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• A tripartite evolutionary game model of participation in recycling is constructed. • The government does not provide enough incentives for consumers. • In this recycling system, the government will gradually reduce its regulation. • The regulation of power battery recycling by the Chinese government is imperfect. • Improving compensation can improve consumers' recycling enthusiasm. While electric vehicles (EVs) are developing at a high speed in China, the power battery market is facing a decommissioning peak. The problem is that the recycling situation of domestic power batteries is not ideal, partly due to neglect by consumers. By considering the recycling system, mode, and policy of China's EV power batteries, we construct a tripartite evolutionary game model of the government, consumers and EV manufacturers; analyse the stable strategy adjustment mechanisms of tripartite participation in this recycling cooperation game; and simulate the tripartite evolutionary game. The results show that when the initial willingness of the government, consumers and EV manufacturers to recycle power batteries is not strong, the government takes the lead, driving EV manufacturers and consumers to participate in power battery recycling. When the government, consumers and EV manufacturers have medium or high levels of initial willingness, the government evolves and chooses a nonregulation strategy. In addition, by simulating the impact of changes in consumer-related influencing factors on this tripartite evolutionary game, we find that subsidies for recycling power batteries are a key factor affecting consumers' strategy choices and that boosting recycling compensation for consumers can improve their enthusiasm to participate in such recycling. Therefore, to improve the recycling of power batteries for EVs, in terms of both efficiency and percentage of deployment, the Chinese government should strengthen public education on power battery recycling, further integrate informal recycling channels, and balance the distribution of profits among consumers for recycling compensation. [ABSTRACT FROM AUTHOR]

Under the pressure of energy and environmental protection, new energy vehicles have become the future direction of automotive development. However, the safety performance of the power battery has always been the most critical indicator in the new energy vehicle industry. The battery will be aged in the continuous charging and discharging cycle, and the aging will cause safety hazards when it reaches a limit. A model that can predict the battery life can be obtained using Machine Learning. To obtain models that can predict power battery life relatively accurately, this paper revolves around the chaos sparrow search optimization algorithm, Random Forest, XGBoost, LightGBM, CatBoost, and NN, the importance assessment of the features, the hyperparameter search process, and the comparison of the differences and performance between the different algorithms are discussed. CatBoost has the highest prediction accuracy, with the amount of predicted data with a relative error of less than 10% being 88.44%. (a total of 10,275 data in the test set). And finally comes up with a general approach to predicting power battery life using Machine Learning. [ABSTRACT FROM AUTHOR]

• A novel SSPCM/TEC/seawater cooling system for marine power batteries was proposed. • Seawater cooling without metal pipes solution was proposed. • SSPCM was used as seawater channels in the cooling systems. • Thermoelectric coolers were used to optimize the cooling system. For green shipping, lithium-ion battery-powered ships are rapidly developing. Compared with power batteries for electric vehicles, the large volume and capacity of cells in electric ships make it difficult to dissipate heat, thus requiring efficient cooling. Herein, a novel shape-stabilized phase change material (SSPCM) channel/thermoelectric cooler (TEC) cooling system for marine power batteries was proposed and experimentally studied. It uses TEC for localized cooling and seawater flowing inside the SSPCM as coolant. First, the arrangement of the TECs was experimentally determined. Then, the cooling performance of the system was verified by comparative experiments. The results show that, compared to the cooling system without TECs, the maximum temperature difference was reduced by 28.8 %, with the maximum temperature and temperature difference of 49.8 °C and 3.7 °C, respectively. Compared with the single cooling method, the maximum temperature and temperature difference were reduced by 15.5 % and 53.3 %, respectively. Finally, the experiments were conducted to explore the impact of various parameters on the cooling performance of the system. The unique combination of SSPCM channel, seawater, and TEC provides a new strategy for efficiently cooling marine power batteries at high charge/discharge rates. [Display omitted] [ABSTRACT FROM AUTHOR]

Cell voltage inconsistency in a battery pack is an important signal released by the deterioration of battery performance. In this paper, voltage inconsistency is categorized into static inconsistency and dynamic inconsistency, and the latter contains progressive fluctuation fault and sudden fluctuation fault. For voltage dynamic inconsistency, this paper innovatively proposes a fault diagnosis and type identification method based on weighted Euclidean distance evaluation and statistical analysis. Specifically, firstly, the Euclidean distance evaluation method is used to quantify the cumulative length of the voltage curve of each individual cell in the time window, and at the same time, different forgetting factors are weighted on the voltage line segments to mitigate the fault diagnostic delay and memory effect defects of the time window. Then, a voltage abnormality evaluation coefficient is introduced to characterize the degree of inconsistent fluctuation of the cell voltage, and statistical methods are used to find a reasonable threshold. Further, fault type identification algorithm layer is conducted to identify the type of cell voltage fluctuation by using the optimized correlation coefficient method while the cell is detected by the fault diagnosis algorithm layer. Finally, the effectiveness of the proposed fault diagnosis strategy is verified by experimental data, and an online platform is utilized to obtain voltage data with different fault characteristics to test the practical application of the method. By comparing the proposed method with various data-driven methods with the same data evaluation significance, the results show that the method in this paper is more robust to static voltage inconsistency and the length of the time window, and is capable to recognize the various data patterns of potential threats, with higher accuracy and computational efficiency. • Euclidean distance evaluation is used for cell voltage inconsistency fault diagnosis. • Forgetting factor is used to mitigate fault diagnosis delay and memory effect defects of time window. • Optimized correlation coefficient is used to isolate pattern of cell voltage inconsistent fluctuations. • The accuracy is more than 99%. [ABSTRACT FROM AUTHOR]

• Given that the government gives a certain percentage of research and development subsidies to the supplier who carries out technological innovation, the cost pressure is eased. • We construct two different co-opetition models, namely wholesale co-opetition and patent licensing co-opetition. • The optimal model selection of supply chain members is discussed from the perspective of profit. • Under certain conditions, the patent licensing model can enable the supplier and manufacturer to achieve a win–win situation. We study the co-opetition model selection problem for a new energy vehicle (NEV) supply chain comprising a dominant manufacturer and a subordinate supplier. Given the impact of R&D subsidies on suppliers' technological innovation, we consider two co-opetition models, namely wholesale co-opetition and patent licensing co-opetition, derive the optimal decisions of the models, and analyze the optimal selections for supply chain members. We also conduct extensive numerical studies to verify the research findings and generate practical insights. We find that patent licensing co-opetition is not necessarily beneficial to supply chain members. However, under certain conditions, patent licensing co-opetition can create a "win-win" situation for the supply chain. Furthermore, as the degree of product substitution increases under patent licensing co-opetition, the optimal retail prices of the manufacturer and supplier both decrease first and then increase, but their thresholds are different. In addition, the R&D cost coefficient positively affects the fixed cost of patent usage, but it does not affect the unit patent licensing fee. This study provides theoretical guidance for supply chain members to choose the optimal co-opetition models and has important practical significance for promoting the sustainable development of the NEV industry. [ABSTRACT FROM AUTHOR]

• Considering the sales proceeds, the network's profit reached RMB 88.84 million. • The closer node is to the central district, the more EOL batteries are processed. • The uneven distribution of nodes increases transportation costs. • 21,192 tons of EOL power batteries do not flow into the network from 2015 to 2022. China's policies contributed to the significantly growing sales of electric vehicles (EVs). Unproperly managing giant amounts of end-of-life (EOL) power batteries could result in resource waste and negative environmental impacts. Accelerating the construction of a reverse logistics network (RLN) for EOL power batteries in Chengdu plays an exemplary role in Western China. Using the mixed-integer linear programming (MILP) model with the aim of cost minimization, we design an RLN for EOL batteries in Chengdu based on the existing collection sites. Considering costs, sales of refurbished batteries and metals from EOL batteries, the network's profit in 2022 amounted to RMB 88.84 million. The RLN still faces challenges. 21,192 tons of EOL batteries that don't flow into the network prevent the recovery of many metal resources. In addition, wide variation in scrapping volumes by district, uneven distribution of nodes, and a few alternative sites put more load on nodes closer to the central district and aren't conducive to cost reduction and digestion of the urban mine. We agree that traceability management, the implementation of extended producer responsibility, increasing nodes in surrounding districts while rationalizing the layout, and government incentives and supervision contribute to the sustainability of the economy and resource supply. [ABSTRACT FROM AUTHOR]

The battery modeling and iterative state calculation in the battery management system is very important for the high-power lithium-ion battery packs, the accuracy of which affects its working performance and safety. An adaptive improved unscented Kalman filtering algorithm is developed to realize the iterative calculation process, aiming to overcome the rounding error in the numerical calculation treatment when it is used to estimate the nonlinear state value of the battery pack. As the sigma point is sampled in the unscented transform round from the unscented Kalman filter algorithm, an imaginary number appears that results in the working state estimation failure. In order to solve this problem, the decomposition is combined with the calculation process. Meanwhile, an adaptive noise covariance matching method is implied. Experiments show that the proposed method can guarantee the semi-positive and numerical stability of the state covariance, and the estimation accuracy can reach the third-order precision. The estimation error remains 1.60% under the drastic voltage and current change conditions, which can reduce the estimation error by 1.00% compared with the traditional method. It can provide a theoretical safety protection basis of the energy management for the lithium-ion battery pack. • An adaptive power battery modeling and iterative state calculation method is proposed. • The improved Kalman filtering algorithm is investigated in the SOC estimation. • The relaxation effect is considered to realize the accurate power availability correction. • The residual power prediction problem is solved by considering complex current influence. [ABSTRACT FROM AUTHOR]

A fault diagnosis method for electric vehicle power batteries based on a time-frequency diagram is proposed. First, the original voltage signal is decomposed by improved variational mode decomposition to eliminate the influence of battery inconsistency on battery feature extraction. Then, the continuous wavelet transform is used to transform the one-dimensional signal into a two-dimensional time-frequency diagram, and the image entropy is used to reflect the characteristic parameters of the battery fault. Finally, the abnormal battery is marked with clustering algorithm. It is verified by real vehicle data that the proposed method can identify the battery fault and advance the identification time. • Decomposing voltage signals to reduce the impact of battery inconsistencies. • Extract battery fault features from time-frequency domains to improve sensitivity. • The clustering algorithm is used to realize the recognition of the fault battery. [ABSTRACT FROM AUTHOR]

The production and sales of electric vehicles in China are continuously increasing, and a massive wave of scrap batteries is about to emerge. However, there are many challenges in the power battery recycling, such as a disorganized recycling system, a lack of standards, and low efficiency. In addition, the power battery recycling supply chain faces issues such as a lack of transparency, inadequate data management, low trustworthiness, and counterfeit products. Therefore, it's crucial to establish an efficient and sustainable power battery supply chain recycling system. Blockchain technology can provide solutions in decentralized data management, privacy protection, and anti-counterfeiting traceability. It addresses existing problems and improves the Power Batteries Reverse Supply Chain (PBRSC) ecosystem. Thus, this paper proposes a blockchain-based PBRSC management integration framework. Firstly, a four-layer blockchain power battery recycling platform is designed. Secondly, based on the proposed PBRSC framework, this paper studies three key component technologies: double-chain structure and its storage mechanism, information tracing module, and smart contract enabled battery trading management. Then, the validity and feasibility of the double-chain structure was demonstrated experimentally. Finally, the above architecture and key technologies are implemented in the system, the main functional modules are displayed on the interface, and the effectiveness of the framework is explained in cooperation with related enterprises. [ABSTRACT FROM AUTHOR]

Metal cobalt has infinite potential for super performance aqueous alkaline batteries. But it is a high degree of requirements to design and synthesize composites to solve the common problems than Co materials. Hence towards that end, Co@N-rich carbon hybrids (CoNC) have been designed and prepared via an in-situ, facile and effective method. By doping abundant nitrogen (about 5 wt%), the mesoporous carbon shells provide more active sites, enhanced electrical conductivity and electrode infiltration, and avoid the Co nanoparticles agglomeration and serious electrode dissolution. When used as the electrode for the aqueous alkaline battery, the CoNC hybrids deliver outstandingly higher discharge capacity, superior high-rate ability, and stable cycling ability. A high discharge capacity of 625 mAh g −1 has been delivered at 500 mA g −1 and reduced to 455.7 mAh g −1 after 150 cycles for the CoNC electrode. Even the current increases up to 5 A g −1 , the capacity still remains at 461.3 mAh g −1 , manifesting the fascinating high-rate discharge ability. Importing the rich nitrogen species makes a significant difference to give full play a positive role on the aqueous alkaline battery, which as well guides a new direction towards high-rate power batteries. [ABSTRACT FROM AUTHOR]

The mileage range of electric vehicles is still restricted incredibly due to the limitation of the onboard battery energy and long charging time; therefore, a comprehensive energy consumption optimization of the thermal management system is necessary to guarantee the normal use of electric vehicles. A test bench of integrated battery thermal management system for pure electrical transportation vehicles is established in this study. The optimal thermal management strategy is developed based on analyzing the energy-saving potential of thermal management components. When the battery system operates under real-vehicle operating conditions at representative ambient temperatures, the thermal management system characteristics applied to the proposed strategy are validated and analyzed. The experimental tests show that the proposed thermal management strategy can effectively reduce the energy consumption of the thermal management system under the premise that the battery temperature is controlled within the appropriate range, compared with the prototype vehicle. • The performances of thermal management system based on a real vehicle prototype test. • Analyzation on the energy-saving potential of the energy consuming components for the battery thermal management system. • Experimental investigation of a collaborative control strategy at the real vehicle application level. • Recommendations for optimizing the energy consumption of battery thermal management systems. [ABSTRACT FROM AUTHOR]

Being one of the important parameters describing the state of power battery, state of charge (SOC) is essential for the electric vehicle battery management system (BMS). SOC estimation method, which combines the constructed controlled auto-regressive and moving average (CARMA) model with the feedforward-feedback compensation method used for revising SOC by the deviation of terminal voltage, is presented in this paper. Fully taken into account the measurement errors of voltage and current, the CARMA model is employed to estimate the battery open-circuit voltage (OCV). With the good consistency of the OCV-SOC curve under the process of battery charge and discharge cycles within a certain temperature range, OCV is adopted to estimate SOC. BP neural network rather than the high order polynomial approximation is used to capture the strong nonlinear relationship between OCV and SOC with the high precision. It is a big challenge for OCV-based SOC estimation that the flat area of OCV-SOC curve for lithium-ion power battery enlarges the measurement errors of OCV. By analyzing the flat characteristic of ΔSOC-OCV curve, the feedforward-feedback compensation for SOC is used for improving the accuracy of OCV-based SOC estimation. Experiment results confirm the effectiveness of the proposed approach that has evidently advantages over other estimation methods. [ABSTRACT FROM AUTHOR]

A large amount of heat inside the power battery must be dissipated to maintain the temperature in a safe range for the hybrid power train during high-current charging/discharging processes. In this article, a combined experimental and theoretical study has been conducted to investigate a newly designed thermal management system integrating phase change material with air cooling. An unsteady mathematical model was developed for the battery with the integrated thermal management system. Meanwhile, the heat generation power, thermal resistance, and time constant were calculated. The effect of several control parameters, such as thermal resistance, initial temperature, melting temperature and ambient temperature, on the performance of the integrated thermal management system were analyzed. The results indicated that: (1) the calculated temperature rise of the battery was in good agreement with the experimental data. The appropriate operation temperature of the battery was attained by the action of the phase change storage energy unit which is composed of copper foam and n -Eicosane, (2) the remarkable decrease of the battery temperature can be achieved by reducing the convection thermal resistance or increasing the conductivity of the phase change storage energy unit, where the latter could be the better option due to no additional energy consumption. When convective resistance and thermal resistance between the battery surface and the phase change storage energy unit are less than 2.03 K/W and 1.85 K/W, respectively, the battery will not exceed the safety temperature under extreme condition, (3) the temperature rise declines with the decrease of the melting temperature or with the increase of the ambient temperature. It could be possible that the battery temperature exceeds the safety temperature for the high ambient temperature, (4) even if the phase change material is completely melted, the integrated thermal management system can still maintain the battery temperature within the safe range because of the air cooling. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of the mechanisms of battery is essential for the design of thermal management systems. In this work, a second-order equivalent circuit model considering the hysteresis effect of the battery was developed, and a combined EIS and HPPC characterization method was used for parameter identification. The results showed that the combining EIS and HPPC parameter measurement methods could extract and analyze the parameters of the battery with different time scales and improve the accuracy of the model. It was found that the polarization internal resistance (R 1 and R 2) of the battery shows a stronger temperature dependence than the ohmic internal resistance at low temperatures, with R 1 being about 170 times higher at low temperatures (10 °C) than high temperatures (50 °C). At the same temperature, τ 2 was approximately 100 times larger than τ 1 , which was two orders of magnitude difference between τ 1 and τ 2. In addition, the dynamic heat generation results showed that the heat generation rate of the battery increases with decreasing temperature. At 3C discharge rate, the heat generation rate of the battery at low temperature (−10 °C) was about 1.5 times higher than at room temperature (25 °C). • The time-scale information in the battery is extracted and analyzed. • Relationships between internal parameters and kinetic processes were established. • The dynamic heat generation process of the battery was studied. [ABSTRACT FROM AUTHOR]

The use of electric bicycles (EBs) for short- and medium-distance travel provides an opportunity to decarbonize the transport sector. Using life cycle assessment, this study evaluated and compared the environmental and economic performances of EBs powered by four different types of batteries in China. The results show that EBs powered by lead-acid batteries have the worst environmental performance, whereas those powered by lithium iron phosphate batteries have the best environmental performance. Motor production was found to have the highest potential for metal depletion and toxicity for humans and ecology, owing to the use of copper in its production. These environmental consequences can be mitigated by continuous promotion of circular economy. The use stage brings about the most environmental pressure related to energy use. Power cleaning will reduce most environmental indicators by 10–25%. EBs powered by lithium manganate batteries have been a mainstream technology, but the short lifetime and low economic value of recycling the battery may constrain their development. [Display omitted] • Environmental and economic performance of electric bicycles (EBs) are evaluated. • Lead-acid battery EBs have the worst performance but use the least metals. • Motor is the component with the greatest environmental impact due to copper use. • Promoting circular economy can reduce environmental impact related to metal use. • EBs powered lithium-ion batteries have higher total cost but lower cost per km. [ABSTRACT FROM AUTHOR]

The data-driven method is used widely to estimate the state of health (SOH) of the battery, but the selection of data features and the data training methods affect the estimation results greatly. With the stacking algorithm, this paper proposes a multi-feature fusion model to estimate battery SOH by fusing different feature parameters and combining support vector regression (SVR) and long short-term memory network (LSTM). The feature parameters were extracted only from the current change curve of the constant voltage charging stage. The support vector regression based on grid search (GS-SVR) was selected as the primary-learner, and the primary SVR models were constructed through 5-fold cross-validation for different feature parameters. The LSTM was selected as the secondary-learner. With the stacking algorithm, LSTM was used to fuse multiple primary SVR models to form an ensemble learner model to improve the performance of multi-feature fusion. The battery aging test data set and NASA battery test data set were used to evaluate the effectiveness. The results verified the validity and superiority of the proposed method. Compared with the existing estimation methods, root mean square error is reduced by at least 0.11, and mean absolute percentage error is reduced by at least 0.12%. • The input features are extracted from the current change curve during the charging process. • The Stacking algorithm is used to estimate the battery SOH. • LSTM are used to fuse multiple primary SVR models to form an ensemble learner model. • The battery aging test data set and NASA battery test data set are used to evaluate the effectiveness. [ABSTRACT FROM AUTHOR]

Recycling waste batteries for remanufacturing or echelon utilization is conducive to energy storage and the electric vehicle market. To address the distinct difficulties in the process of waste battery-to-reutilization, we build an evolutionary game to model three parties that include the government, manufacturing, and consumers. We find that the choice of strategies for government support, echelon utilization, and consumer participation is an equilibrium. Moreover, we simulate waste batteries-to-reutilization under the subsidy reduction scenario. Our findings demonstrate that: (1) the profits of manufacturers and consumers increase significantly in the first year of subsidies granted by the government; (2) a reduction in subsidies initiates various profit trends across the strategies of echelon utilization and remanufacturing. Generally, the profits earned through echelon utilization are higher than those through remanufacturing during the period of subsidy reduction; and (3) after the subsidies fall to zero, the earnings of manufacturers and consumers increase in the long run. Our paper proposes generalizing the typical mechanism of recycling subsidies for cities and formulating a reasonable path for reducing subsidies. Manufacturers should pay more attention to echelon utilization for the two decision-making schemes of echelon utilization and remanufacturing. • Evolutionary game model of waste battery-to-reutilization is constructed. • Simulate waste battery-to-reutilization under the scenario of subsidy reduction. • Profits of manufacturers and consumers will rebound after the subsidy ends. • Echelon utilization rather than remanufacturing should be aroused more attention. [ABSTRACT FROM AUTHOR]

To protect electronic devices and batteries from sharp temperature rise and thermal runaway, active/passive/hybrid thermal management using phase change materials (PCMs) shows prominent and promising potential to maintain them within an optimum temperature range. However, not all PCMs are suitable for practical applications owing to their undesirable thermophysical properties. Therefore, this paper focuses on the selection criteria of PCMs and various techniques for enhancing the thermal conductivity and improving the latent heat of fusion of PCMs. Over 200 PCMs as candidate covering the relevant operating temperature range of 0–100 °C are presented and compared in terms of different selection criteria. Furthermore, a comprehensive review of PCMs employed in cooling of electronic devices and thermal management of power batteries is provided. Finally, future outlooks and research topics on physico-thermal properties enhancement for PCMs and thermal management technologies with PCMs for electronic devices and power batteries are proposed. • Selection criteria of PCMs based on figure of merit are provided. • Methods for improving the latent heat of fusion of PCMs are summarized. • Techniques for enhancing the thermal conductivity of PCMs are documented. • Over 200 high latent heat PCMs covering temperature range of 0–100 °C are compared. • Review for PCMs employed in electronics and power batteries thermal management is given. [ABSTRACT FROM AUTHOR]

Based on the analysis of the existing State of charge (SOC) Estimation Algorithms of batteries, this paper proposes a hybrid estimation algorithm according to the UKF method, the Ah method and the open circuit method in SOC estimation of battery pack which consists of 320 batteries. The “buckets effect” exists between series batteries and the differences between each cell were also been taken into consideration during estimating the SOC of power batteries. Finally, the hybrid estimation algorithm was applied to estimate SOC of battery pack under ECE driving cycles. The result shows that the error of the hybrid estimation algorithm is less than 5%. [ABSTRACT FROM AUTHOR]

• Pure emulsion and graphene oxide emulsion were successfully prepared. • Shape-stabilized phase change material with channel was successfully developed. • The cooling performances with different coolants were compared. • The cooling strategies were compared. With the rapid development of new energy vehicles, fast charging and fast discharging are gradually popularized, which puts forward higher requirements for power battery thermal management. In this paper, a novel power battery thermal management system with emulsion as coolant and shape-stabilized phase change material (SSPCM) as its channel is designed and heat transfer performance was experimentally studied. The cooling performance of water, emulsion and graphene oxide emulsion (GO emulsion) were compared. The results show that GO emulsion with high thermal conductivity not only shows better temperature uniformity than water, but also exhibits maximum temperature control ability than pure emulsion. Furthermore, to verify the heat storage effect of SSPCM, thermal performance of the novel system is compared with metal (aluminium) channel system. The SSPCM cooling system can greatly improve temperature uniformity compared with metal cooling system at high charge–discharge rate. Based on the above comparative analysis, the SSPCM cooling system with GO emulsion cooling presents optimal cooling performance of batteries. Finally, to reduce pump consumption, the cooling strategies of different start-up times for GO emulsion are studied. The results show that compared with continuous cooling, starting GO emulsion cooling when the maximum temperature of power battery reaches 42 ℃ can improve latent heat utilization of emulsion and reduce the power consumption with few temperature rise (0.2 ℃). [ABSTRACT FROM AUTHOR]

Abstract: In order to increase the cycle time of power batteries and decrease the overall cost of electric vehicles, the thermal management system equipped with heat pipes was designed according to the heat generated character of power batteries. The experimental result showed that the maximum temperature could be controlled below 50°C when the heat generation rate was lower than 50W. Coupled with the desired temperature difference, the heat generation rate should not exceed 30W. The maximum temperature and temperature difference are kept within desired rang under unsteady operating conditions and cycle testing conditions. Applying heat pipes based power batteries thermal management is an effective method for energy saving in electric vehicles. [Copyright &y& Elsevier]

Abstract: This paper reviews the development of clean vehicles, including pure electric vehicles (EVs), hybrid electric vehicles (HEVs) and fuel cell electric vehicles (FCEVs), and high energy power batteries, such as nickel metal hydride (Ni-MH), lithium-ion (Li-ion) and proton exchange membrane fuel cells (PEMFCs). The mathematical models and thermal behavior of the batteries are described. Details of various thermal management techniques, especially the PCMs battery thermal management system and the materials thermal conductivity, are discussed and compared. It is concluded that the EVs, HEVs and FCEVs are effective to reduce GHG and pollutants emission and save energy. At stressful and abuse conditions, especially at high discharge rates and at high operating or ambient temperatures, traditional battery thermal energy management systems, such as air and liquid, may be not meeting the requirements. Pulsating heat pipe may be more effective but needs to be well designed. In addition, progress in developing new high temperature material is very difficult. PCM for battery thermal management is a better selection than others. Nevertheless, thermal conductivity of the PCMs such as paraffin is low and some methods are adopted to enhance the heat transfer of the PCMs. The performance and thermo-mechanical behaviors of the improved PCMs in the battery thermal management system need to be investigated experimentally. And the possibility of the heat collection and recycling needs to be discussed in terms of energy saving and efficient. [Copyright &y& Elsevier]

Abstract: Thermal energy management performance of ageing commercial rectangular LiFePO4 power batteries using phase change material (PCM) and thermal behavior related to thermal conductivity between the PCM and the cell are discussed in this paper. The heat sources are simplified according to the experimental results of the cells discharged at 35A (≈5C). 3-D modules of a single cell and battery pack are formulated, respectively. The results show that the thermal resistance in the cell leads to an inevitable temperature difference. It is necessary to improve the thermal conductivity and to lower the melting point of the PCM for heat transfer enhancement. The PCM with a melting point lower than 45°C will be more effective for heat dissipation, with a desired maximum temperature below 50°C. The temperature difference in the whole unit before PCM melting will be decreased significantly. In addition, a proper k PCM:k c is necessary for a well designed battery thermal energy management system. [Copyright &y& Elsevier]

• Pulsating heat pipe with TiO 2 nanofluids for EV power battery thermal management was proposed. • Thermal performance of pulsating heat pipe with TiO 2 nanofluids for EV power battery under various thermal management strategies was investigated. • Series of thermal management tests were carried out on power battery under low temperatures. • Thermal management system had excellent heating performance and highly improved the low temperature usability performance of power battery. For effective solutions to performance problems, such as discharge capacity attenuation of the pure electric vehicle (EVs) power battery under low temperatures, the heating module of the pulsating heat pipe (TiO 2 CLPHP) for the power battery was constructed with TiO 2 nanofluid as working medium. Moreover, heating performance experiments under different heating strategies were carried out through consideration of the higher efficient bidirectional thermal transfer and thermal conductivity characteristics of the pulsating heat pipe, along with thermal management technology. The results demonstrated that the optimal heat transfer performance of TiO 2 CLPHP could be achieved with working medium concentration of 2% and heat pipe filling ratio of 50%. In addition, TiO 2 CLPHP was utilized for the preheating and heat preservation of power batteries, which could highly reduce the fluctuation of voltage during discharge, as well as improve the low-temperature discharge capacity of power batteries, followed by the effective surface temperature uniformity improvement of the battery (maximum temperature difference on power battery surface did not exceed 2.6°C). Furthermore, when TiO 2 CLPHP was utilized for the power battery heating under continuous heat preservation, the discharge capacity of the power battery reached 58.69Ah, while the capacity enhancement ratio reached up to 56.6%. Therefore, on the basis of the current research, through comprehensive consideration of preheating, heat preservation (preheating time, discharge voltage and discharge capacity) and energy consumption, it proved that the preheating coupled continuous heat preservation was an ideal heating strategy. Through this strategy, rapid heating along with discharge voltage and discharge capacity increase to the maximum extent, the performance of the EVs power battery under low-temperatures was improved. [ABSTRACT FROM AUTHOR]

Excessive high and low temperature can significantly reduce the performance of power battery. To extend the cycle life of battery and then improve the performance of electric vehicles, an oscillating heat pipe (OHP)-cooled battery thermal management (BTM) system was designed and experimentally studied in this paper. The results showed that the higher temperature section of battery should be near the condensation section of OHP. The OHP should not be placed horizontally in order to reduce the reflow resistance of the working fluid within OHP. The start-up temperature of OHP is determined by the desired maximum temperature and the acceptable maximum temperature difference of the battery. The conclusions can be used as reference for the design of OHP-based battery thermal management system. [ABSTRACT FROM AUTHOR]