Techniques to Increase EV Battery Life

Battery temperature adjustment system for electric vehicles to prevent battery deterioration. The system has a battery, cooling device and control system. When the vehicle is connected to an external power source, the control system selects either the battery or external power to cool the battery based on a deterioration sensitivity map. If cooling with external power would cause more deterioration than using battery power, it cools with battery power.

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Battery charging method to improve battery life by reducing expansion force during charging. The method involves adjusting the charge rate when the battery's state of charge (SOC) reaches a certain range where expansion force is maximized. The charge rate is lowered when SOC is close to the range to reduce expansion force and prolong battery life. When SOC exceeds the range, the charge rate is raised to ensure efficiency. This optimizes charging near the expansion limit to extend battery cycle life.

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Electrodes for energy storage systems with improved performance and cycle life. The electrodes are made of materials like silicon, carbon-sulfur, lithium or graphene-silicon composites, coated with parylene. The parylene coating acts as a barrier to prevent contact between the electrode and the electrolyte. This reduces capacity fade and degradation from reactions between the electrode and electrolyte. The parylene coating also contains polysulfides in lithium-sulfur batteries to improve cycle life.

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Optimizing the operating lifetime of an energy storage system like a vehicle battery pack by monitoring parameters like temperature and voltage that indicate cell degradation. When a parameter approaches a threshold indicating end-of-life, the system heats the battery pack to extend its performance and lifetime.

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Apparatus for diagnosing the state of a vehicle battery and presenting measures to suppress battery degradation. The apparatus analyzes the battery usage history and presents suitable suppression measures for factors causing degradation. If an alternative measure doesn't meet certain criteria, it is prohibited from being presented. This prevents presenting ineffective measures that could restrict vehicle use without benefit. By selectively presenting only suitable measures, battery degradation can be suppressed without reducing the vehicle's value.

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The accurate prediction of remaining useful life (RUL) is crucial in order to reasonably and efficiently utilize lithium-ion batteries (LiBs). In this paper, a construction method periodic time series applied the degradation data LiBs address issues insufficient training smooth RUL interval based on trend filtering segmentation fuzzy information granulation. for used form new dataset from original data, which fusion model, by combining variational mode decomposition (VMD) gated recurrent unit (GRU), as model LiBs. Moreover, effectiveness advantage proposed paper was verified analyzed utilizing CALCE battery NCA dataset.

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Accurate estimation of the state health (SOH) and remaining useful life (RUL) lithium-ion batteries (LiBs) is critical for ensuring battery reliability safety in applications such as electric vehicles energy storage systems. However, existing methods developed estimating SOH RUL LiBs often rely on full-cycle charging data, which are difficult to obtain engineering practice. To bridge this gap, paper proposes a novel data-driven method estimate only using partial curve features. Key features extracted from constant voltage (CV) process relaxation, validated through Pearson correlation analysis SHapley Additive exPlanations (SHAP) interpretability. A hybrid framework combining CatBoost particle swarm optimization-support vector regression (PSO-SVR) developed. Experimental validation public datasets demonstrates superior performance methodology described above, with an root mean square error (RMSE) absolute (MAE) below 1.42% 0.52% relative (RE) under 1.87%. The proposed also exhibits robustness computational efficiency, making it suitable management systems (BMSs) LiBs.

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Enhanced Battery Management Systems (BMS) are essential for improving operational efficacy and safety within Electric Vehicles (EVs), especially in tropical climates where traditional systems encounter considerable performance constraints. This research introduces a novel two-tiered deep learning framework that utilizes two-stage Long Short-Term Memory (LSTM) precise prediction of battery voltage SoC. The first tier employs LSTM-1 forecasts individual cell voltages across full-scale 120-cell Lithium Iron Phosphate (LFP) pack using multivariate time-series data, including history, vehicle speed, current, temperature, load metrics, derived from dynamometer testing. Experiments simulate real-world urban driving, with speeds 6 km/h to 40 variations 0, 10, 20%. second uses LSTM-2 SoC estimation, designed handle temperature-dependent fluctuations high-temperature environments. cascade design allows the system capture complex temporal inter-cell dependencies, making it effective under variable-load Empirical validation demonstrates 15% improvement estimation accuracy over methods driving co... Read More

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Lithium-ion batteries are an indispensable component of numerous contemporary applications, such as electric vehicles and renewable energy systems. However, accurately predicting their remaining service life is a significant challenge due to the complexity degradation patterns time series data. To tackle these challenges, this study introduces novel Multi-Scale Time Attention (MSTA) mechanism designed enhance modeling both short-term fluctuations long-term trends in battery performance. This integrated with Bidirectional Gated Recurrent Unit (BiGRU) develop BiGRU-MSTA framework. framework effectively captures multi-scale temporal features enhances prediction accuracy, even limited training The model evaluated via two sets experiments. First, using NASA lithium-ion dataset, experimental results demonstrate that proposed outperforms LSTM, BiGRU, CNN-LSTM, BiGRU-Attention models across all evaluation metrics. Second, experiments conducted on CALCE dataset not only examine impact varying scales within MSTA but also compare against state-of-the-art architectures Transformer LSTMTransfo... Read More

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Lithium-ion batteries experience nonlinear degradation characteristics during long-term operation. Accurate estimation of their remaining useful life (RUL) is significant importance for early fault diagnosis and residual value evaluation. However, existing RUL prediction approaches often suffer from limited accuracy insufficient specificity. To address these limitations, this study proposes an methodology based on Gaussian process regression, which incorporates pattern recognition auxiliary features derived knee points. First, 9 health-related are extracted the first 100 charge/discharge cycles battery. Based features, clustering classification techniques employed to categorize into three distinct patterns. Moreover, feature assessed identify eliminate redundant indicators, thereby enhancing relevance set prediction. Subsequently, each pattern, GPR-based models with composite kernel functions constructed by integrating point positions corresponding slopes. The model hyperparameters further optimized through particle swarm optimization (PSO) algorithm improve adaptability generalizati... Read More

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Preventing output limit of a battery in a vehicle by selectively raising the battery temperature based on the state of charge (SOC) and temperature during driving and restart. A method of preventing output limit of a battery of a vehicle includes monitoring the battery SOC and temperature while driving, determining the likelihood of output limit based on a map, and selectively increasing the battery temperature if output limit is likely. This prevents output limit when the SOC decreases in cold temperatures.

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Thermal stress mitigation system for refuse vehicles to prevent damage to energy storage and generation devices due to thermal loading, cycling, and events. The system uses a thermal stress mitigation substance deployed by nozzles on the vehicle body. Sensors detect thermal stress on the devices and a controller decides when to deploy the substance based on threshold exceedance. This provides localized cooling to mitigate thermal stress on the devices during operation.

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Thermal management system for electric vehicle batteries that allows individual cooling or heating of different zones within the battery to optimize performance and lifespan. The system uses multiple distinct circuits, each associated with a cooling zone, with independent flow control valves. A controller ranks the zones by temperature and adjusts the valves to balance cooling and heating based on the hottest and coldest zones. This provides customized cooling/heating to prevent hot spots and improve overall battery temperature management.

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A secondary battery with improved thermal management to prevent overheating and extend battery life. The battery has a unique sheath design with a thick metal layer making up 50-70% of the total sheath thickness. The metal layer provides high thermal conductivity to efficiently dissipate heat generated during charging and discharging. This reduces the internal battery temperature without adding bulk. The remaining sheath thickness is non-metal insulation layers.

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Battery array design for electric vehicle packs that integrates roll-bonded cold plates into the battery array itself rather than using separate external cold plates. The roll-bonded cold plates are formed by joining two metal sheets along a bonded seam. This allows the cold plates to be integrated into the battery array support structure to directly manage the thermal performance of the battery cells. It reduces cost and complexity compared to separate cold plates. The roll-bonded plates can form one or more sides of the array structure.

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A detection system to monitor battery pack heat insulation performance in electric vehicles. The system derives a heat insulation performance value based on battery temperature changes and environmental temperatures during vehicle stops. If the derived value falls below a reference, it indicates decreased insulation. This is detected and an output provided to alert the user that insulation is deteriorating.

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This review examines recent advancements in lithium-ion battery (LIB) technology for extreme conditions, focusing on applications electric vehicles, renewable energy, defense, and remote sensing. We explore innovative electrode electrolyte designs that enhance performance at temperatures, addressing challenges like freezing increased impedance. The highlights the development of novel electrolytes, including high-entropy formulations, fast-charging electrodes. Emphasizing sustainability resilience, we aim to inspire near-future research LIBs capable meeting demands operational scenarios, space exploration. analysis provides insights perspectives improving LIB reliability across challenging applications.

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<title>Abstract</title> Li-Ion batteries have become essential for space missions as secondary powersources due to their high performance and power density over the past twodecades. However, variations in cells characteristics of a battery pack can leadto reduced overall capacity time. To address this issue, various balancingmethods been developed extend life. This article presentsa space-qualified passive balancer that utilizes commercial off-the-shelf (COTS) components. Its design is particularly suited applications,emphasizing low consumption, efficiency, tolerating harsh spaceradiation environment. A incorporating proposed cellbalancer has successfully deployed microsatellite recently launched intolow Earth orbit (LEO). The satellites housekeeping data demonstrate effectivenessof balancer, maintaining cell voltages within specified limits up toone year after launch. Notably, suggested reduce state ofcharge (SOC) by 1% per orbit, contributing enhanced longevity.

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LiFePO4 batteries need a battery management system (BMS) to improve performance, extend their lifespan, and maintain safety by utilizing advanced monitoring, control, optimization techniques. This paper presents the design, development, implementation of an intelligent (i-BMS) that integrates real-time monitoring control batteries. The was extensively tested using multiple datasets, results show able temperature within set range, balance cell voltages, distribute energy according load prioritization. It uses fuzzy logic approach effectively manage farm requirements. Additionally, proposed method embedded three-level prioritization algorithm woven into rule allocate dynamically among essential, regular, non-essential loads.

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With the widespread adoption of electric vehicles (EVs), optimizing their charging and discharging strategies to improve energy efficiency extend battery life has become a focal point current research. Traditional often fail adequately consider batterys state health (SOH), resulting in accelerated aging decreased efficiency. In response, this paper proposes safe reinforcement learningbased optimization method for EV strategies, aimed at minimizing costs while accounting SOH. First, novel status prediction model based on physics-informed hybrid neural networks (PHNN) is designed. Then, decision-making problem, considering status, formulated as constrained Markov decision process, an interior-point policy (IPO) algorithm long short-term memory (LSTM) proposed solve it. The filters out that violate constraints by introducing logarithmic barrier function. Finally, experimental results demonstrate significantly enhances maintaining maximum economic benefits during process. This research provides solution intelligent personalized EVs, which great significance promoting sustainable de... Read More

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