Sensor Modeling, Low-Complexity Fusion ... - Semantic Scholar

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 60, NO. 2, FEBRUARY 2011

Sensor Modeling, Low-Complexity Fusion Algorithms, and Mixed-Signal IC Prototyping for Gas Measures in Low-Emission Vehicles Sergio Saponara, Esa Petri, Student Member, IEEE, Luca Fanucci, and Pierangelo Terreni

Abstract—This paper addresses the detection of hydrogen leaks for safety warning systems in automotive applications and the measurement of nitrogen oxide concentration in exhaust gases of zero-emission vehicles. The presented approach is based on the development of accurate models (including nonlinearity and error sources of real building components) for all the system elements: sensors and acquisition chain. This methodology enables efficient design space exploration and sensitivity analysis, allowing an optimal analog–digital and hardware–software partitioning. Such analysis drives also the development of effective data fusion techniques to reduce the measure uncertainty (due to crosssensitivity to other gases or to temperature/humidity variations). Such techniques have been implemented on a microcontrollerbased mixed-signal embedded platform for intelligent sensor interfacing with limited complexity, suitable for automotive applications. Index Terms—H2 acquisitions, Intelligent Sensor InterFace (ISIF), mixed-signal integrated circuit (IC), NOx acquisitions, sensor fusion, sensor signal conditioning, zero-emission vehicles.

I. I NTRODUCTION

O

NE of today’s trends in the automotive field is to achieve zero-emission vehicles [1], and to this aim, two key research fields should be investigated. First, the state of the art pays attention particularly to reducing the CO2 emission [2], but in the future Euro 6 standard, in place by 2014, reducing the emission of nitrogen oxides will be crucial (indicated by the symbol NOx , with NO and NO2 being the most damaging ones). NOx is an unavoidable pollutant produced by the combustion of any fuel—diesel, petrol, and gas—in presence of air (instead of pure oxygen O2 ) [3]–[5], affecting also future hydrogen-based engines. NOx generation depends on combustion parameters such as duration of the reaction, temperature, and concentration of O2 (see Fig. 1). Therefore, the measure of the NOx emission can be used as a feedback signal to control the parameters of the combustion process, thus reducing the NOx formation. This approach can offer an alternative to other solutions suggested in literature

Manuscript received December 14, 2009; revised February 4, 2010; accepted February 7, 2010. Date of publication November 1, 2010; date of current version January 7, 2011. This work was supported in part by the Tuscany Region project “H2 Filiera Idrogeno” and in part by a Ministero Istruzione Universita Ricerca Fondi Investimenti Ricerca di Base project. The Associate Editor coordinating the review process for this paper was Dr. Cesare Alippi. The authors are with the Department of Information Engineering, University of Pisa, 56122 Pisa, Italy (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TIM.2010.2084230

such as adding selective catalyst filters and urea-based additives (e.g., Blue TDI technology by Volkswagen [6]), which have problems of cost and catalyst life time as already experienced, for example, by particulate filters in diesel engines. Second, there is a growing interest for cars based on fuel cell or internal combustion engines (ICEs) powered by hydrogen [7]–[9]. Hydrogen-based vehicles are equipped with a storage system (a tank with pressures of up to 350 bar) and a supply system affected by problems of gas leakage. Similar problems affect all the components of the hydrogen chain (see Fig. 2), from industrial production plants to the vehicle’s tank. Since the lower explosivity limit (LEL) of hydrogen is just 4% (40 000 ppm) in air, it is necessary to monitor both H2 concentrations of several thousands of parts per million, to open security valves with very fast response, and small gas leaks (few hundreds of parts per million) to warn the car users. For vehicles or storage/distribution systems where both methane and hydrogen are used [9], there is the problem of methane (CH4 ) cross-sensitivity of state-of-the-art H2 sensors. A. Limits of the State of the Art and Paper Outline The state of the art of sensing and acquisition systems for H2 and NOx measures suffers from some problems to overcome. The performance of commercially available H2 and NOx sensors often depends on temperature and humidity and is correlated with the presence of other gases (for our applications, mainly O2 in case of NOx measures and mainly CH4 in case of H2 measures). As far as the acquisition electronics is concerned, the response of the sensors is often not compensated, thus increasing the risk of false alarms and missed detections. Complex data fusion techniques based on principal component analysis or fuzzy rules or sparse Gaussian process mixture models are proposed in literature, whose implementation in low-cost embedded devices for automotive is not possible. As an example, in [10], multiple gas sensors are acquired by a microcontroller, but the data fusion using fuzzy logic is implemented by a dedicated host PC connected through a serial bus. A first step for a rapid and cost-effective design of gas leak or gas emission monitoring systems is developing sensor models that enable fast but accurate system simulations. Such models should allow a precise analysis of the issues of practical implementation of the entire system, showing problems that could be discovered only through time-consuming testing and solved by iterating design activities with the real sensors.

0018-9456/$26.00 © 2010 IEEE

SAPONARA et al.: SENSOR MODELING, FUSION ALGORITHMS, AND MIXED-SIGNAL IC PROTOTYPING

Fig. 1.

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NOx generation versus temperature, reaction time duration, and O2 excess.

algorithm for the H2 case study starting from commercial offthe-shelf (COTS) available sensors. Section IV applies the same methodology to the NOx measurement. Section V introduces the ISIF platform designed in two configurations and implemented in 0.35-μm Bipolar CMOS DMOS (BCD) technology. Section VI presents the prototyping and measurement of the acquisition systems (H2 /NOx plus relevant compensating sensors and low-cost data fusion implementation) on the ISIF embedded device. Conclusions are drawn in Section VII. II. S ENSOR M ODELING AND H2 L EAK D ETECTION

Fig. 2. Processing chain of H2 -based transport system. For safety reasons, a networked monitoring of H2 gas leakage is needed.

A fast and accurate sensor modeling is also essential to develop effective data fusion algorithms that can be implemented in programmable embedded systems with limited resources in terms of computational power and memory, thus meeting automotive requirements for low power consumption and low cost. To address the aforementioned issues, this paper proposes a sensor modeling strategy for fast analysis of the sensing and acquisition system. The main target of the work is enabling a cost-effective design of fusion algorithms and the relevant prototyping implementation on a mixed-signal acquisition hardware platform (including a configurable analog front end, analog-to-digital converters (ADCs), on-chip digital processing, and communication resources). The proposed platform is programmable at software level, since it integrates a CPU core, but is also configurable at hardware level (both analog and digital parts), thus increasing reusability and flexibility versus the state of the art, where most of the acquisition systems are dedicated to a specific sensor [11], [12] or have a programmable controller core, but the hardware is not configurable. This work extends the conference paper [12] by addressing sensor modeling and novel fusion algorithms also for hydrogen leaks, by presenting the design of a mixed-signal programmable hardware platform for multisensor interfacing and signal conditioning, called Intelligent Sensor InterFace (ISIF), and by detailing the prototyping activity of the proposed NOx and H2 acquisition techniques on the ISIF platform. The rest of this paper is organized as follows. Sections II and III describe the sensor modeling activity and the data fusion

From the analysis of the state of the art, two COTS sensors were selected whose performances are good representatives of the available classes of H2 sensors: the FIGARO TGS6812 [13] and the TGS821 [14]. The first device can be used for the detection of large leaks of hydrogen in explosiveness warning systems, while the second one can be used for small H2 concentrations in leakage detection warning systems. Commercial sensors from other suppliers, e.g., Synkera [15], Kebaili [16], E2V, and others, are available, having performances comparable to FIGARO ones, and hence, the proposed approach keeps its validity also when applied to other selected commercial sensors. The TGS6812 is a catalytic resistive H2 sensor for the detection of concentrations up to 100% of LEL. Like other sensors on the market, the TGS6812 is sensitive also to hydrocarbon gases, mainly CH4 , which will be present in future gas-based vehicles or storage/distribution systems using both methane and hydrogen. The front-end reading requires the use of a Wheatstone bridge with dummy resistors of 1 kΩ. A potentiometer can be used to adjust the offset. The sensor has a linear output and is practically insensitive to variations of humidity and temperature. The sensitivities to H2 and CH4 are about 15/4000 and 15/5000 mV/ppm, respectively. The TGS821 is a tin dioxide (SnO2 ) semiconductor sensor which has low conductivity (i.e., high resistance R0 ) in clean air. In the presence of a detectable gas, the sensor’s conductivity increases (the sensor resistance RS decreases) depending on the gas concentration in the air. Table I shows the functions that determine the variation of output resistance (RS /R0 ) in relation to the gas concentration (H2 but also CH4 , CO, and C2 H6 O) and to environmental factors such as relative humidity (%RH) and temperature (T ). The functions in Table I, not provided in the data sheet of the sensor, have been obtained by curve fitting of experimental measurements on the sensor. Such

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TABLE I FIGARO TGS821 R ESPONSE L AWS

functions are used to build an accurate model that is the basis for fusion algorithm development and prototyping. Simulating models for different classes of sensors (gas sensors, accelerometers, gyro, . . .) can be found in literature, although most works mainly propose computational intensive physical models focusing on the design of optimized sensors [17]–[24]. Sensor models for fast system level analysis and design space exploration are provided in some cases by industry (e.g., [25]), but they often simulate typical conditions without considering degradation of performance due to noise or error sources or the interaction with the other system components. On the contrary, in our approach, we develop Simulink sensor models for fast system level analysis and top–down definition of data fusion algorithms and hardware architecture in a Matlab environment while still guaranteeing the required level of modeling accuracy. This is achieved by taking into account all main error sources relevant for the sensor and by building parametric models that can be bottom–up annotated from experimental measurements. Therefore, a meet-in-the-middle approach between top–down and bottom–up strategies is realized. Matlab/Simulink tool is chosen to ensure interoperability of the models in different designs and ease their assembly at system level with third party models. As a matter of fact, Matlab/Simulink is the most used modeling environment in system engineering. These models allow a sensitivity analysis highlighting potential sources of error and help to size the acquisition system architecture (hardware and software) and to select COTS components. Note that the same parametric model can be reused to represent different sensors belonging to the same sensor class (e.g., catalytic resistive sensors with different performance or sensitive to different gases). By configuring the parameters of the specific sensor, it is possible to obtain different dedicated models. When configuring the model’s parameters, the designer of the acquisition system can customize the tradeoff between accuracy and reduced design time. Indeed, for best accuracy, the parameter value should be obtained by experimental data after a characterization campaign on a sensor sample. For a quick model setup, the parameters can be extracted from the sensor data sheet if available, or default values from designer’s experience can be applied. A combination of the aforementioned methods is also possible, e.g., using experimental data for parameters having a key role for the objective of the acquisition system while default or data sheet values for the others. In this work, measured values are used to configure the sensitivity

parameters in the model (e.g., to the specific gas, H2 , to the main source of interference, CH4 , and to temperature), while data sheet typical values are used for the other parameters. As an example, Fig. 3 shows the system block diagram of the proposed TGS821 Simulink model which takes into account the following: 1) the dependence on environment conditions (temperature and humidity); 2) error sources such as saturation, noise, offset, and sensitivity errors; 3) timing (and frequency) response; and 4) main dependences on other gases (methane, ethanol, carbon oxide, and air). The response of the proposed model takes into account also the statistical uncertainty of the sensor offset and sensitivity due to technology spreading. The uncertainties are calculated each time by an initial script, using a function that extracts a random Gaussian number with mean equal to zero and variance equal to one-third of the range of uncertainty, so the whole range of uncertainty is in ±3σ. Note that, for the considered sensors, the declared technology spreading is not negligible, up to 10%, and hence, in practical applications, a precalibration phase is always applied. The developed model is then integrated in a test environment, simulating external condition change and interactions with nonidealities of other components: external temperature and humidity; concentration of other gases; and power supply ripple, tolerance, and errors of the circuitry needed to produce a voltage output (VRL in Fig. 3) proportional to the sensor resistance (Rsensor in Fig. 3) and, hence, to the hydrogen concentration according to VRL = VCH2 ·

RL Rsensor + RL

(1)

where VCH2 is a supply voltage of 10 V and RL amounts to 4 kΩ in this specific design. Equation (1) (and the corresponding blocks in Fig. 3) models the basic measuring circuit suggested in the TGS821 data sheet, implementing a resistance-to-voltage conversion. The obtained output signal VRL is then processed by analog circuitry before being converted into a digital form by an ADC: For the specific TGS821 device in the analog domain, the amplitude of VRL is scaled by a factor K = 0.2, and an offset VOFF = 1 V is subtracted. The signal is then low-pass ∗ is sent to the ADC. filtered, and the obtained output voltage VRL Figs. 4 and 5 show the sensor response versus H2 gas concentration (Fig. 4) and the dependence on temperature and humidity (Fig. 5) obtained running the model. To assess the model, we compared the simulated data with experimental measured data (different H2 concentrations at ambient temperature conditions). As discussed earlier, the TGS821 sensor was selected and modeled to be used in small-leakage detection warning systems, targeting a concentration range of up to 1000–2000 ppm. Above 2000 ppm, our model saturates, while in the data sheet, the RS /R0 ratio is further reduced below 0.1 when the concentration grows up to 5000 ppm. However, in the target concentration range, the modeled response shows a good match with both data sheet values and experimental data (e.g., in Fig. 4, the differences between simulated and measured results are within 1%). The same modeling approach described earlier has been followed for the TGS6812 H2 sensor and the relevant circuitry for output voltage reading (based on the Wheatstone bridge


Fig. 3.

Simulink block diagram of TGS821.

Fig. 4.

TGS821 sensor: Model response versus experimental data.

Fig. 5.

Sensitivity to temperature and relative humidity.

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Fig. 6. TGS6812 sensor: Model response versus experimental data.

configuration as suggested in [13]). The sensor model simulates its sensitivity characteristic, its dependence on other gases, the saturation effect, the input and output amplitude dynamic range, the timing and frequency response, internal noise sources, the sensitivity error, and the offset error. Being linear in response and with negligible dependence on temperature and humidity, the TGS6812 model is simpler than the TGS821 one. As an example, a Simulink diagram obtained with the TGS6812 model and compared to experimental data is shown in Fig. 6, representing the variation of the Wheatstone bridge output voltage VOUT versus the concentration of H2 .

Fig. 7. Data fusion for TGS6812, calibrated sensor.

Note that only plots highlighting the dependence on the main sources of errors for the considered applications have been included in this paper: temperature and relative humidity (see Figs. 5, 8, and 9 in Sections II and III), methane concentration (see Fig. 7 in Section III), and approximation error when the sensor fusion technique is implemented on a real prototyping platform with limited ADC size and arithmetic accuracy (see Fig. 18 in Section VI). The dependence on other sources of errors (such as saturation, noise, offset, etc.) has not been illustrated in dedicated figures, but it is supported by the models (see Fig. 3), and its effect can be observed in the included figures (e.g., the effect of saturation in Fig. 4 for H2 values

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higher than 2000 ppm for the TGS821 or the effect of offset at 0 ppm of H2 in Fig. 6 for the TGS6812 sensor). III. DATA F USION FOR H2 D ETECTION Although the developed sensor models take into account all relevant interferences, the fusion algorithms proposed in this work aim at removing the dependence of the target sensor response only on the main interfering variables for the considered application, as a tradeoff between complexity/cost and accuracy of the measure. In fact, the correction of the measured concentration w.r.t. all possible sources of interference, including the ones giving just a minor contribution, would result in a too complex and too costly fusion technique not suitable for automotive applications. Since the high sensitivity of TGS821 to changes in temperature and humidity (see Fig. 5) can corrupt the measured concentration of H2 , compensating sensors have to be used to correct the measure. Furthermore, if the vehicle or the storage/distribution system is using also methane, it is necessary to correct the measure obtained with the TGS821 and TGS6812 sensors by using a methane-specific sensor. Moreover, given the wide variation of sensitivity for the TGS6812 device [13], it is necessary to calibrate this sensor, determining by experimental data the values of offset and sensitivity (otherwise, calibration measuring errors in the range of thousands of parts per million can occur). For TGS821 compensation, a humidity sensor that is able to measure relative humidity levels in a wide range and preferably with a linear output characteristic is required. The selected commercial sensor is the SY HC1 [26] by RHOPOINT, a linear capacitive sensor that is able to measure %RH ranging from 5% to 95% with sensitivity of 0.6 pF/[%RH]. The effect of noise and the dependence on temperature and frequency described in [26] have been considered when modeling the humidity sensor. The used temperature sensor for thermal compensation of the TGS821 H2 and SY HC1 sensors is a simple PT100 resistor [27] featuring a good linearity and whose response (following the classic Callendar–Van Dusen equation) does not depend on humidity or gas concentration. This feature is important to reduce cross-correlation between the sensors involved in the compensating process. The PT100 sensor has been modeled considering the Callendar–Van Dusen law linking the output resistance to the temperature and taking into account errors due to offset, noise, and sensitivity gain. The last sensor used for compensation of both TGS6812 and TGS821 H2 sensors is the FIGARO TGS6810 [28], a methane sensor that can detect concentrations up to 100% of LEL (50 000 ppm for methane in air). The TGS6810 methane sensor is similar to the TGS6812 hydrogen sensor in terms of structure and model, except that TGS6810 is not influenced by H2 concentration. The dependence on temperature and humidity of this device is practically zero, thus avoiding cross-dependence with the other compensating sensors. Data fusion on TGS6812 sensor is required only if the vehicle (or the distribution/storage system) uses also methane. In this case, the TGS6810 sensor has to be used to correct the measure of hydrogen. The following formula shows the

Fig. 8.

Data fusion for TGS821, temperature variation.

output of the TGS6812 sensor as a function of the methane concentration: VOUT = SensH2 · H2 ppm + SensCH4 · CH4 ppm

(2)

from which we get the concentration of H2 H2 ppm =

VOUT − SensCH4 · CH4 ppm . SensH2

(3)

This is the formula used to derive the true concentration of hydrogen in the air. Fig. 7 shows, with simulations, an output obtained with the data fusion technique versus the case not using data fusion, after precalibration of the sensor. The response curves were obtained by simulating a fixed methane concentration of 5000 ppm and showing the detected concentration of hydrogen. To compensate by data fusion the TGS821 sensor, the outputs of temperature, humidity, and methane sensors are first converted into voltage signals. Then, for the three sensors, an electronic circuit adjusts the dynamics, eliminates the offsets, and applies the antialiasing filtering before sampling. The resulting signals are then passed on to the ADC. The bandwidths of the sensors are low, and consequently, it is possible to employ only a single ADC multiplexing the signals provided by the three sensors (see further details in Section VI). After sensor acquisition, the following equation is used, linking the change in resistance of the sensor output to the parameters measured by the other three sensors (temperature, methane, and humidity; the value of the latter is first thermally compensated using the same PT100 sensor): RS RS RS + (H2 ) + (RH) RS = R0 · R0 Air R 0 H2 R0 RH RS RS + (CH4 ) + (T ) (4) R0 CH4 R0 Temp where RS /R0 Air is the resistance ratio in air and (RS /R0 )(XX) represents the resistance variation caused by the interfering XX compound, by relative humidity or by temperature (see Table I). Fig. 8 shows the simulation results obtained with this algorithm (after sensor precalibration), for different H2 concentrations and different temperature operating points: At low temperatures, the correction of the measured


Fig. 9.

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Data fusion for TGS821, humidity variation. TABLE II I ONOTEC S ENSOR C HARACTERISTICS [29]

Fig. 10. Ionotec sensor model block diagram.

concentration is fundamental to avoid underestimations. Fig. 9 shows that measurements not taking into account the effect of humidity can be extremely inaccurate. IV. NOx S ENSING AND DATA F USION A LGORITHMS A. NOx Sensing and Modeling For NOx sensor selection, modeling and sizing of the sensor compensating technique, we followed the same approach described for H2 acquisition in Sections II and III. Measuring NOx emission concentration requires sensors that are able to work at the high temperatures of gas expelled after combustion in the ICE. The Ionotec NOx gas sensor in [29] has been preferred to other sensors on the market (e.g., [30]) due to its wider temperature range (up to 500 ◦ C). It is a ceramic sensor that can measure concentrations of NOx up to several thousands of parts per million and hence covers the required dynamic range (see Fig. 1). The main features of the device are reported in Table II. Fig. 10 shows the block diagram of the proposed Simulink NOx sensor model. The dependences on the temperature and on the presence of other substances, such as O2 , CO2 , and CH4 , are modeled, along with phenomena of saturation, noise, offset, and gain errors, and bandwidth limits. The formula describing the sensor output is

temperature (see Table II). The third term in (5) models the effects of the presence of XX compounds in addition to NOx (αXX is the sensitivity to a generic XX compound). The values of of f set and αNOx are derived from experimental measures, while the others are taken from the data sheet. The dynamics of the Ionotec device is considered comparable to the one of a first-order system, where the response time used for the model is 10 s (worst case from experimental measures). Interactions with other substances and with temperature are determined in the two subsystems in Fig. 10, by implementing the last two terms of (5). Simulations with the model in Fig. 10 show how the presence of each compound (other from NOx ) directly affects the sensor output. Given the role of O2 excess in NOx generation (see Section I) and considering also the absence of HC in hydrogen-based vehicles, mainly, the interference from O2 and temperature are relevant. Moreover, for all compounds in Table II, the αXX values are one order of magnitude lower than αNOx . B. NOx Sensor Compensation

(5)

The NOx sensor provides a measure influenced by other factors (particularly by temperature and presence of oxygen). Hence, it is necessary to correct the measurement by integrating in the system temperature and oxygen sensors. Several COTS temperature sensors were examined, and following similar considerations in Section III, a PT100 device was selected and modeled as it features a good linearity, is not influenced by gas concentration, and presents a wide measurement range, comparable with the operating temperature range of the NOx device. The Electrovac SO-A0-250 sensor [31] was chosen to have a measure of oxygen for its temperature range and its response law similar to the Ionotec one. According to (6) from the manufacturer, the signal provided by the device is a current IS which is a logarithmic function of the O2 concentration; the constant k is obtained from experimental tests O2 IS = −k · ln 1 − (6) 100

where αNOx is the sensor sensitivity to nitrogen oxides (according to a logarithmic law) and βT is the sensitivity to the

The oxygen range of measurement is 0.1%–25%, covering the required range in Fig. 1. A summary of its characteristics is

VOUT = of f set + αNOx · Log10 NOx + βT · (T − T0 ) + αXX · Log10 XX

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TABLE III OXYGEN S ENSOR C HARACTERISTICS [31]

shown in Table III; for our application, it is relevant that the O2 sensor response is temperature dependent. After selecting the compensating sensors, an algorithm of data fusion was then performed to eliminate the influence of temperature and oxygen from the measure of NOx concentration. For this purpose, the signals provided by the three sensors (NOx , temperature, and O2 ) have to be acquired. The outputs of both the temperature and the O2 sensors are converted into voltage signals. Then, for the three sensors, an electronic circuit adjusts the dynamics, eliminates the offsets, and applies the antialiasing filtering before sampling. The resulting signals are then passed on to the ADC. The bandwidths of the sensors are lower than 1 Hz, and the sampling frequency may be limited to a few hertz. Consequently, it is possible to employ only a single ADC multiplexing the signals provided by the three sensors (see Section VI). By elaborating the characteristics of the sensors and of the realized conditioning circuits, it is possible to derive the following equations that express the concentration of oxygen as a function of the temperature, and the concentration of NOx as a function of both O2 and temperature: VIN − 1 · 100 − KT · (T − T amb) O2 = − exp − Kr (7) ∗ V 1 IN NOx = 10∧ · − αO2 · Log10 O2 αNOx Kv − βT · (T − T0 ) − of f set

. (8)

Equation (7) is derived from (6) after adding a term for the dependence on the temperature (KT and T amb are the temperature-dependent parameters shown in Table III). VIN in (7) is the input signal of the ADC, generated by the conditioning circuit of the oxygen sensor. Kr is the product between the value k in (6) and the transresistive gain of the acquisition chain linking VIN at the ADC input to the IS output of the oxygen sensor. In addition, the NOx sensor output [see (5)] is elaborated by a conditioning circuit (having a gain Kv ), and ∗ in (8) that is the input signal of the ADC. the result is VIN Derived from (5), in (8), the estimation of NOx concentration is corrected taking into account the contributions of offset, temperature, and O2 , while other compounds [labeled XX in (5)] are not considered since their contribution is negligible. By means of these formulas, an estimation of the NOx concentration can be obtained. In fact, the concentration of oxygen can be corrected using the temperature measurement, and subsequently, the concentration of NOx can be determined using the concentration of oxygen previously corrected and the

Fig. 11.

Response with/without O2 compensation (for 500-ppm NOx ).

Fig. 12.

Response with/without T compensation (for 500-ppm NOx ).

measured temperature. The Simulink model that implements the data fusion was used to compare the measures obtained neglecting the interactions with the ones obtained by applying the described compensation algorithm. The results of a simulation assuming, as an example, 500 ppm of NOx , a temperature of T = T0 , and 5000 ppm of O2 are shown in Fig. 11. In the first test, the NOx concentration is calculated by means of (7) and (8), i.e., using in the calculation the information provided by the temperature sensor and by the oxygen sensor. In the second test, the measure is obtained without compensating the sensor output w.r.t. the presence of O2 . When neglecting the influence of oxygen, the measurement of NOx concentration is incorrect. Thus, it is necessary to use the measure of O2 concentration to eliminate the dependence. Fig. 12 shows the results of analogous tests referring to effects of temperature. When the measure does not take into account ΔT (20 ◦ C in these tests) w.r.t. the nominal value of temperature T0 , the measured NOx concentration presents an error greater than 300 ppm. A correct measurement of temperature is therefore fundamental to obtain a reliable measurement of NOx concentration. V. M IXED -S IGNAL ISIF To fast identify, trim, and verify, at experimental level, an architecture to interface and compensate a given sensor, a mixed-signal embedded hardware platform for ISIF has been developed by the University of Pisa in close collaboration with SensorDynamics AG [32], [33]. The IC, realized in 0.35-μm BCD technology with 3.3-V supply for the digital part and 5 V for the analog one, has been developed according to a platformbased design strategy [34], by assembling a set of analog,


Fig. 13. Architecture of the 8051-based ISIF.

digital, and software intellectual property (IP) modules in the same multichannel sensor interfacing chip. The architecture of the mixed-signal embedded system is shown in Fig. 13 and briefly described in this section. It is composed of an analog front end and a digital processing section with a joint test action group (JTAG) standard interface between the two signal domains. The basic idea behind the architecture in Fig. 13 is using a low-cost sensor and reducing to a minimum the analog signal processing, while compensating nonideality through digital signal conditioning, since digital circuitry can be easily designed and scaled in microelectronics technologies. The analog front end in Fig. 13 mainly accomplishes tasks of driving sensor’s electrodes (in the case of sensor requiring external excitations), through couples of thermometer-type digital-toanalog converters (DACs) and performing signal acquisition by means of SAR-type ADCs and programmable-gain operational amplifiers. It also provides a regulated power supply to the digital section. All modules are digitally controlled since gain coefficients, offset values, and reference voltages are set by means of dedicated registers accessed via the JTAG bridge by the digital processor. Hence, also the analog part is configurable, while the digital part is both hardware configurable and software programmable. The default hardware configuration plane is stored in an on-chip PROM. All nontrivial signal processing required for sensor conditioning, i.e., filtering, function generation, and demodulation, is performed by the digital section which also monitors system activity and handles communication with external devices. Both dedicated and general-purpose computing resources are available in the digital part to achieve a good tradeoff between power consumption and flexibility. The hardware DSP chain in Fig. 13 contains dedicated circuits for digital signal processing: finite-impulse response/infiniteimpulse response filters to remove noise/interference sources and a digital phase-locked loop (based on numerically controlled oscillators) for demodulating the sensor response and for function generation (e.g., sensor stimuli) based on the direct digital synthesis concept. General-purpose tasks are managed by CPU core provided in a configuration with on-chip program/data memories and standard parallel I/O plus universal asynchronous receiver/ transmitter (UART) and serial peripheral interface (SPI) interfaces for communication. In this system, the CPU core is in

379

charge of monitoring the DSP chain and managing communication/control flows among the mixed-signal part via JTAG, the DSP unit, the internal memories, and the external devices. This basic architecture has been implemented in silicon in two configurations where the main differences are the used CPU core and the amount of on-chip memory resources. The first configuration, more suited for a direct translation in real devices, respecting the bounded cost and limited computing and memory resources of the automotive field, is based on a power-optimized 8051-compliant core we first presented in [35] and [36]. The prototype chip implementing, in 0.35-μm silicon technology, the architecture in Fig. 13 with a configuration of 32-kb PROM and two 4-kb SRAM memories and an 8-bit 8051 core with timer/counter, UART, and SPI has an overall area of roughly 20 mm2 and works at 20-MHz clock frequency. The ADCs are sized for 10 b and a max sample rate of 100 kS/s. The second generation targets more powerful sensor conditioning systems; it is based on a 32-b SPARC V8 LEON2 CPU core and is more suited for fast sensor conditioning prototyping, or for applications requiring computation-intensive signal processing but with less bounded limits in terms of chip size and cost. Still realized in 0.35-μm BCD technology, this embedded platform (see architecture in Fig. 14) has an area of roughly 70 mm2 and enhances the first generation in Fig. 13 with the following: 1) four multistage configurable analog (instrumentation amp + filter) acquisition channels, two channels optimized for voltage and capacitive sensing, and two channels optimized for current sensing; 2) four 12-b SAR ADCs (max. 150 kS/s) and two 16-b sigma–delta ADCs (max. 15 kS/s); 3) six high-precision 12-b and six high-speed 10-b on-chip DACs; 4) a 32-b 20-MHz SPARC V8 fixed-point core (LEON2); 5) on-chip 32-kB EEPROM and 32-kB RAM; 6) UART/SPI interface plus two timer peripherals and 16-b GPIO. As further discussed in Section VI, for the target sensors of this work, the 8051-based ISIF embedded device in Fig. 13 is enough and ensures lower cost and size versus the chip in Fig. 14. Other mixed-signal platforms for sensor interfacing are available on the market, such as the Actel Fusion (AFS600) based on an ARM7 core and a single 12-b ADC [37], [38] or the Cypress PSOC CY8C featuring four ADCs and four DACs configurable from 6 to 14 b and based on 8-b CPU. With respect to the aforementioned COTS platforms, the proposed ISIF device is preferable in the 8051-based version when the application is more dedicated to the conditioning of a sensor class with limited size and cost budgets, while the LEON-based version is preferable versus the state of the art if high-precision ADCs or a powerful CPU is required. These conditions typically apply for fast setup of sensor conditioning hardware–software architectures or for acquisition systems with stringent requirements in terms of accuracy and computation capabilities. From a software and algorithmic point of view, the development flow of an application on the ISIF platform (both chip configurations) is integrated with the Simulink/Matlab flow in

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Fig. 14. Architecture and layout of the LEON2-based ISIF.

Fig. 15. ISIF design flow from Matlab models to prototyping.

Fig. 15. The starting point is therefore the realization of a Matlab/Simulink model of the whole system, which is made of a set of functional blocks allowing cosimulation of the sensor model with the analog/digital conditioning circuitry. As an example, Fig. 16 shows the block diagram of the Matlab/Simulink model for the configurable input analog chain of the ISIF device: It is made up of a configurable cascade of multiple stages alternating differential amplifiers (with programmable RC feedback network) and low-pass dedicated second-order Bessel filtering stages with programmable cutoff frequency. To each subblock in Fig. 16, a simulating model of the relevant circuitry is associated (detailing input–output transfer charac-

teristic plus main error sources such as saturation, offset, noise, temperature dependence, and frequency response). Using such configurable models at an early stage of the design, it is possible to run a number of simulations for identifying the critical parameters for the overall system performance and, hence, for correctly sizing the sensor signal processing circuit. A system exploration phase, based on simulations, design iterations, and functional block refinements, leads to a first partitioning of the system in analog and software-programmable digital building blocks. After architecture definition and hardware/software partitioning in the Matlab/Simulink environment, each block is modeled with the most appropriate description language, and electronic design automation tools or proper predesigned IPs to be reused are selected, configured, and assembled. Conventional flows [39], [40] are used for the lower level design phases (VHDL based for digital hardware, VHDL-AMS and Spice for analog circuitry, and C/C++ for software routines). The top–down platform-based design flow ends up with the prototyping phase, through which the whole system can be tested under practical operating conditions. This methodology represents a powerful strategy for rapid managing also of complex designs, owing to high reuse of concepts, architectures, blocks, and IPs among different projects. VI. S ENSOR ACQUISITION AND DATA F USION ON THE M IXED -S IGNAL ISIF A. Data Fusion Implementation on Embedded ISIF Platform for H2 Measurements As proved in Section III, data fusion algorithms are very important for measuring H2 concentration both in explosion warning systems (targeting with the TGS6812 sensor a dynamic


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Fig. 16. Block diagram of the Matlab/Simulink model for one input acquisition channel.

range up to 40 000 ppm and a measuring resolution of 100 ppm) and in H2 leak detection systems for early warning (targeting with the TGS821 sensor a dynamic range of few thousands of parts per million, e.g., 2000 ppm, but with a fine-grained resolution of 10 ppm). However, it must be noted that the data fusion algorithm, particularly the one for TGS821, is computationally complex. Beyond simple additions, subtractions, and products, some exponential, logarithm, and division operations must be performed to calculate the formulas described in Sections II and III. The use of a DSP with a floating-point unit is required, but this device is generally expensive compared to a traditional microcontroller, and it cannot be used for automotive applications, which are intended for a high-volume market. A solution relies on the use of a tabulated form of the needed nonlinear analog functions, based on precalculated lookup tables (LUTs) in the digital domain. Naturally, it is necessary to find a tradeoff between the number of levels of the tabulated functions and the related memory cost. A coarse function approximation can lead to unacceptable measuring errors; on the contrary, an approximation with a large number of levels and bits for their encoding can lead to a memory resource requirement too high for the limited budgets of embedded automotive hardware. When choosing the LUT-based approach, it is necessary to use a processing platform, such as the ISIF in Section V, with a PROM that contains the entire array describing the tabulated functions. In sizing the hardware platform, first of all, the number of ADC’s bits has to be defined. For the H2 data fusion algorithm, extensive simulations in the Matlab environment have been carried out trying to find the lower number of ADC data size and LUT levels that ensure at system level a quantization error below a “quality target” of 10 ppm for leak detection in the range of 0–2000 ppm and below 100 ppm for explosivity warning in a wider measuring range up to 40 000 ppm. As a result of this design exploration activity, a 10-b ADC was selected. It has to be noted that, from our analysis for humidity and for temperature sensors, an 8-b converter would be sufficient. Therefore, the SAR-type 10-b ADC of the ISIF platform can be used to acquire the required signals: Since the bandwidths of the sensors are on the order of hertz and the sampling rate of the ADC is up to 100 kS/s, all sensor inputs can be multiplexed on the same ADC. Moreover, to reduce glitch noise when switching between different inputs and to further reduce quantization noise (thus having an effective number of bits equal to the nominal target of 10 bits), the digital conversion can be performed at a higher frequency w.r.t. the required Nyquist rate, and then, the digital signal can be cleaned by decimating the samples. This way, the resolution obtained using the SAR ADC of the ISIF platform is as low as 2 ppm for TGS821

acquisitions and about 40 ppm for TGS6812 acquisitions. After sizing the ADC, the next choice to be made is related to the size of the tables that will describe the various nonlinear functions used for data fusion. For the example of TGS821, the functions that have to be tabulated are the following [see Table I and (1)].

1)

RS (CH4 ) = 1.89.4−log(CH4 ppm) . R0 CH4

RS (T emp) = 1.0188−T +12 − 1. R0 Temp KVCH2 3) RL /R0 ∗ + 1 − VOFF . VRL 2)

4)

RS −1 (. . .). R0 H2

Hereafter, we report, for each formula, the criteria for sizing the corresponding table, emerged after extensive simulations in the Matlab environment targeting an overall quantization error below 10 ppm. 1) The table that represents the function is sufficiently informative if it is implemented with 256 lines of input, divided in the range of concentration of CH4 from 0 to 4000 ppm, with outputs represented over 8 b, for a total memory usage of 256 B. 2) In order to get a satisfactory approximation, it is necessary to have a table with 256 entries, where outputs have an 8-b representation. This way, the memory requirement amounts to 256 B. 3) Since the function in the denominator contains the value of voltage corresponding to the measure of the hydrogen sensor, the entire function is tabled. To do this, a table with 256 entries is needed that provides output values represented on 12 b. 4) The function can be calculated in tabular form, using a table with 2048 input lines and outputs with 8-b precision. This table requires 2 kB of memory. Note that the low-cost implementation of the sensor fusion compensation for the TGS6812 device is easier since its dependence on temperature and humidity is negligible and the correlation with methane measurement is based on a linear law. Following the aforementioned considerations, the fusion technique for both H2 sensors, TGS8612 and TGS821, can be implemented on a mixed-signal embedded device, such as the 8051-based ISIF platform described in Section V (see Fig. 17).

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do preventive maintenance on vehicles as it cannot detect small leaks. In fact, having to set a minimum detectable concentration for the comparison, it will be impossible to detect concentrations below that threshold. Instead, a comparator system could be used for the detection of big leaks (for example, setting the threshold to one-fourth of LEL ∼ 10 000 ppm of H2 ), thus reducing the complexity of electronic circuits and also its cost.

Fig. 17. ISIF-based acquisition and compensation of H2 concentration.

Fig. 18. TGS821: LUT-based implementation of data fusion algorithm.

Indeed, the required hardware resources after the LUT-based strategy and bit sizing reported earlier are the following: 1) a 10-b ADC with four multiplexed channels (one for the hydrogen sensor, TGS6812 or TGS821, depending on the target dynamic range for concentration measurements, while the others are for methane compensation and, in case of the TGS821, for temperature and humidity compensation); 2) on-chip memory of roughly 3 kB for LUT-based implementation of complex processing functions; 3) an 8-b CPU of few million instructions per second which implements only digital sample decimation and signal control tasks at low repetition frequencies since the sensors have bandwidth of few hertz; 4) UART serial interface toward a host controller. Fig. 18 shows the results obtained after implementing data fusion for TGS821 on the ISIF platform (see Fig. 17). Both simulated data of the overall Matlab/Simulink model and experimental data (different H2 concentration points) are illustrated. From Fig. 18, it can be noted the effect of the representation of values on a limited number of bits and of the LUT-based realization of nonlinear equations. The use of LUTs and of a fixed-point arithmetic allows for the implementation of the algorithm in the low-complexity 8051-based ISIF embedded device, instead of using a floating-point DSP, and the introduced error with respect to the ideal response curve amounts to tens of parts per million when working in a range from 0 to 1000 ppm. Similar simulation and experimental analysis have been repeated after implementing data fusion for TGS6812 on the ISIF platform. In this case, the maximum error amounts to few hundreds of parts per million in a range up to 10 000 ppm, which is acceptable considering the application of this sensor. An alternative to the measurement system is the use of a comparatorbased warning system. This choice surely loses the ability to

B. Data Fusion Implementation on Embedded ISIF Platform for NOx Measurements For NOx sensor fusion, the same LUT-based approach described in Section VI-A is applied, starting from (8). First, we express NOx concentration in logarithmic scale (in decibels), thus avoiding the calculation of the exponential. Furthermore, the exponential in the calculation of O2 starting from the ADC input and the logarithm for O2 -dependent NOx concentration are calculated off-line, quantizing the variable on which each term depends and storing the corresponding results in LUTs implemented in the memory of the embedded hardware device. From Matlab simulations, it emerged that a good tradeoff between circuit complexity and performance (targeting an approximation and quantization error below 1% of measured range) is obtained when using tables, where the results of the exponential are stored, expressed on 8 b obtained by dividing the range of O2 into 256 levels. The result of the logarithm is stored in a second table that also contains, for each of the 256 possible input values, the result of the operation calculated offline delivered over 8 b. This way, it is possible to implement the data fusion by using a small portion of memory (about 512 B) and reducing considerably the required computational power. The simulations described in Section IV for NOx data fusion (e.g., 500-ppm NOx input in the presence of 5000 ppm of O2 and applying a temperature variation of 20 ◦ C) were repeated to test the effectiveness of the low-complexity quantized algorithm. The compensation algorithm that uses fixed-point 8-b data representation and the stored precalculated operations (instead of performing on-line calculations) has performances (see Fig. 19) fully comparable to the ones obtained in Section IV-B without the use of LUTs and with floating-point data arithmetic. The error committed is limited to few parts per million. Such results have been also confirmed by experimental data (e.g., the steady-state experimental NOx measure reported with a red dot in Fig. 19) obtained using the prototype platform whose block scheme is in Fig. 20. The same experiments shown in Fig. 19 for a 500-ppm NOx concentration have been repeated for other test cases (NOx input concentrations of 100, 300, 700, and 1000 ppm within the target range in Fig. 1) with errors for the prototyped acquisition system within 1%. Hence, it can be concluded that the algorithm complexity has been greatly reduced, without degrading the system performances and allowing the implementation of the entire system on a low-complexity embedded device like the one proposed in Section V (see Fig. 20): The 8-b CPU core (with 8051 instruction set) is enough to implement only simple processing functions at low frequencies (as the sensors have bandwidth of few hertz); the 10-b SAR-type ADC of the ISIF platform offers a higher precision than the minimum 8-b required. Since


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and making the measurement system more robust w.r.t. operating condition changes. The model has also been used for sizing the low-complexity implementation of the fusion algorithms. To this aim, a software-programmable mixed-signal embedded platform for intelligent sensor interfacing (ISIF) with limited complexity, suitable for automotive applications, has been proposed. The computational power required by the algorithms can be greatly reduced by using fixed-point arithmetic and small LUTs with the precalculated results of the nonlinear operations. The error introduced is acceptable for the target applications as confirmed by simulated and experimental analyses on the prototype acquisition systems. The proposed design flow is general and hence can be applied to the measurement of gas emissions in any propulsion scheme. However, when changing the application case (different propulsion schemes, engine type, and fuel), different sensors have to be selected and modeled, and different fusion techniques are required and have to be developed. The development of specific sensor models and fusion algorithms for all possible application cases of gas measurements in the automotive field is out of scope for this paper but will be addressed in future work exploiting the design flow presented here. Fig. 19. Response obtained with/without sensor fusion compensation (data fusion implemented on the low-complexity ISIF embedded platform).

ACKNOWLEDGMENT The authors would like to thank Dr. Giambastiani and Dr. Rocchi from SensorDynamics AG and also Giampa and Forconi from the University of Pisa for the discussions. R EFERENCES

Fig. 20. ISIF-based acquisition and compensation of NOx concentration.

the sensor bandwidths are low, the three channels for NOx , temperature, and oxygen acquisitions can be multiplexed on the same ADC, and a sampling frequency much higher than the Nyquist rate is used together with decimation in the digital domain to reduce conversion errors. Roughly 0.5 kB of on-chip PROM memory is required for implementing the LUTs used for off-line calculation of complex operations. VII. C ONCLUSION This paper has presented a modeling activity and data fusion technique for hydrogen leak warning systems and for measurement of nitrogen oxide concentration in exhaust gases of vehicles. The modeling activity has included nonlinearity and error sources of the system as a whole, i.e., taking into consideration the sensors and the entire processing chain. This approach enables a thorough system analysis, thus allowing a fine tuning of the critical parameters of the system. The model has also permitted the development of effective data fusion techniques to reduce the uncertainty on the measure, allowing the detection of gas concentration down to few parts per million

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Sergio Saponara received the Laurea (cum laude) and Ph.D. degrees in electronic engineering from the University of Pisa, Pisa, Italy, in 1999 and 2003, respectively. In 2002, he was a Marie Curie Research Fellow with the Interuniversity Microelectronics Centre, Leuven, Belgium. Since 2001, he has collaborated with Consorzio Pisa Ricerche, Pisa. He is currently a Senior Researcher with the University of Pisa, focusing on the field of electronic circuits and systems for telecom, multimedia, space, and automotive applications. He holds the Chair of electronic systems for automotive and automation at the Faculty of Engineering, University of Pisa. He is also a Research Associate with Consorzio Nazionale Interuniversitario per le Telecomunicazioni and Istituto Nazionale Fisica Nucleare. He has coauthored more than 100 scientific publications and is the holder of four patents. Dr. Saponara has served as a Guest Editor of special issues on international journals and as a program committee member of international conferences.

Esa Petri (S’09) received the M.Sc. degree in electronic engineering from the University of Pisa, Pisa, Italy, in 2003, where she is currently working toward the Ph.D. degree in electronics for automotive. Her research addresses hardware/software embedded system architectures and networking. From 2004 to 2005, she was with the European Space Research and Technology Centre, European Space Agency, Noordwijk, The Netherlands. Since 2006, she has collaborated with the Microelectronic Systems Division, Consorzio Pisa Ricerche, Pisa, on several projects of industrial relevance in the fields of aerospace and networking.

Luca Fanucci received the M.Sc. and Ph.D. degrees in electronic engineering from the University of Pisa, Pisa, Italy, in 1992 and 1996, respectively. From 1992 to 1996, he was a Research Fellow with the European Space Research and Technology Centre, European Space Agency, Noordwijk, The Netherlands. From 1996 to 2004, he was a Senior Researcher with the National Research Council (CNR), Pisa. He is currently a Professor of microelectronics with the University of Pisa. His research interests include VLSI architectures for integrated circuits and systems. He has coauthored more than 150 scientific publications and is the holder of more than ten patents. Dr. Fanucci was a Program Chair of IEEE Euromicro DSD 2008 and IEEE DATE Designer’s Forum.

Pierangelo Terreni received the Laurea degree in electronic engineering from the University of Pisa, Pisa, Italy, in 1973. He is currently a full Professor of electronics and the Dean of the Faculty of Engineering at the University of Pisa. He has been involved in research activities in VLSI design for many years. He worked on the design of real-time high-performance systems for digital signal processing; particularly, he participated in identifying, realizing, and testing a design methodology based on systolic arrays. Since the last years, he has been involved in the design of high-performance low-power digital systems and in the research of electronic systems for automotive and for renewable energy. He has been the national Coordinator of several research projects cosponsored by the Ministry of University and Research (MIUR) and by the National Research Council (CNR), and currently, he is the Coordinator of the Tuscany Region project “H2 Filiera Idrogeno.”