System on chip battery state estimator: E-bike case study

This paper discusses the hardware implementation and experimental validation of a model-based battery state estimator. The model parameters are identified online using the moving window least squares method. The estimator is implemented in a field programmable gate array device as a hardware block, which interacts with the embedded processor to form a system on a chip battery management system (BMS). As a case study, the BMS is applied to the battery pack of an e-bike. Road tests show that the implemented estimator may provide very good performance in terms of maximum and rms estimation errors. This work also proposes a new methodology to assess the performance of a battery state estimator

Hardware-in-the-Loop Platform for Assessing Battery State Estimators in Electric Vehicles

The development of new algorithms for the management and state estimation of lithiumion batteries requires their verification and performance assessment using different approaches and tools. This paper aims at presenting an advanced hardware in the loop platform which uses an accurate model of the battery to test the functionalities of battery management systems (BMSs) in electric vehicles. The developed platform sends the simulated battery data directly to the BMS under test via a communication link, ensuring the safety of the tests. As a case study, the platform has been used to test two promising battery state estimators, the Adaptive Mix Algorithm and the Dual Extended Kalman Filter, implemented on a field-programmable gate array based BMS. Results show the importance of the assessment of these algorithms under different load profiles and conditions of the battery, thus highlighting the capabilities of the proposed platform to simulate many different situations in which the estimators will work in the target application

Advances in Li-Ion battery management for electric vehicles

This paper aims at presenting new solutions for advanced Li-Ion battery management to meet the performance, cost and safety requirements of automotive applications. Emphasis is given to monitoring and controlling the battery temperature, a parameter which dramatically affects the performance, lifetime, and safety of Li-Ion batteries. In addition to this, an innovative battery management architecture is introduced to facilitate the development and integration of advanced battery control algorithms. It exploits the concept of smart cells combined with an FPGA-based centralized unit. The effectiveness of the proposed solutions is shown through hardware-in-the-loop simulations and experimental results

Hardware-in-the-loop simulation of FPGA-based state estimators for electric vehicle batteries

This paper describes a hardware-in-the-loop (HiL) simulation platform specifically designed to test state estimators for Li-ion batteries in electric vehicle applications. Two promising estimators, the Mix algorithm combined with the moving window least squares and the dual extended Kalman filter, are implemented in hardware on a field-programmable gate array (FPGA) and evaluated using the developed HiL platform. The simulation results show the effectiveness of using FPGAs for hardware acceleration of battery state estimators and the importance of their assessment under different operating conditions, i.e., driving schedules, which can be simulated by the HiL platform

Comparison of State and Parameter Estimators for Electric Vehicle Batteries

A Battery Management System (BMS) is needed to ensure a safe and effective operation of a Lithium-ion battery, especially in electric vehicle applications. An important function of a BMS is the reliable estimation of the battery state in a wide range of operating conditions. To this end, a BMS often uses an equivalent electrical model of the battery. Such a model is computationally affordable and can reproduce the battery behaviour in an accurate way, assuming that the model parameters are updated with the actual operating condition of the battery, namely its state-of-charge, temperature and ageing state. This paper compares the performance of two battery state and parameter estimation techniques, i.e., the Extended Kalman Filter and the classic Least Squares method in combination with the Mix algorithm. Compared to previous ones, this work focuses on the concurrent estimation of battery state and parameters using experimental data, measured on a Lithium-ion cell subject to a current profile significant for an electric vehicle application

On-chip implementation of Extended Kalman Filter for adaptive battery states monitoring

This paper reports the development and implementation of an adaptive lithium-ion battery monitoring system. The monitoring algorithm is based on the nonlinear Dual Extended Kalman Filter (DEKF), which allows for simultaneous states and parameters estimation. The hardware platform consists of an ARM cortex-M0 processor with six embedded analogue-to-digital converters (ADCs) for data acquisition. Two definitions for online state-of-health (SOH) characterisation are presented; one energy-based and one power-based. Moreover, a method for online estimation of battery's capacity, which is used in SOH characterisation is proposed. Two definitions for state-of-power (SOP) are adopted. Despite the presence of large sensor noise and incorrect filter initialisation, the DEKF algorithm poses excellent SOC and SOP tracking capabilities during a dynamic discharge test. The SOH prediction results are also in good agreement with actual measurements

Design and control of harbour area smart grids with application of battery energy storage system

Global trade occurs mostly on seaborne vessels, and harbours exist as the most significant part for enabling the economic development of any country. However, the amount of fossil fuels used by conventional diesel-engine powered vessels produce a great number of types of toxic emissions, such as air pollution particles at harbours, which create a threat to human health that can contribute to higher morbidity and mortality rates among humans. Therefore, the International maritime organisation and the European Directives recommend that ships implement methods that limit toxic gas emissions and air pollution, such as using onshore power supply and fuel with low-sulphur content for on-board power generation in vessels while remaining at harbours. This research presents cutting-edge methods and tools for contributing to the development of future marine solutions and analyses of modern vessel technological requirements as well as harbour grids, and it proposes novel models of harbour area smart grids for facilitating the support of onshore power supply and charging of batteries for those vessels that require it. This research explores the usage of multiple battery-charging configurations with either slow- or fast-charging systems for electric or hybrid vessels, and it analyses the technical challenges that could inhibit or prevent the practicality of their implementation. The suitable size and allocation of battery energy storage systems for real-world case power systems of Åland Islands harbour grid are also investigated to enhance power capacity of harbour grids. Moreover, a control algorithm for the battery energy storage controller was first developed in MATLAB/Simulink for the Vaasa harbour grid, and then its performance was tested in the OPAL-RT real-time simulator by conducting a controller hardware-in-the-loop test to maintain the power balance inside the harbour grid. The proposed harbour grid models can reduce the degree of pollution that degrades the environment while providing onshore power supply and battery-charging stations for hybrid or electric vessels. Moreover, this dissertation acts as a foundation for developing future business strategies for ship owners, port administrators, and local authorities to solve similar problems as technology develops and environmental degradation continues to be a problem of every country in the world.Maailmanlaajuinen kauppa tapahtuu pääasiassa merialuksilla, ja satamista on tulossa merkittävin osa minkä tahansa maan talouskehitystä. Perinteisten dieselmoottorialusten käyttämä fossiilinen polttoaine aiheuttaa kuitenkin satamissa monenlaisia myrkyllisiä päästöjä ja ilmansaasteita, jotka ovat uhka ihmisten terveydelle ja aiheuttavat monenlaisia vaarallisia sairauksia. Tästä syystä Kansainvälinen merenkulkujärjestö IMO ja EU-direktiivit suosittelevat, että alukset käyttävät satamissa ollessaan maalta tulevaa sähkönsyöttöä tai vähärikkistä polttoainetta myrkyllisten kaasupäästöjen ja ilmansaasteiden rajoittamiseksi. Tämä tutkimus esittelee uusimpia ja tulevaisuuden merenkulun ratkaisuja, analysoi nykyaikaisten alusten teknisiä vaatimuksia sekä satamaverkkoja ja esittelee uusia malleja satama-alueen älykkäille sähköverkoille, joilla tuetaan maasähkön käyttöä ja akkujen lataamista vaativia aluksia. Tutkimuksessa tarkasteltiin useita akkuenergiavarastojen latauskonfiguraatioita sekä hitailla että nopeilla latausjärjestelmillä sähkö-/hybridialuksille ja analysoitiin niiden käytännön toteutukseen liittyviä teknisiä haasteita. Akkuenergiavarastojen sopivaa kokoa ja sijoittelua satamaverkkojen tehokapasiteetin parantamiseksi selvitettiin todelliseen verkkoon perustuvassa tapaustutkimuksessa, jossa parannettiin Ahvenanmaan verkon satamien tehokapasiteettia. Lisäksi kehitettiin akkuenergiavarastojen ohjausalgoritmi tehotasapainon ylläpitämiseksi Vaasan satamaverkossa ensin MATLAB/Simulink-mallina, jonka jälkeen sen suorituskykyä testattiin OPAL-RT reaaliaika-simulaattorilla suorittamalla ns. laitesilmukkasimulaatioita. Ehdotetuilla satamaverkkomalleilla voidaan vastata ilmansaasteista aiheutuviin ympäristöongelmiin sekä mahdollistaa maasähkönsyöttö ja akkujen latausasemat tuleville hybridi- ja sähköaluksille. Lisäksi tämä väitöskirja voi toimia pohjana uusien liiketoimintastrategioiden kehittämiselle alusten omistajien, satamajohtajien ja paikallisviranomaisten tarpeisiin.fi=vertaisarvioitu|en=peerReviewed

Dual Output Power Management Unit for PV-Battery Hybrid Energy System

The tremendous evolution in the electronics industry has provided high performance portable devices. However, the high power demand and the limited capacity of batteries, prevent the devices from operating for a long time without the need of a power outlet. The ease of deploying Photovoltaic (PV) cells close to the device enables the user to harvest energy on the go, and get rid of the conventional power outlets. However, applying the PV power to the electronic devices is not as easy as the plug and play model, due to the unstable output voltage and power of the PV cells. In this thesis, a power management unit is proposed to provide dual regulated outputs using a PV module and a rechargeable battery. The main components of the unit are a Dual Input Multiple Output (DIMO) DC-DC converter and a digital controller. The converter is used to interface the battery and the PV module with the loads. Moreover, the proposed converter has the ability to step up or step down the input voltage. The controller maximizes the PV power using the fractional open circuit voltage Maximum Power Point Tracking (MPPT) method. Furthermore, the controller manages the amount of power supplied to or from the battery in order to satisfy the load demand and regulate the outputs at the required levels. The controller has been implemented and synthesized using VHDL. A prototype has been implemented using an FPGA and off the shelf components. The functionality of the system has been tested and verified under varying environmental conditions