods are implemented in TelosB mote and execution time is evaluated. The experiments are conducted on the MIT-BIH arrhythmia database. The experimental results show that the proposed compression algorithms outperform the state-of-the-art methods.

Optimizing energy efficiency of LoRaWAN-based wireless underground sensor networks: A multi-agent reinforcement learning approach

Extended battery lifetime is always desirable for wireless underground sensor networks (WUSNs). The recent integration of LoRaWAN-grade massive machine-type communications (MTC) technology and WUSNs, namely LoRaWAN-based WUSNs, provides a promisingly energy-efficient solution for underground monitoring. However, due to the limited battery energy, massive data collisions and dynamic underground environment, the energy efficiency of LoRaWAN-based WUSNs still possesses a significant challenge. To propose a liable solution, we advocate reinforcement learning (RL) for managing the transmission configuration of underground sensors. In this paper, we firstly develop the multi-agent RL (MARL) algorithm to improve the network energy efficiency, which considers the link quality, energy consumption and packet collisions between packets. Secondly, a reward mechanism is proposed, which is used to define independent state and action for every node to improve the adaptability of the proposed algorithm to dynamic underground environment. Furthermore, through the simulations in different underground environment and at different network scales, our results highlight that the proposed MARL algorithm can quickly optimize the network energy efficiency and far exceed the traditional adaptive data rate (ADR) mechanism. Finally, our proposed algorithm is successfully demonstrated to be able to efficiently adapt to the dynamically changing underground environment. This work provides insights into the energy efficiency optimization, and will lay the foundation for future realistic deployments of LoRaWAN-based WUSNs.

Battery Lifetime Extension in a Stand-Alone Microgrid With Flexible Power Point Tracking of Photovoltaic System

In stand-alone dc microgrids (dcMGs), battery energy storage systems (BESSs) are conventionally used for regulating the dc-link voltage, causing a continuous battery operation. Though operating the photovoltaic (PV) system at its maximum power point (MPP) yields minimum battery discharge current, the opposite is true for battery charging current. Therefore, reducing the battery charging current based on its state-of-charge (SoC) and the amount of available PV surplus power (which can be treated as virtually stored energy) is an opportunity for improving the battery life. The main objective of the control strategy proposed in this article is to prolong the battery lifetime by reducing the charging current and keeping the battery SoC at lower values if the PV power is enough to supply the loads. Additionally, the PV system is used as the primary asset to regulate the microgrid voltage. The dynamic performance of the proposed control strategy is validated with experimental tests under various operating conditions. Furthermore, its effectiveness in prolonging the battery lifetime is evaluated using an aging model of a lithium-ion (Li-ion) battery (without loss of generality) by simulated case studies.

Maximum Power Point Tracking for Solar Photovoltaic System Based on Interval Type-3 Fuzzy Logic: Practical Validation

Maximum power point tracking (MPPT) is a technique that allows photovoltaic (PV) arrays to operate at or near their maximum power point (MPP) under all weather conditions. Fuzzy logic controller (FLC) is the most popular method to monitor the MPPT, such as Type-1 fuzzy logic (T1FL), Type-2 fuzzy logic (T2FL), and Type-3 fuzzy logic (T3FL) controllers. This article proposes a new T3FL algorithm that improves tracking efficiency and tracking speed because it is better equipped to deal with uncertainty arising from disturbances. The proposed system consists of a PV module, a DC-DC buck converter controlled by the MPPT algorithm, a resistive load, and a battery. The PV solar panel is directly connected to the DC-DC buck converter, which operates based on the proposed controller’s output pulse width to ensure that the PV system works at MPP. The T3FL algorithm has the advantage of extracting the maximum power from the PV panel while preventing battery damage caused by variable MPPT voltage and thus extending battery lifetime. The maximum power and voltage of T3FL are compared with the existing T1FL and T2FL of battery and ohmic load. The MPPT performance of the proposed different algorithms was applied to a PV module and a DC-DC buck converter to be evaluated via simulation studies and experimental studies. MATLAB/Simulink is used to implement different algorithms. Several weather conditions are simulated: (i) uniform irradiation, (ii) sudden changing, and (iii) partial shading. A real-time implementation of the three MPPT algorithms by using Arduino Uno and spatial Simulink package known as the “support package for Arduino hardware”. Finally, the simulation and experimental results demonstrate that the T3FL controller of an ohmic load provides accurate maximum power, high levels of stability, and robust performance against uncertainties arising from disturbances to the PV system’s inputs

Crosstalk Reactions Induce Positive- and Negative-Electrode Resistance Increments in Li[Li1/3Ti5/3]O4/LiNi1/2Mn3/2O4 Cells

With the rapid utilization of lithium-ion batteries (LIBs) in various applications, including automobiles, battery-lifetime extension has become an important issue. A major cause of capacity fading in batteries is the increase in the positive- and negative-electrode resistance, mainly due to the formation of highly resistive electrode films. Previous studies indicate that crosstalk reactions significantly influence the resistance-increase mechanism. Although previous publications confirm that the rate of positive-electrode resistance-increase is greatly affected by the negative-electrode potential, the quantitative relationship between the crosstalk reaction and cell-resistance increment remains unexplained. In this study, potentiostatic charge tests were performed on Li[Li1/3Ti5/3]O4/LiNi1/2Mn3/2O4 cells by varying the charging voltage. Although the number of side reactions increased on increasing the charging voltage, the increase in cell resistance remained almost same; the Li[Li1/3Ti5/3]O4 and LiNi1/2Mn3/2O4 electrode resistances increased significantly after potentiostatic charging. To explain the resistance-increase behavior of the cell, a phenomenological model incorporating the crosstalk reaction was proposed. Subsequently, this model was used to calculate the increase in cell resistance. The simulation results were consistent with the experimental results, indicating that the model could be used to quantitatively predict the increase in cell resistance.

Predictive Cruise Controller for Electric Vehicle to Save Energy and Extend Battery Lifetime

Electric vehicles are considered the most effective solution to the petroleum crisis and reduction of air pollution. In order to enhance energy efficiency and battery lifetime, this paper designs a predictive cruise controller (EC) for electric vehicles. Road information and traffic preview are employed for velocity planning while minimizing energy usage, maintaining battery health, and avoiding collision with a lead vehicle. To enable real-time implementation, we apply a model predictive control (MPC) framework formulated in space domain, and approximation and relaxation are introduced to obtain a smooth nonlinear program. Simulation results indicate that the proposed controller yields suboptimal performance as compared to the globally optimal solution. For higher practicability on real-life scenarios, we develop an enhanced EC that is capable of optimizing the stopping of the ego vehicle. According to the car-following studies where the lead vehicle is driven using real-life data, the enhanced EC achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$7.14 \%$</tex-math></inline-formula> energy saving and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$29.81 \%$</tex-math></inline-formula> battery life extension when compared to the intelligent driving model. The computation time of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$40 \,\mathrm{ms}$</tex-math></inline-formula> per MPC update also demonstrates its potential for real-time applications.

Impact of the electrode potential of negative electrode on the increase in resistance of LiNi1/2Mn3/2O4 positive electrode

An increase in the resistance of electrode materials due to the formation of electrode/electrolyte interphases (EEI) induces capacity loss and power fading in lithium-ion batteries, and hence, is a significant problem that hinders the extension of battery lifetime. Previous studies have reported that the increase in the resistance of one electrode is affected by the other electrode, suggesting that the crosstalk reaction plays a crucial role in the increase in the resistance of the electrodes. In this study, to understand the resistance increase mechanism in relation to the crosstalk reaction, lithium-ion cells with a LiNi1/2Mn3/2O4 (LNMO) electrode with various materials (Li metal, graphite, Li[Li1/3Ti5/3]O4, and LiFePO4) as a negative electrode were examined via potentiostatic charge tests. The rate of increase in resistance of the LNMO electrodes against capacity due to side reactions depends on the electrode potential of the negative electrode: a lower electrode potential of the negative electrode slows down the rate of increase in resistance of the LNMO electrodes. Based on these results, an explanation for the resistance increase was provided by taking into consideration the crosstalk reaction. The crosstalk reaction reduces the amount of oxidative decomposition products generated at the LNMO electrode by consuming these products at the negative electrode; i.e., the resistance increase by EEI formation is minimized. A cell design based on the proposed mechanism, wherein the lower electrode potential of the negative electrode suppresses the increase in resistance of the positive electrodes, will open up the possibility of batteries with a long life.

Performance Assessment of Reinforcement Learning Policies for Battery Lifetime Extension in Mobile Multi-RAT LPWAN Scenarios

Considering the dynamically changing nature of the radio propagation environment, the envisioned battery lifetime of the end device (ED) for massive machine-type communication (mMTC) stands for a critical challenge. As the selected radio technology bounds the battery lifetime, the possibility of choosing among several low-power wide-area network (LPWAN) technologies integrated at a single ED may dramatically improve its lifetime. In this article, we propose a novel approach of battery lifetime extension utilizing reinforcement learning (RL) policies. Notably, the system assesses the radio environment conditions and assigns the appropriate rewards to minimize the overall power consumption and increase reliability. To this aim, we carry out extensive propagation and power measurements campaigns at the city-scale level and then utilize these results for composing real-life use cases for static and mobile deployments. Our numerical results show that RL-based techniques allow for a noticeable increase in EDs’ battery lifetime when operating in the multi-RAT mode. Furthermore, out of all considered schemes, the performance of the weighted average policy shows the most consistent results for both considered deployments. Specifically, all RL policies can achieve 90% of their maximum gain during the initialization phase for the stationary EDs while utilizing less than 50 messages. Considering the mobile deployment, the improvements in battery lifetime could reach 200%.

Battery lifetime extension through optimal design and control of traction and heating systems in hybrid drivetrains

In this paper, we investigate the optimal design and control of an integrated energy and thermal management system (IETMS) of a battery-assisted trolley bus that is subject to minimum battery lifetime requirements. Therefore, we jointly optimize both the control trajectories of the traction and the heating systems and the design of the thermal energy buffer. We further analyze the resulting Pareto fronts which characterize the trade-off between the battery lifetime and the energy consumption. This holistic approach fills a gap in the literature published, namely the formulation of an optimization problem that combines component sizing with a battery-health-aware IETMS. While the model derived allows the formulation of a convex optimal control problem for a given vehicle design, the combined design and control problem is non-convex. Therefore, we perform grid searches within a reasonable subset of the design space and show that the problem is smooth, has only one stationary point, and that the solver converges to the optimal solution even for the simultaneous, non-convex problem formulation. We further present a case study showing that, if an IETMS is used, a realistic bus service life without battery replacement can be achieved with a reduction of energy consumption of up to 7% on some driving missions, compared to a heuristic heating strategy. If the design of the thermal system is co-optimized, battery lifetime can be extended further by up to 15% without affecting the amount of energy consumed. In summary, our study reveals a potential to make electric transportation more efficient in terms of both energy and costs based on a holistic consideration of battery-health-aware IETMS with optimized component dimensioning.