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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSJhbGwiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMCIgLz48L3BhcmFtcz48ZmlsdGVycz48ZmlsdGVyIGZpZWxkPSJpbmRleHRlcm1zIiB2YWx1ZT0iJnF1b3Q7Q2hhcmdlIC9lbGVjdHJpYy8mcXVvdDsiIG9yaWdpbmFsX3ZhbHVlPSImcXVvdDtDaGFyZ2UgL2VsZWN0cmljLyZxdW90OyIgLz48L2ZpbHRlcnM+PHJhbmdlcyAvPjxzb3J0cz48c29ydCBmaWVsZD0icHVibGlzaGVkIiBvcmRlcj0iZGVzYyIgLz48L3NvcnRzPjxwZXJzaXN0cz48cGVyc2lzdCBuYW1lPSJyYW5nZXR5cGUiIHZhbHVlPSJwdWJsaXNoZWRkYXRlIiAvPjwvcGVyc2lzdHM+PC9zZWFyY2g+" rel="self" type="application/rss+xml" />
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    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Development of an Energy Prediction Model Based on Driving Data for Predicting the Driving Distance of an Electric Vehicle</title>
      <link>https://trid.trb.org/View/1596640</link>
      <description><![CDATA[In 2016, the 4th industrial revolution, represented by hyper-connected and hyper-intelligent technologies, began, and the global automobile industry is now focusing on the development of smart cars, such as driverless vehicles, connected vehicles, and electric vehicles. In particular, as electric vehicles with autonomous driving features are to be manufactured and sold, the precise prediction of driving distances has become more important. If the actual driving distance is shorter than the prediction of the available driving distance, the autonomous vehicle will stop on the way to its destination. Moreover, the route for electric vehicles should be determined by considering the location of charging stations and available driving distance. Existing studies about the expected driving distance do not appropriately reflect all of the various circumstances, components, and variables, thus restricting their predictive performance. In particular, current methods do not precisely predict the running resistance of electric vehicles, or changes in driving distance caused by the use of electrical functions in such vehicles. Thus, a vehicle energy model for predicting the exact driving distance of an electric vehicle is described in this paper, considering the driving speed, road status, tire pressure, temperature, driving altitude, regenerative braking, and other factors. Finally, the proposed model for predicting the driving distance of electric vehicles was confirmed to be feasible by an in-vehicle test on real roads.]]></description>
      <pubDate>Thu, 23 May 2019 10:23:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/1596640</guid>
    </item>
    <item>
      <title>State of charge estimation of a lithium-ion battery using robust non-linear observer approach</title>
      <link>https://trid.trb.org/View/1593856</link>
      <description><![CDATA[A robust non-linear observer is proposed to estimate the state of charge (SoC) of a lithium-ion battery by employing an electrical model of the battery. Considering the non-linear behaviour of the open circuit voltage versus SoC curve, a non-linear state space model is established. The modelling errors and uncertainties are compensated by the proposed non-linear observer resulting in robustness in the presence of these errors, which is the main feature of the proposed observer. The stability of the observer is proved by the Lyapunov criteria. The effectiveness of the proposed observer is verified by using the experimental test. The test results show that the proposed approach is effective and estimates the SoC with high accuracy. Additional experimental test verifies the robust performance of the proposed observer in the presence of the modelling errors and disturbances.]]></description>
      <pubDate>Tue, 30 Apr 2019 13:57:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/1593856</guid>
    </item>
    <item>
      <title>Power Management Strategy for the 48 V Mild Hybrid Electric Vehicle Based on the Charge-Sustaining Control</title>
      <link>https://trid.trb.org/View/1583541</link>
      <description><![CDATA[To enhance the 48 V mild hybrid electric vehicle performance using a smaller capacity and lower voltage battery than the full hybrid electric vehicle, a novel power management strategy needs to be established that considers the characteristics and limitations of the components. This paper proposes a charge-sustaining control strategy as a ground principle of the 48 V hybrid electric vehicle control for managing the battery state-of-charge (SOC) to stay near the most efficient regime. The base efficiency characteristics of the component models including engine, motor/generator, and battery are determined in the form of efficiency maps using the powertrain analysis tool. Then the control strategy is formulated as a nonlinear optimal regulation problem that meets two conflicting control objectives, such as fuel efficiency improvement and state-of-charge maintenance. The optimal regulation problem implements a discrete-time Hamilton-Jacobi-Bellman approach. The proposed strategy is evaluated by comparing with the reference strategy applying the Dynamic Programming (DP), i.e. a global optimal result, under urban dynamometer driving schedule and worldwide harmonized light duty test cycle. Through the evaluation, the fuel efficiency of the proposed strategy with three different electrical loads is slightly deteriorated at most by 5.03 % from the DP results with staying within a desirable SOC. This suggests that the proposed strategy is operating very closely to global optimal performances.]]></description>
      <pubDate>Wed, 20 Mar 2019 10:39:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/1583541</guid>
    </item>
    <item>
      <title>Prediction of the Vehicle Amount that Can Connect to Power Grid in Local Area</title>
      <link>https://trid.trb.org/View/1528075</link>
      <description><![CDATA[The regulations for internal combustion vehicles, CO2 emission or NOx emission or noise and so on, are strengthened. Therefore EV (electric vehicle)'s market is expanding. The amount of EV get more, the amount of electric get more and the impact for grid that are voltage fluctuation and frequency fluctuation is concerned. The short driving range is also problem for usability. It is important to inform driver not only SOC but possibility to arrive at destination without charge. So predicting to traffic condition is important. As the basic technology, the prediction the vehicles’ state that is drive or stay is important to solve two problems. In this research, Algorithm for predicting vehicle fleet's condition is developed. The data for study and test is obtained by person-trip survey conducted by Ministry of Land, Infrastructure and Transport. The location was divided into 3 areas. And the state was stay or drive from an area to an area. The algorithm is based on left to right Markov-model. Future state probability is predicted using the latest observed state and state transition probability. As the result, prediction error is 3 % as parking. The prediction error of stay is less than the prediction error of drive. The frequency of stay is more than the frequency of drive, and robustness of stay for outliers is stronger. Therefore study data and test data are separated into week day and holiday, prediction error is about 1 %.電気自動車の充電負荷や渋滞予測には車両の移動の予測が必要である。車両の状態を駐車と運転に分類して、マルコフモデルを利用した車両の使用を予測するアルゴリズムを構築した。学習データとテストデータには中部地区のパーソントリップデータを用い、中部地区内での移動予測を行った。]]></description>
      <pubDate>Fri, 31 Aug 2018 13:45:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/1528075</guid>
    </item>
    <item>
      <title>Power optimisation of electric coaster</title>
      <link>https://trid.trb.org/View/1524312</link>
      <description><![CDATA[Sustainability is the capacity to endure and ensure that the advanced technology for electric vehicles (EVs) remain diverse and productive over time. The power optimisation of the battery pack has been maintained by enhancing the lifespan of the battery with keeping the battery temperature 35-40°C and cell's state-of-charge (SOC) balance with the variation 2-5%. A ZigBee wireless battery management system (WBMS) has been developed from this study to control the evaporative cooling battery thermal management system for the optimum range of battery temperature both in charging/adverse discharging. The WBMS allows the vehicle to utilise the maximum energy available from the battery for a given drive cycle whilst maintaining pack SOC balance within the range of optimal functionality. The WBMS multistage charge balancing system offering more effective and efficient responses to several numbers of series connected battery cells. The balancing results for two cells and 16 cells are improved by 15.12% and 25.3% respectively.]]></description>
      <pubDate>Mon, 27 Aug 2018 14:05:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/1524312</guid>
    </item>
    <item>
      <title>A Solar Power-Assisted Battery Balancing System for Electric Vehicles</title>
      <link>https://trid.trb.org/View/1515494</link>
      <description><![CDATA[This paper proposes a solar power-assisted electric vehicle battery balancing system. There are three operation modes of the system: solar-balancing, storage-balancing, and charge balancing. The solar-balancing mode charges the battery module with the lowest state of charge (SOC) using the solar power during vehicle driving; the charge-balancing mode is operated when the vehicle is parked and being charged by the conventional charger. Under this mode, the balancing circuit discharges the battery module with the highest SOC by transferring the energy to an additional storage cell while the solar panel also charges the storage cell independently at the same time if solar power is available. When the solar power is low, the storage-balancing mode will be selected to charge the battery module with the lowest SOC using energy stored in the storage cell. This system eliminates the energy loss that would otherwise happen in conventional active and passive balancing schemes by equalizing the battery using solar/stored energy in the storage cell. A 48-V battery pack with four 12-V battery modules system is simulated and tested. A prototype system is developed to prove the concept. The simulation and experimental results verify that the proposed system not only achieves the same balancing performance as conventional balancing circuits, but also effectively increases the overall usable battery energy by 2.1%–3.3% every 13.2 km.]]></description>
      <pubDate>Mon, 27 Aug 2018 14:05:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/1515494</guid>
    </item>
    <item>
      <title>Improved Battery SOC Estimation Accuracy Using a Modified UKF With an Adaptive Cell Model Under Real EV Operating Conditions</title>
      <link>https://trid.trb.org/View/1515492</link>
      <description><![CDATA[Electric vehicles (EVs) require reliable and very accurate battery state-of-charge (SOC) estimation to maximize their performance. A commonly used estimation technique, the extended Kalman filter (EKF), provides an accurate estimate of the SOC. However, EKF has some limitations, such as it assumes the knowledge of the statistics of the process noise and measurement noise is available, which practically cannot be guaranteed. In this paper, an adaptive equivalent-circuit model is proposed and used for SOC estimation. The proposed model is based on a common cell model with adaptive parameters tracking feature implemented using an artificial neural network controller embedded within the model. A variant of the EKF, namely the unscented Kalman filter (UKF), is used to achieve more accurate estimates of the SOC with a relatively fast convergence speed. The UKF uses the unscented transform to obtain the statistics of the process noise covariance. Furthermore, the autocovariance least-squares technique is used to estimate the measurement noise covariance by accounting for possible correlation in the measurement innovations, which enhances the accuracy of the estimate. Derivation of the proposed method followed by experimental verification is presented in this paper.]]></description>
      <pubDate>Mon, 27 Aug 2018 14:05:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/1515492</guid>
    </item>
    <item>
      <title>Time-Efficient Stochastic Model Predictive Energy Management for a Plug-In Hybrid Electric Bus With an Adaptive Reference State-of-Charge Advisory</title>
      <link>https://trid.trb.org/View/1526184</link>
      <description><![CDATA[In order to develop a practicality oriented low-cost energy management controller for a plug-in hybrid electric bus, besides minimizing energy consumption, algorithmic time efficiency should be put great attention so as to substantially lower the requirement of the controller hardware. This paper first compares two forecasting methods including a Markov chain model and an artificial back propagation neural network based on real driving cycles, showcasing significant superiority of the Markov chain especially in computational efficiency. Moreover, an adaptive reference state-of-charge (SOC) advisement, which is tuned iteratively by taking advantage of speed forecasts in each prediction horizon, is provided with the aim of guiding the battery to discharge reasonably. Then, the Markov chain-based model predictive control is conducted and compared with a linear SOC reference model. Moreover, numerous influencing factors of the computational efficiency, including the prediction horizon length, the sampling width of the optimal power sequence, and the discretization size of state/control variables for solving the dynamic programming problem, are systematically investigated. The results show that the proposed reference SOC advisory is superior to the linear model. The authors further introduce several ways of accelerating the operational efficiency for the model predictive controller. Comparisons with common dynamic programming and charge-depleting and charge-sustaining solutions are also carried out to show the improved performance of the proposed control approach.]]></description>
      <pubDate>Thu, 26 Jul 2018 14:38:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/1526184</guid>
    </item>
    <item>
      <title>Performance Evaluation of Short Range Frequent Charging Electric Bus (Second Report)</title>
      <link>https://trid.trb.org/View/1499954</link>
      <description><![CDATA[A long-term operation test of a specially developed electric bus has been performed as part of a project organized by the Japanese Ministry of the Environment. The following battery-related information was obtained. (1) The bus was driven for 48,714 km over 1,872 days and charged 4,958 times. As a result, the capacity of the on-board lithium-ion batteries (LIB) fell by approximately 25% and the internal resistance increased by approximately 50%. An evaluation method was proposed and adopted that estimated the capacity and internal resistance from easily obtainable terminal voltage and current data without removing the battery and performing detailed measurements. (2) Various measures were proposed and adopted to extend the battery lifetime. These included rotating the batteries between installation positions under different temperature environments, and adopting a battery-friendly seasonally variable charging rate method that reduces the charging power in seasons with lower power consumption. Simulations predicted that these measures have the potential to increase capacity retention by several % over 10 years from battery manufacture.筆者らは環境省事業の一環として，独自に開発した電動バスの三カ年にわたる営業運行を行った．本論文では，ここで得られたバッテリに係る各種知見を報告する．具体的には，バッテリ劣化の簡易的推定方法の検討と検証結果について，さらにはバッテリ寿命伸長を目的とした運用方法改善策の検討結果，検証結果等をまとめる．]]></description>
      <pubDate>Mon, 23 Apr 2018 16:47:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/1499954</guid>
    </item>
    <item>
      <title>Combined estimation of the state of charge of a lithium battery based on a back-propagation– adaptive Kalman filter algorithm</title>
      <link>https://trid.trb.org/View/1502009</link>
      <description><![CDATA[The precise estimation of the battery’s state of charge is one of the most significant and difficult techniques for battery management systems. In order to improve the accuracy of estimation of the state of charge, the forgetting-factor recursive least-squares method is used to achieve online identification of the model parameters based on the first-order RC battery model, and a back-propagation neural-network-assisted adaptive Kalman filter algorithm is proposed. A back-propagation neural network is established by using the MATLAB neural network toolbox and is trained offline on the basis of the battery test data; then the trained back-propagation neural network is used to realize the online optimized results of an adaptive Kalman filter algorithm for estimation of the state of charge. The proposed methodology for estimation of the state of charge is demonstrated using experimental lithium-ion battery module data in dynamic stress tests. The results indicate that, in comparison with the common adaptive Kalman filter algorithm, the back-propagation–adaptive Kalman filter algorithm significantly improved precise estimation of the state of charge.]]></description>
      <pubDate>Mon, 23 Apr 2018 16:44:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/1502009</guid>
    </item>
    <item>
      <title>State of Charge and Parameter Estimation of Lithium-ion Batteries for HEVs and EVs</title>
      <link>https://trid.trb.org/View/1500078</link>
      <description><![CDATA[This paper discusses the accuracy of the simultaneous state of charge (SOC) and parameter estimation method of the batteries for hybrid electric vehicles (HEVs) and electric vehicles (EVs) use. Although it is important to determine the battery aging, to control the charging and the discharging, and/or to maintain battery fault detections, the accuracy of the parameter estimation has not been thoroughly discussed. To address this issue, physico-chemical study using simultaneous state and log-normalized parameter estimation of batteries is provided in this paper. The proposed method is verified by performing a series of experiments using an EV.ハイブリッド自動車(HEV)や電気自動車(EV)の電池のパラメータは劣化診断などのために重要であるが，その精度検証は一般に難しい問題である．本論文では対数化UKFによるSOCとパラメータの同時推定法におけるパラメータ推定精度について，物理化学的な知見に基づいて検証する．提案法の有用性をEVによる走行実験データを用いて示す．]]></description>
      <pubDate>Fri, 30 Mar 2018 09:52:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/1500078</guid>
    </item>
    <item>
      <title>Improvement of Open Circuit Voltage Estimation Method for Lithium Ion Battery</title>
      <link>https://trid.trb.org/View/1499804</link>
      <description><![CDATA[To estimate the remaining capacity of the li-ion battery, the Open Circuit Voltage (OCV) estimation method was examined. The OCV was calculated with revision of the terminal voltage from current and internal resistance. We analyzed the influence of the temperature and the voltage response for the internal resistance calculation and constructed the high accuracy OCV estimation method. Also, carried out the single cell simulation test and inspected the estimation accuracy. It revealed that possible to estimate the OCV with error 0.002V.]]></description>
      <pubDate>Fri, 30 Mar 2018 09:51:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/1499804</guid>
    </item>
    <item>
      <title>The Possibilities of Increasing the Electric Vehicle Range</title>
      <link>https://trid.trb.org/View/1472170</link>
      <description><![CDATA[Electric vehicles have a significantly shorter range compared to the conventional vehicles with the internal combustion engine. Hence, it is important to inform the driver of an electric vehicle as accurately as possible about the actual range and how to reduce energy consumption and thus improve range. The paper presents proposed electric vehicle energy usage assist for increasing vehicle range, system implementation and measured data for energy usage assist function. The developed energy assist encourages the driver to modify his driving style in order to be on the powertrain greatest efficiency area. The system informs the driver about the limitations for example caused by weather conditions or low battery state of charge.]]></description>
      <pubDate>Tue, 29 Aug 2017 10:13:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/1472170</guid>
    </item>
    <item>
      <title>Accurate Range Estimation for an Electric Vehicle Including Changing Environmental Conditions and Traction System Efficiency</title>
      <link>https://trid.trb.org/View/1468207</link>
      <description><![CDATA[Range anxiety is an obstacle to the acceptance of electric vehicles (EVs), caused by drivers’ uncertainty regarding their vehicle's state of charge (SoC) and the energy required to reach their destination. Most estimation methods for these variables use simplified models with many assumptions that can result in significant error, particularly if dynamic and environmental conditions are not considered. For example, the combined efficiency of the inverter drive and electric motor varies throughout the route and is not constant as assumed in most range estimation methods. This study proposes an improved method for SoC and range estimation by taking into account location-dependent environmental conditions and time-varying drive system losses. To validate the method, an EV was driven along a selected route and the measured EV battery SoC at the destination was compared with that predicted by the algorithm. The results demonstrated excellent accuracy in the SoC and range estimation, which should help alleviate range anxiety.]]></description>
      <pubDate>Wed, 31 May 2017 16:09:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/1468207</guid>
    </item>
    <item>
      <title>An Energy Management Strategy for a Concept Battery/Ultracapacitor Electric Vehicle With Improved Battery Life</title>
      <link>https://trid.trb.org/View/1459414</link>
      <description><![CDATA[Using multi-input converters (MICs) in hybrid energy storage systems (HESSs) presents several advantages, such as low component count, control simplicity, and fully control of source energies. The power levels of sources in these systems need to be determined wisely by an energy management strategy (EMS). This paper presents an EMS for a battery/ultracapacitor (UC) HESS including a bidirectional MIC for electric vehicles (EVs). Thanks to the fact that energy flow between battery and UC is free in this MIC, the proposed EMS not only regulates the state-of-charge of UC but also smooths the battery power profile by using a fuzzy logic controller and a rate limiter. Therefore, it results in a sustainable HESS with longer battery life. Through a simulation study and an experimental setup including a real EV, the performance of the proposed system is evaluated comprehensively. Then, based on experimental results, battery cycle-life improvement due to the battery/UC hybridization is explored.]]></description>
      <pubDate>Tue, 28 Mar 2017 17:09:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/1459414</guid>
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