A review on online state of charge and state of health estimation for lithium-ion batteries in electric vehicles

Electric vehicles (EVs) are emerging as a favorable strategy to meet the increasing environmental concerns and energy insufficiency, and this trend is expected to grow in the near future. However, the inadequate charging infrastructure is becoming a major barrier to the wide acceptance of EVs. Deployment of this infrastructure is expected to maximize the adoption of EVs to facilitate users' range anxiety. Therefore, connectivity between the charging stations (CS) is mandatory. Understanding the real-time status of CSs can provide valuable information to users such as availability of charging provisions, reserves and the time to reach the CS. The intent of this paper is to provide a better EV charging system by utilizing the advantages of the Internet of Things (IoT) technology. The IoT paradigm offers the present facilities a real-time interactional view of the physical world by a variety of sensors and broadcasting tools. This research article proposes a real-time server-based forecasting application: i) to provide scheduling management to avoid waiting time; and ii) to provide a real-time CS recommendation for EVs with an economic cost and reduced charging time. In addition, the proposed scheme avoids third-party intervention and protects EV user privacy and complex information exchange between the user and CS. The end users can easily use the CS based on their requirements. This synergetic application is built up through the PHP programming language in the Linux UBUNTU 16.04 LTS operating system, and all relevant information is processed and managed through Cloud Structured Query Language (CSQL) from a Google cloud platform. The effectiveness of this application is also validated through a low-cost test system using LTC 4150, ESP 8266 Wi-Fi module and Arduino.

Internet of Things based real-time electric vehicle load forecasting and charging station recommendation

In this paper, a general quasi-steady backward-looking model for energy consumption estimation of electric vehicles is presented. The model is based on a literature review of existing approaches and was set up using publicly available data for Nissan Leaf. The model has been used to assess the effect of ambient temperature on energy consumption and range, considering various reference driving cycles. The results are supported and validated using data available from an experimental campaign where the Nissan Leaf was driven to depletion across a broad range of winter ambient temperatures. The effect of ambient temperature and the consequent accessories consumption due to cabin heating are shown to be remarkable. For instance, in case of Federal Urban Driving Schedule (FUDS), simplified FUDS (SFUDS), and New European Driving Cycle (NEDC) driving cycles, the range exceeds 150 km at 20 °C, while it reduces to about 85 km and 60 km at 0 °C and −15 °C, respectively. Finally, a sensitivity analysis is reported to assess the impact of the hypotheses in the battery model and of making different assumptions on the regenerative braking efficiency.

Effect of Ambient Temperature on Electric Vehicles’ Energy Consumption and Range: Model Definition and Sensitivity Analysis Based on Nissan Leaf Data

The estimation of energy consumption is an important prerequisite for planning the required infrastructure for charging and optimising the schedules of battery electric buses used in public urban transport. This paper proposes a model using a reduced number of readily acquired bus trip parameters: arrival times at the bus stops, map positions of the bus stops and a parameter indicating the trip conditions. A deep learning network is developed for deriving the estimates of energy consumption stop by stop of bus lines. Deep learning networks belong to the important group of methods capable of the analysis of large datasets—“big data”. This property allows for the scaling of the method and application to different sized transport networks. Validation of the network is done using real-world data provided by bus authorities of the town of Jaworzno in Poland. The estimates of energy consumption are compared with the results obtained using a regression model that is based on the collected data. Estimation errors do not exceed 7.1% for the set of several thousand bus trips. The study results indicate spots in the public transport network of potential power deficiency which can be alleviated by introducing a charging station or correcting the bus trip schedules.

Estimation of the Energy Consumption of Battery Electric Buses for Public Transport Networks Using Real-World Data and Deep Learning

State-of-charge (SoC) estimation is of great importance for electric vehicles (EVs) optimum operation, while highly dynamic operation environment makes this task extremely thorny. By far, considerable researches have been done in modeling and approaches to accurately estimate SoC for lithium-ion batteries (LiBs) used in EVs. Nevertheless, existing reviews are either ambiguous in classifications or incomplete in methodologies, especially lack of detailed evaluation. To solve these issues, this work undertakes a comprehensive survey on various models and methods utilized in LiBs SoC estimation, which aims to more specifically guide automobile engineers and researchers utilize these techniques in an effective manner and facilitate future research works. Enumeration method is used for review screening to select the most representative papers in an application-oriented manner. Firstly, five modeling techniques are outlined, which are significant in improving estimation precision. Then, six categories along with twenty-one evaluation criteria are summarized for quantitative performance verification. Moreover, various SoC estimation methods in six categories are thoroughly discussed, among which ninety-three methods are carefully elaborated in table for a systematic evaluation and fair comparison. Meanwhile, this work gives insight to SoC estimation methods with emphasis on their major benefits/limitations and application conditions. Lastly, important recommendations of future researches are disclosed in the hope of providing some inspirations to propel EVs applications. All in all, this work provides a state-of-the-art one-stop handbook of LiBs SoC estimation used in EVs.

Classification, summarization and perspectives on state-of-charge estimation of lithium-ion batteries used in electric vehicles: A critical comprehensive survey

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.

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Accurate range estimation for an electric vehicle including changing environmental conditions and traction system efficiency

This paper proposes a state-of-the-art algorithm for a real-time charging recommendation for an electric vehicle driver based on an accurate real-time range indicator system to avoid range anxiety. The charging recommendation algorithm alerts the driver when charging is deemed required for the selected route. This algorithm determines the nearest charging location obtained using GPS based on an accurate estimation of state of charge (SoC) at the destination and when charging determines the optimum charging time required by the battery to have sufficient energy to reach the destination. The graphical user interface of the real-time range indicator system is also used to show the driver an accurate estimation of the remaining range to destination and the current SoC. The results from simulations of a range of routes validate the proposed algorithm.

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An Intelligent Driver Alerting System for Real-Time Range Indicator Embedded in Electric Vehicles

Most of the commercially available range indicator systems for electric vehicles (EVs) do not provide a sufficiently accurate range to destination, as the environmental factors and driver's behavior are generally not taken into account. In this paper, a real-time range indicator system is developed and implemented using online environmental data from various internet resources to estimate accurately the real-time state of charge and the remaining range for the EV while it is on the road. The estimation considers 1) the dynamic wind speed and wind direction with respect to vehicle position, 2) the probability of rain and ambient temperature, 3) the dynamic effective rolling resistance and terrain adhesion coefficient (based on the condition of the road surface), 4) the time-domain efficiency analysis of the propulsion system, 5) the online traffic conditions and auxiliary loads, and 6) the braking force distribution used in commercially available EVs. The real-time range indicator system is validated using measured data from a 2012 Nissan Leaf driven along a selected route in Australia with the maximum error of 8% for the entire route and less than 1% error at the destination.

A Real-Time Range Indicator for EVs Using Web-Based Environmental Data and Sensorless Estimation of Regenerative Braking Power

A precise estimation of the state of charge (SoC) of the lithium-ion battery is crucial for reducing range-anxiety and improving the performance of the electric vehicle (EV) battery management system An accurate estimation of the SoC, however, has remained elusive due to the complex and nonlinear behavior of the battery In this article, a new mixed estimation model (MEM) for the battery parameters and the SoC estimation is proposed, where the route is specified before the travel The new MEM uses a combination of a battery power-based method (BPBM), a combined model, and a partial adaptive forgetting factor recursive least-square (PAFF-RLS) SoC calibration algorithm to make use of the best characteristics of each model to determine a better and more accurate SoC estimation The partial adaptive forgetting factors solves the issue of the different rate changes in the battery parameters and reduces the complexity of the algorithm compared to the fully adaptive recursive models The BPBM allows various traveling factors to be included in the model, such as the environmental conditions, the effect of auxiliary loads, and the traffic congestion To verify the validity of the PAFF-RLS algorithm, two laboratory tests using real-time driving cycles have been conducted on a 2012 Nissan Leaf 311 Ah Manganese-oxide Li-ion battery cell The effectiveness of the MEM model has been demonstrated by driving the Nissan Leaf along two selected routes in Australia The results demonstrate the great accuracy of the proposed method for the SoC estimation, when compared with those from the previous models

Real-Time Estimation of Model Parameters and State-of-Charge of Li-Ion Batteries in Electric Vehicles Using a New Mixed Estimation Model

This paper proposes a state-of-the-art algorithm for a real time charging recommendation for an electric vehicle (EV) driver based on an accurate real-time range indicator system to avoid range anxiety. The charging recommendation algorithm alerts the driver when charging is deemed required for the selected route. This algorithm determines the nearest charging location obtained using GPS based on an accurate estimation of SoC at the destination and when charging determines the optimum charging time required by the battery to have sufficient energy to reach the destination. The graphical user interface (GUI) of the real-time range indicator system is also used to show the driver an accurate estimation of the remaining range to destination and the current state of charge (SoC). The results from simulations of a range of routes validate the proposed algorithm.

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Kaveh Sarrafan

Papers

Accurate range estimation for an electric vehicle including changing environmental conditions and traction system efficiency

An Intelligent Driver Alerting System for Real-Time Range Indicator Embedded in Electric Vehicles

A Real-Time Range Indicator for EVs Using Web-Based Environmental Data and Sensorless Estimation of Regenerative Braking Power

Real-Time Estimation of Model Parameters and State-of-Charge of Li-Ion Batteries in Electric Vehicles Using a New Mixed Estimation Model

An intelligent driver alerting system for real-time range indicator embedded in electric vehicles