Lithium-ion Battery Pack Research Articles

A Model-Based Research on Performance Evaluation and Topology Optimization of Series-Parallel Lithium-Ion Battery Packs

A Model-Based Research on Performance Evaluation and Topology Optimization of Series-Parallel Lithium-Ion Battery Packs

Multi-objective energy management strategy for a dual-stack fuel cell heavy-duty truck via deep reinforcement learning

The Proton Exchange Membrane Fuel Cell (PEMFC) system, as an efficient, zero-pollution emission, and high-efficiency energy conversion device, is particularly suitable for heavy-duty transportation. Considering the limited power of the current PEMFC system, this study focuses on a hybrid fuel cell heavy-duty truck with dual stacks. Considering the optimal operating range of the PEMFC system and the health status of the lithium-ion battery pack, an energy management strategy (EMS) based on the Deep Deterministic Policy Gradient (DDPG) algorithm is established. This strategy optimizes the hydrogen consumption cost, degradation costs of lithium-ion battery and fuel cells. Additionally, a dynamic programming (DP) and an equivalent consumption minimization strategy (ECMS) based EMSs are established. The power allocation and overall transportation costs under the CSC driving cycle are compared and verified for the three EMSs. The results show that the DDPG algorithm provides a more optimal power allocation for the dual-stack fuel cells, improving the overall system efficiency, enhancing the lifecycle of heavy-duty trucks, and significantly reducing overall travel costs.

Study on Thermal Runaway Behavior and Early Warning Algorithm of Ternary Lithium Battery Pack Under Preload Force

Overcharging is a primary cause of thermal runaway in ternary lithium‐ion batteries, often leading to serious safety incidents. Early detection of thermal runaway during overcharging is therefore critical. This study investigates a 5 Ah ternary lithium battery pack, applying appropriate preload force to simulate real‐world conditions. Various overcharge experiments are conducted under different conditions, and changes in battery voltage, temperature, and expansion force are thoroughly analyzed. The results indicate that under the same initial conditions, higher charging rates accelerate the temperature rise in the lithium battery. Additionally, the internal gas generation rate increases, causing a faster rise in edge pressure and leading to earlier battery cracking. Building on these findings, a three‐level early warning algorithm is developed, which comprehensively considers voltage, temperature, and expansion force changes. Experimental validation demonstrates that this algorithm can accurately identify the current stage of thermal runaway and detect the transition to the third warning stage 604 s before complete failure, thus providing critical protection for the safe operation of the battery pack. This study offers valuable guidance for enhancing the monitoring and early warning capabilities of battery management systems.

Multi-fault diagnosis of lithium battery packs based on comprehensive analysis of locally weighted Manhattan distance and voltage ratio

The diagnosis of faults in lithium-ion battery packs is pivotal to ensuring the operational safety of electric vehicles. A fault diagnosis method is introduced to address the lack of a discharge phase and the existence of a single fault type in traditional diagnostic methods. This method relies on a comprehensive assessment involving locally weighted Manhattan distance and voltage ratio anomalies. The KD-Tree-MLLE dimensionality reduction technique is utilized for fast data processing, which improves the computational efficiency by 1.3 % compared to the original MLLE algorithm. The fault detection method utilizing locally weighted Manhattan distance can meet the same threshold evaluation criteria as the charging phase. This approach resolves the constraint of a single phase and accomplishes comprehensive fault detection. The precision and efficacy of the methodology are confirmed through F1-score evaluation. Accuracy and recall rates are compared under varying thresholds during the charging and discharging phases to determine the optimal threshold value. The ratio between the voltage data of the defective battery and the average voltage data of the normal batteries is calculated, and the comprehensive anomaly analysis method based on the voltage ratio and temperature is proposed. The use of comprehensive data such as voltage ratio and temperature to judge the type of fault can simultaneously achieve the judgment of a single fault and mixed fault types. The applicability of locally weighted Manhattan distance under dynamic circumstances and the efficacy of the voltage ratio analysis method across charging and discharging phases are confirmed through the establishment of an experimental and simulation platform dedicated to lithium-ion batteries. Findings indicate that all faults in both phases can be accurately located when the threshold is set to 0.17. This affirms the superior accuracy of the proposed method in detecting and localizing individual faults and combinations of faults quickly and reliably.

Data-driven state-of-charge estimation of a lithium-ion battery pack in electric vehicles based on real-world driving data

State-of-charge (SOC) is one of the most important indicators in the battery management system (BMS) of electric vehicles (EVs). Accurate online estimation of battery pack SOC is essential for ensuring safety by preventing overcharging and overdischarging, as well as for facilitating proper operation and efficient energy utilization. This paper proposes a deep learning model, CNN-BiLSTM-Attention, to drive the battery pack SOC estimation by datasets collected from actual operating EVs. Multivariate time series (MVTS) signals such as vehicle speed, battery voltage, current and temperature are carefully selected as inputs to the model. Additionally, a three-parameter Weibull probability model is used to model the terminal voltage distribution of all cells within the investigated battery pack, and the evolution of cell terminal voltage inconsistency concerning depth of discharge (DOD) is described from the perspective of dispersion and symmetry. By adding these distribution features to the input dimension of the CNN-BiLSTM-Attention model, the estimation accuracy of battery pack SOC has been further improved. Experimental results indicate that when the window length (WL) is set to 25 sampling time steps and the window overlap rate is >72 %, the SOC estimation error is <3 % and the root mean square error (RMSE) is <1.12 % within the maximum DOD variation range experienced by the battery pack. Especially when the battery pack is in the discharge platform period of 30 %–100 % SOC, the SOC estimation error is reduced to <2 %. After adding the Weibull distribution features that reflect the cell inconsistency evolution into the model's input dimensions, the overall SOC estimation RMSE is reduced by about 31 %, especially by about 51 % at lower SOC levels (below 31 %).

Impact of Data Corruption and Operating Temperature on Performance of Model-Based SoC Estimation

Electric vehicles (EVs) are becoming popular around the world. Making a lithium battery (LIB) pack with a robust battery management system (BMS) for an EV to operate under different complex environments is both a challenge and a requirement for engineers. A BMS can intelligently manage LIB systems by estimating the battery state of charge (SoC). Due to the nonlinear characteristics of LIB, influenced by factors such as the harsh environment and data corruption caused by electromagnetic interference (EMI) inside electric vehicles, SoC estimation should consider available capacity, model parameters, operating temperature and reductions in data sampling time. The widely used model-based algorithms, such as the extended Kalman filter (EKF) have limitations. Therefore, a detailed review of the balance between temperature, data sampling time, and different model-based algorithms is necessary. Firstly, a state of charge—open-circuit voltage (SoC-OCV) curve of LIB is obtained by the polynomial curve fitting (PCF) method. Secondly, a first-order RC (1-RC) equivalent circuit model (ECM) is applied to identify the battery parameters using a forgetting factor-based recursive least squares algorithm (FF-RLS), ensuring accurate internal battery parameters for the next step of SoC estimation. Thirdly, different model-based algorithms are utilized to estimate the SoC of LIB under various operating temperatures and data sampling times. Finally, the experimental data by dynamic stress test (DST) is collected at temperatures of 10 °C, 25 °C, and 40 °C, respectively, to verify and analyze the impact of operating temperature and data sampling time to provide a practical reference for the SoC estimation.

A novel pulse liquid immersion cooling strategy for Lithium-ion battery pack

Ensuring the lithium-ion batteries’ safety and performance poses a major challenge for electric vehicles. To address this challenge, a liquid immersion battery thermal management system utilizing a novel multi-inlet collaborative pulse control strategy is developed. Moreover, different cooling methods (cooling structures, immersion coolants and pulse control method) are numerically investigated to assess their impact. Compared with other structural schemes, the battery module using baffles with fish-like perforations for the 5-in and 5-out scheme demonstrates superior cooling capability while reducing external power consumption. Furthermore, the utilization of FC-3283 exhibits better comprehensive performance compared to AC-100 and mineral oil. Notably, in terms of pulse cooling, when the output ratio reaches 50 %, the battery pack temperature can not only be maintained within a reasonable range, but also the time-averaged pump power consumption decreases by 87.6 % compared to the bottom inlet and top outlet scheme. At the same average flow rate, the liquid immersion battery thermal management system with output ratio of 25 % is the optimal choice for the trade-off between cooling performance and flow resistance, and compared with the bottom inlet and top outlet scheme, the maximum temperature and maximum temperature difference decrease by 23.7 % and 13.9 %, respectively.

Thermal management for the prismatic lithium-ion battery pack by immersion cooling with Fluorinated liquid

This study constructs a novel FS49-based battery thermal management system (BTMS), proposing an optimization method for the system energy density and an indirect control method for the system cooling capacity. The boiling of dielectric refrigerant occurred at the battery surface, which provided strong and uniform cooling for each battery cell. The results show that the peak temperature difference of liquid immersion cooling (LIC) module during 1C rate discharging and charging was reduced by 91.3% and 94.44%, respectively, compared to the natural convection (NC) module. The reduction of temperature nonuniformity greatly reduced the state of charge (SOC) inhomogeneity of different cells within the module. Moreover, the energy density of LIC module can be optimized by reducing the cell spacing and liquid filling ratio. Comparing with 100% filling rate, the module maximum temperature corresponding to 25% filling rate (with wick) during 2C rate discharging is only increased by 1.6℃, however, the module peak temperature difference is reduced by 53.3%, and the energy density is increased by 13.14%. In addition, Equivalent circuit model (ECM) and volume of fluid (VOF) model were used to simulate the LIC module, and the numerical results are in good agreement with the experimental data. This study provides a systematic design plan and numerical method for the engineering application of LIC in the field of BTMS.

Model-free detection and quantitative assessment of micro short circuits in lithium-ion battery packs based on incremental capacity and unsupervised clustering

Timely diagnosis of micro short circuit (MSC) faults is crucial for ensuring the safe operation of lithium-ion battery energy storage systems. Existing diagnostic methods face limitations such as high dependency on battery models, difficulty in determining accurate diagnostic thresholds, or low computational efficiency. This work presents a model-free approach for the detection and quantitative assessment of MSCs in lithium-ion battery packs, with incremental capacity (IC) and unsupervised clustering. First, the IC is extracted from charging voltage data to effectively characterize MSC faults in lithium-ion batteries. Next, principal component analysis is used to map the high-dimensional feature space onto a two-dimensional plane to facilitate fault detection and result visualization. Then, an unsupervised clustering algorithm is employed for anomaly detection to identify MSC cells within the battery pack. For the detected MSC cells, a method based on the maximum charging voltage difference between adjacent cycles is designed to estimate the MSC resistance, quantitatively assessing the severity and evolution stage of the MSC. Experimental results show that the accuracy of MSC detection is 99.17 % and the minimum relative error of short-circuit resistance estimation is 1.20 %, which demonstrates the effectiveness and feasibility of the proposed method.

Design of alveolar biomimetic enhanced heat transfer structure for cylindrical lithium battery pack

Aiming to address the issue of thermal runaway in high energy density batteries during high charge and discharge rates, a heat management system for an alveolar-like honeycomb liquid-cooled power battery was designed. This system is inspired by the bionic concept of cell cooling in biological tissues, with cylindrical batteries serving as cells and cooling channels resembling blood vessels. The cooling system for a battery pack consisting of 24 cylindrical cells was designed and implemented. Numerical simulations were conducted to optimize and study the effects of inlet diameter, branch angle, flow rate, and spiral baffle on coolant flow uniformity and temperature distribution within the battery pack. The results indicate that as the inlet diameter of the fractal channel increases from 2 mm to 20 mm, the coolant flow uniformity improves and the pressure drop decreases. However, the influence of inlet diameter diminishes after reaching a certain limit. When the inlet diameter increases from 10 mm to 20 mm, the relative standard deviation of the velocity at the branch outlet decreases only slightly, from 0.9 % to 0.8 %. Increasing the branch angle from 30° to 75° leads to an increase in the relative standard deviation of velocity, from 0.79 % to 1.24 %. Incorporating a spiral baffle into the internal flow channel of honeycomb cooling significantly enhances temperature uniformity within the battery pack while reducing maximum temperature levels considerably. Furthermore, a smaller pitch for the spiral baffle yields better cooling performance for the immersed liquid cooling thermal management system. After optimization, the maximum temperature difference is only 1.27 °C across the surface of the battery pack, achieving a remarkable temperature uniformity level of just 0.11 %.

An experimental study on lithium-ion electric vehicles battery packs behavior under extreme conditions for prevention of thermal runaway

The need for efficient and dependable lithium-ion battery packs has significantly increased as a result of the progressively rising sales of electric vehicles (EVs). Thermal management is one of the key factors in battery performance and durability. To avoid thermal deterioration, improve safety, and maximize system effectiveness, the battery pack's temperature must be carefully managed. These battery systems' potential for thermal runaway has raised concerns about how safely they can be operated in harsh environments. Environmental considerations, governmental laws, and developments in battery technology are driving the switch from internal combustion engines to electric automobiles. Lithium-ion batteries are sensitive to temperature variations and operating them outside the optimal temperature range can lead to accelerated degradation, reduced capacity, and compromised safety. Key performance indicators used to assess battery thermal management system effectiveness include temperature uniformity, cooling effectiveness, energy usage, and effect on battery life. This paper describes an experimental investigation that looked at how lithium-ion EV battery packs behaved in harsh environments. It also suggests a unique strategy to prevent thermal runaway by using materials like Transformer Oil (TO) and Phase Change Materials (PCM). A specially built experimental setup was created to undertake this inquiry in order to imitate several extreme situations, including high ambient temperatures. A lithium-ion (NMC) battery pack (7S3P) was put through the experimental phase's predicted harsh circumstances to see how it would react thermally. In order to obtain insight into the underlying mechanisms causing thermal runaway, the data acquired were evaluated, and crucial thermal metrics like temperature distribution, heat dissipation, and thermal gradients were studied. The experiment's findings showed that under extreme circumstances, conventional cooling techniques were ineffective in preventing thermal runaway, which resulted in serious safety risks and a reduction in battery performance. The suggested strategy, which incorporates PCM and TO, was then put into practice to fix these flaws and improve battery safety. Due to their ability to self-regulate, they served as components that prevented thermal runaway by limiting the rise in temperature under high-stress situations.

Kinetics study on inhibiting battery thermal runaway using an inorganic phase change material with a super high thermochemical storage capacity

Lithium-ion batteries are susceptible to fires and explosions due to thermal runaway, a serious safety hazard. This study explores the potential of using hydrated inorganic salt (TCM40) composite phase change materials to prevent thermal runaway in battery packs. TCM40 composites stand out due to their exceptional thermochemical heat storage capacity, which allows them to effectively absorb excess heat during runaway events. The research investigates how thermal conductivity, thermal storage capacity, and cell spacing influence the propagation of thermal runaway. The findings demonstrate that TCM40 composites, with a thermal storage density exceeding 1000 kJ/kg, are significantly more effective in preventing thermal runaway compared to traditional latent heat storage phase change materials with lower capacities. To gain a comprehensive understanding of thermal runaway mitigation, a combined thermal management model was developed. This model integrates a battery thermal runaway model with a kinetic model describing the decomposition of TCM40 composites. The analysis reveals that the high heat absorption capability of TCM40 composites minimizes heat transfer to neighboring cells during thermal runaway. Furthermore, the model provides valuable insights into the synergistic effects of thermal conductivity and heat storage capacity on runaway propagation. This knowledge can be directly applied to design safer battery packs, even for compact configurations where cell spacing is less than 2 mm. This study offers significant advancements in both thermal protection materials and design strategies for lithium-ion battery packs. These advancements have the potential to significantly improve battery system safety and minimize the risk of explosions.

Investigating the impact of battery arrangements on thermal management performance of lithium-ion battery pack design

The working temperature is one of the key factors affecting the efficiency and safety performance of automotive power batteries. Current battery pack design primarily focuses on single layout configurations, overlooking the potential impact of mixed arrangements on thermal management performance. This study presents a module-based optimization methodology for comprehensive concept design of Lithium-ion (Li-ion) battery pack. Firstly, the arrangement modules is optimized and performed using particle swarm optimization algorithms considering various arrangement layout (i.e. rectangular, diamond, and staggered arrangements) by taking the intercell spacing and maximum temperature of the modules as design objectives. Secondly, the battery pack configuration design is performed employing a neural network model reflect diverse battery module configurations within the pack, exploring their impact on thermal management performance. The hybrid battery arrangement effectively improves thermal management, and the module spacing helps to enhance heat dissipation. The staggered arrangement has a greater impact on the heat dissipation performance of the battery pack, but the spacing between different modules varies with the position of the modules. When all configuration schemes are staggered modules, the optimal range of the spacing between modules is between 6 and 7 mm. However, the study observes a non-linear relationship between module spacing and the maximum temperature difference within the battery pack. While increasing module spacing initially decreases temperature differences, it eventually reverses, suggesting that spacing alone may not consistently enhance thermal management. Validation with a lithium-ion battery pack case study demonstrates the method’s effectiveness, providing valuable knowledge for future cell and pack designs that employ different battery cell arrangements and diverse cooling strategies.

Hierarchical equalization scheme for retired lithium-ion battery packs based on inductor-flyback transformer

Some lithium-ion batteries can be reused after sorting and recombination. Equalization of these batteries during the process of step utilization is an effective way to improve the inconsistency of battery packs and extend their lifespan. This paper proposes a hierarchical equalization topology based on a combination of inductor and transformer, which is divided into intra-group and inter-group battery equalization. The intra-group equalization circuit achieves energy transfer between non-adjacent individual batteries through an improved Buck-Boost circuit. The inter-group equilibrium circuit exchanges energy between any battery subpackages and the entire battery pack using a flyback transformer. An LCD (Inductance-Capacitor-Diode) lossless absorption network is added to the transformer to absorb voltage spikes caused by leakage inductance while achieving soft switch to reduce switch losses. Considering that retired lithium-ion batteries can affect battery capacity and State of Charge (SOC) estimation accuracy due to aging issues, a modified Extended Kalman Filter (EKF) algorithm is proposed to improve battery SOC estimation accuracy. A fuzzy logic controller is introduced into the SOC-based control strategy to improve the equalization efficiency of the battery pack. Finally, to validate the effectiveness of the proposed equalization scheme, a model is built and simulated using MATLAB/Simulink software. The experimental results show that compared with the traditional double-layer inductor equalization scheme using the most value equalization algorithm and fuzzy logic control algorithm respectively, this equalization scheme has a faster equalization speed and higher equalization efficiency.

An Internal Resistance Consistency Detection Approach for Lithium-Ion Battery Pack Using Unbalanced Capacitor Current

An Internal Resistance Consistency Detection Approach for Lithium-Ion Battery Pack Using Unbalanced Capacitor Current

Experimental investigation on an innovative serpentine channel‐based nanofluid cooling technology for modular lithium‐ion battery thermal management

AbstractMany nations have committed to becoming carbon neutral by 2050 as a means of addressing the global warming challenge. To achieve carbon neutrality, transportation is one of the most essential and important tasks. Energy‐efficient pure electric vehicles (EVs) and hybrid electric vehicles (HEVs) with green energy power are being developed in response to the worldwide energy and environmental crises, as the potential replacements for the current generation of combustion‐engine automobiles. EVs require batteries more than ever before. In this perspective, lithium‐ion batteries (LIBs) stand out as remarkable energy storage technologies and have been widely used due to their numerous impressive benefits. Owing to LIBs sensitivity to temperature, EVs typically use the battery thermal management system (BTMS). The working temperature span of a lithium‐ion battery in an electric car is 15°C–35°C, which is achieved by the use of a BTMS. The production of internal heat during charging and discharging also affects how well lithium‐ion batteries work. A battery heat control system is therefore required. The temperature of the LIB pack might be efficiently controlled by liquid‐cooled systems in discharge and charge scenarios. Based on Al2O3 nanofluid (NF), the current experimental study suggests a novel active cooling technology for regulating the heat produced by the 18650‐format lithium‐ion batteries. A thorough analysis is conducted on the impact of charge/discharge C‐rates, Al2O3 nanoparticle (NP) volume fractions, inflow coolant velocity, and intake liquid temperature on the thermal efficiency of the LIB pack. By incorporating aluminum oxide NPs into the water at varying volume fractions of 0.3%, 0.5%, and 1%, the LIB pack's maximum temperature was significantly reduced by 7.9%, 18.09%, and 19.56%, respectively. With increase in mass flow rate of coolant from 0.0290 to 0.5810 kg/s, the maximum temperature has been substantially reduced by 3.7%–8.6%. Results show that using higher fluid inflow temperature significantly increased both the highest experienced temperature and temperature diversity throughout the discharge operation by about, 6°C and 5°C, respectively. The outcomes of the study indicate that NFs exhibit superior cooling performance compared to conventional coolants such as water and ethylene glycol.

Increasing the energy efficiency of an electric vehicle powered by hydrogen fuel cells

The article considers fuel cell electric vehicle energy efficiency improvement by means of traction battery remanent state of charge modern control algorithms application which based on fuzzy logic. Fuel cell vehicle traction system used as experimental setup for the study includes brushless dc motor, lithium-ion batteries pack and electronic differential approach implementation. The results of simulation modelling in MATLAB-Simulink software comparing conventional PID-controller based and fuzzy logic based controller for vehicle speed control systems are presented. Fuzzy logic controller provides an electric vehicle dynamic behavior tuning for different driving modes automatically transferring the control system to economy mode when it possible. However, the control system is able to provide fast dynamic behavior when it is required. The proposed control algorithm is tested by means of developed experimental setup. Experimental results validate conducted simulation modelling results and the proposed control algorithm. Application of the proposed fuzzy logic control algorithm to control the electric vehicle dynamic behavior allows energy efficiency improvement and driving range increase.

Meeting the challenge of mitigating Li-ion battery fires for aviation

The aviation industry faces a pressing challenge in reducing its environmental footprint, especially with the projected growth in global passenger traffic. Electrification and more-electric aircraft, particularly through Lithium-ion Battery (LiB) systems, offers a promising pathway to reduce emissions but introduces significant safety concerns, particularly regarding thermal runaway (TR) events. This prospective paper examines the unique challenges posed by aviation environments for developing safe LiB systems. It discusses multifaceted mitigation approaches, integrating fire containment structures with advanced thermal management and fire protection technologies. Various cooling technologies and fire suppression agents are explored for their effectiveness in extinguishing LiB fires and mitigating thermal runaway propagation. Integrated LiB suppression systems are proposed to combine fire containment, suppression, and thermal management functionalities for achieving the demanding specific energy density levels that will be required. Scaling safe LiB pack solutions for commercial aviation requires coordinated efforts among regulators, original equipment manufacturers (OEMs), engineers, and researchers to establish standardized design criteria, develop validated modeling tools, and establish rigorous certification testing requirements. In conclusion, addressing the safety concerns of large LiB packs in aircraft applications requires a holistic, integrated approach. This paper provides insights into current research, identifies key challenges, and outlines future directions for advancing LiB safety in aviation.

Novel AgO-based nanofluid for efficient thermal management of 21700-type lithium-ion battery

In this study, a novel thermal management system for a 21700-type lithium-ion battery pack is designed using AgO-based nanofluid and mesh plate to regulate maximum temperature and uniformity during fast discharging. The effects of discharge C-rate, AgO nanoparticle volume fractions, inflow coolant velocity, inlet/outlet hydraulic diameters, mesh plate shape, and flow direction on thermal efficiency at a high discharge rate (7C) are numerically analyzed. Results show the highest temperature and temperature difference decrease significantly with increased inflow velocity and nanofluid volume fraction. Using 4 %-VF AgO-based nanofluid reduced the maximum temperature and temperature difference by 3.08 % and 60.7 %, respectively, compared to DI-water. Increasing inflow velocity decreased the highest temperature and temperature difference by 0.64 K and 0.3 K, respectively. Different inlet/outlet hydraulic diameters and flow directions significantly affected both the highest temperature and temperature uniformity. Comparing different mesh plate shapes of mesh plates in the thermal management system suggests that the new cooling channel format is promising.

Machine learning modeling for fuel cell-battery hybrid power system dynamics in a Toyota Mirai 2 vehicle under various drive cycles

Electrification is considered essential for the decarbonization of mobility sector, and understanding and modeling the complex behavior of modern fuel cell-battery electric-electric hybrid power systems is challenging, especially for product development and diagnostics requiring quick turnaround and fast computation. In this study, a novel modeling approach is developed, utilizing supervised machine learning algorithms, to replicate the dynamic characteristics of the fuel cell-battery hybrid power system in a 2021 Toyota Mirai 2nd generation (Mirai 2) vehicle under various drive cycles. The entire data for this study is collected by instrumenting the Mirai vehicle with in-house data acquisition devices and tapping into the Mirai controller area network bus during chassis dynamometer tests. A multi-input - multi-output, feed-forward artificial neural network architecture is designed to predict not only the fuel cell attributes, such as average minimum cell voltage, coolant and cathode air outlet temperatures, but also the battery hybrid system attributes, including lithium-ion battery pack voltage and temperature with the help of 15 system operating parameters. Over 21,0000 data points on various drive cycles having combinations of transient and near steady-state driving conditions are collected, out of which around 15,000 points are used for training the network and 6,000 for the evaluation of the model performance. Various data filtration techniques and neural network calibration processes are explored to condition the data and understand the impact on model performance. The calibrated neural network accurately predicts the hybrid power system dynamics with an R-squared value greater than 0.98, demonstrating the potential of machine learning algorithms for system development and diagnostics.