The Seamless Low-cost Development Platform LoRra for Model

based Systems Engineering

Sven Jacobitz and Xiaobo Liu-Henke

Ostfalia University of Applied Sciences, Salzdahlumer Str. 46/48, 38302 Wolfenbüttel, Germany

Keywords: Rapid Control Prototyping, Systems Engineering, Code Generation, Real-time Interface, Measurement and

Calibration, Low-cost, Scilab, Battery Management.

Abstract: This paper presents a seamless low-cost Rapid Control Prototyping (RCP) development platform, LoRra for

short, based on the open source software Scilab / Xcos. The model-based, verification-oriented RCP devel-

opment process is introduced to master the increasing system complexity in ever-shortening development

cycles. Within this process Model-in-the-loop (MiL)-, Software-in-the-Loop (SiL)-, and Hardware-in-the-

Loop (HiL)-simulations are performed for testing and optimization. Based on requirements derived from the

process, the concept of the LoRra platform is developed first. It contains model libraries, a code generator, a

real-time interface, real-time hardware and a human-machine interface for measurement and calibration tasks.

Subsequently, the design of each component will be discussed. Finally, a first validation and optimization of

the platform is carried out by using the state of charge estimation for lithium-ion batteries.

1 INTRODUCTION

Nowadays, a key challenge for innovative companies

is to develop even more complex products faster and

faster. In order to meet the constantly intensifying re-

quirements, more and more hard- and software is being

integrated in technical systems. Core of the resulting

embedded mechatronic systems are the embedded con-

trol units (ECU) with implemented intelligent func-

tions for signal processing and control. Due to the rap-

idly increasing amount of functionality as well as the

degree of networking, increasingly complex software

components are designed which interact strongly with

each other (Quantmeyer, 2013). As a result, software

and hardware designs often contain numerous errors

that need to be detected through intensive testing and

eliminated in time-consuming iteration loops. How-

ever, in order to meet the demand for a fast time to mar-

ket, the development and validation of embedded

mechatronic systems using an effective development

methodology is indispensable (Liu-Henke, 2005).

For this reason, the structured, model-based, veri-

fication-oriented Rapid Control Prototyping (RCP)

development process is used, which includes system

structuring and composition. Model-in-the-Loop

(MiL)-, Software-in-the-Loop (SiL)-, and Hardware-

in-the-Loop (HiL)-Simulations are carried out for

testing. In the automotive industry, this has estab-

lished itself in the development of ECUs (Staron,

2017). The methodology is characterized by the high

degree of consistency and automation, from model-

ling, model-based function design and automated

generation of source code to the automated imple-

mentation on real-time hardware. All process steps

mentioned are seamlessly executed in a fully auto-

mated CAE environment to minimize manual work

and resulting random errors. Currently, only cost-in-

tensive combinations of CAE tools and real-time

hardware, such as Matlab/Simulink with a dSPACE

system, support the development process described

above in a seamless way (Liu-Henke, 2014).

As part of the EU-funded research project Low-

Cost Rapid Control Prototyping System with Open

Source Platform for the Function Development of

Embedded Mechatronic Systems (LoCoRCP), the

seamless Low-Cost RCP-Development Platform,

LoRra for short, is being developed at Ostfalia. The

following paper presents the concept and design of

the integrated RCP platform. It will be applied for the

functional development of a battery management sys-

tem for verification and optimization.

The rest of the paper is structured as follows. Sec-

tion 2 describes the basic RCP-methodology, which

is provided by the platform. Afterwards, in section 3

Jacobitz, S. and Liu-Henke, X.

The Seamless Low-cost Development Platform LoRra for Model based Systems Engineering.

DOI: 10.5220/0008993500570064

In Proceedings of the 8th International Conference on Model-Driven Engineering and Software Development (MODELSWARD 2020), pages 57-64

ISBN: 978-989-758-400-8; ISSN: 2184-4348

the concept of the platform is introduced. The follow-

ing section 4 describes the design of each module of

the LoRra platform. For a first verification and opti-

mization, section 5 demonstrates the application of an

Extended Kalman Filter for State of Charge estima-

tion for Lithium-Ion batteries as part of a battery man-

agement system (BMS). The paper closes with a con-

clusion and a short outlook in section 6.

2 METHODOLOGY

As mentioned in the introduction, modern embedded

mechatronic systems are developed and validated us-

ing model-based, verification-oriented RCP method-

ology. After system structuring through modulariza-

tion and hierarchization, integration takes place by

using the mechatronic composition. Based on the

specifications and requirements, a model of the real

plant is created and analysed. This model serves as

the basis for functional synthesis. Using offline sim-

ulations, the designed algorithms are tested and opti-

mized (MiL). Once a sufficient state of functionality

has been reached, the automated generation of code

takes place, which in turn is tested and optimized in

offline simulations (SiL). With increasing software

quality, HiL-simulations are carried out. For this pur-

pose, the designed function is automatically inte-

grated into a real-time environment, compiled into an

executable program and transferred to target hard-

ware. Further tests and optimizations take place under

real-time conditions before the final implementation

and validation on the real system is done (Liu-Henke,

2005).

The presented RCP methodology is seamlessly

supported by an integrated computer aided engineer-

ing (CAE) development environment. For this pur-

pose, libraries for modelling are required. The analy-

sis and functional synthesis is supported by using

block diagrams. A code generator, transforms the

model into C code and then the function is automati-

cally implemented on a target hardware using a real-

time interface (RTI). Due to the high degree of auto-

mation and the model-based validation in early

phases of development, errors are systematically min-

imized.

3 CONCEPTION

The following section discusses the concept of the

LoRra platform to support the methodology outlined

in Section 2. First, the issues to be solved are concre-

tized by the problem description. From these, the con-

cept of the platform is derived.

3.1 Problem Description

As early as the mid-1990s tools for the integrated

model-based development of mechatronic systems

were published (Hanselmann, 1996). The combina-

tion of the CAE tool Matlab / Simulink with real-time

systems from dSPACE (see Figure 1 on the left) has

established itself as a quasi-standard in the automo-

tive industry (Beine, 2009).

A wide range of libraries are available from this

high-cost platform to support modelling and analysis

as well as functional synthesis. The Simulink Coder

supports the automated transformation of block dia-

grams into source code in a variety of ways as well as

for various target languages and systems. dSPACE's

RTI links the Simulink model to the interfaces of a

real-time hardware and automatically implements the

generated executable on a target hardware. Here, a

wide range of powerful real-time systems are availa-

ble. The program ControlDesk provides a human-ma-

chine interface (HMI) for interactive communication

in case of measurement and calibration tasks

(Schuette, 2005). The process sequence in combina-

tion with the described tool chain illustrated by the

middle of Figure 1.

Figure 1: High-Cost RCP process and principle Low-Cost

solution.

The costs associated with the purchase and opera-

tion of the presented tool chain are immense (Liu-

Henke, 2014). For this reason, various open-source

solutions with comparable functionalities have been

developed. Especially the CAE environment Scilab

with its graphical simulation tool Xcos (formerly Sci-

MODELSWARD 2020 - 8th International Conference on Model-Driven Engineering and Software Development

cos) comply with many functions of Matlab / Sim-

ulink (Jacobitz, 2018). In combination with a micro-

controller as real-time hardware, a low-cost develop-

ment process could be realized. However, there are

still various gaps in this process as shown by Figure

1. Single process steps, such as code generation, can

be realized up to a limited extent, but there is still a

lack of automation and consistency.

The simulation of dynamic systems with Xcos and

the generation of C code to accelerate the simulation

is described in detail by (Campbell, 2010). Already at

the beginning of the 2000s, approaches arose which

execute this generated code on a PC using the real-

time Linux system RTAI (Bucher, 2005), (Duma,

2009) and couple it to a real process via the interface

library COMEDI (Weichinger, 2011). Work that is

more recent deals with the execution of the program,

generated by Xcos, on a microcontroller (Skiba,

2015). Also the code itself has been improved. Thus,

(Grabmair, 2014) presented a toolbox that generates

C code for a microcontroller from specially imple-

mented Xcos blocks. Furthermore, the automated par-

allelization of the generated source code is being re-

searched (Reder, 2017).

Many of the above solutions are based on old ver-

sions of Scilab and are not under development any-

more. In addition, many partial solutions are available,

but the integration to a seamless process according to

Figure 1 is missing. For the current Scilab version 6,

the solutions presented are not compatible.

3.2 Conception of LoRra

In order to fill the gaps described in Section 3.1 and

meet the non-functional requirements, the functionality

is modularized, first. The LoRra model libraries are

used to support modelling in Xcos. The automatic gen-

eration of C code is performed by the LoRra-Code-

generator. Further processing for online simulation on

a microcontroller is possible with the LoRra-RTI. Fi-

nally, online experiments can be performed using the

LoRra-iGES graphical user interface. All mentioned

modules are integrated to fill the gaps shown in Figure

1 and build the seamless LoRra platform. Figure 2

summarizes the over-all concept of LoRra.

4 TOOL DESIGN

The following section describes in detail the design

of the modules introduced in section 3. As they are

essential, the focus will be on the LoRra Model librar-

ies, the LoRra-Code-generator, the LoRra-RTI and

the target hardware.

Figure 2: Concept of the LoRra-Platform.

4.1 Design of the LoRra Model

Libraries

The LoRra platform supports a large number of dif-

ferent technical domains as model libraries. In this

section, the modeling of a lithium-ion battery cell ac-

cording to (Quantmeyer, 2014a) is carried out in an

exemplary manner. This model will be used for the

application in section 5.

The behaviour of the lithium-ion battery depends

highly nonlinear on the current 



as well as on the

state of charge (), temperature  and the state of

health (). For this model, the influences of  and

 are neglected.

First, the  is determined on basis of the bal-

ance equation (Eq. 1). The determined charge is

scaled with the nominal capacity 



and the coulomb

efficiency 



  

















dt

(1)

The terminal voltage 



is calculated from an

equivalent circuit model (cf. Figure 3). It consists of

a voltage source representing the open circuit voltage

(), a series resistance and four RC modules to ap-

proximate the dynamics. The  as well as the pa-

rameters of resistors and capacitors depends highly

nonlinear on the . This nonlinearity poses a huge

challenge particularly to the identification.

Therefore, identification is done by using a elec-

trical impedance spectroscopy in the frequency do-

main at various SoC level. After that, the model has

been validated in time domain using a dynamic stress

test (DST).

The Seamless Low-cost Development Platform LoRra for Model based Systems Engineering

Figure 3: Equivalent circuit model of a battery

(Quantmeyer, 2014a).

4.2 Design of LoRra-Code-generator

As shown in section 2, the automated generation of

code from the model is an essential part of the RCP

process. The block diagram of the function is trans-

formed into equivalent, high-performance C code

without user intervention. This avoids random errors

caused by manual programming and saves develop-

ment time (Hanselmann, 1996).

Figure 4 illustrates the concept of the LoRra-

Code-generator. The Xcos model is divided into its

functional and topological description. The functional

description is available for each basic block in the

form of its algorithms. The topology, i.e. the connec-

tion of the blocks to the model logic, can be inter-

preted as a directed graph. A model transformation

compiler links both information to code fragments e.g.

for initialization, output and state calculation and

event calculation. These can be optimally used for

post-processing (e.g. for generating SiL- or HiL-sim-

ulations).

Figure 4: Concept of the LoRra-Code-generator.

4.2.1 Topological Description of the Model

As mentioned before, the topology of the Xcos model

is represented by a graph  



,,



.  is the set

of nodes (each node represents a block in the dia-

gram),  is the set of regular edges (continuous sig-

nals) and  is the set of event edges (time discrete

event impulses).

A node 



∈ can represent a basic block or a hi-

erarchy element. Hierarchy elements are called Super

Blocks and contain an independent dataflow graph.

The basic blocks Input and Output are used to link it

to the next higher level. Since the sequence of input

or output signals of a block is relevant for the calcu-

lation, an edge 



∈ or 



∈ must also contain in-

formation about the input / output port number in ad-

dition to the source and target nodes.

4.2.2 Functional Description of the Blocks

The functional behavior of each basic block can be

described as an extended nonlinear state space repre-

sentation:





 





,



,



,



,





(2)



,



,



,



,





(3)

Where  is the current simulation time, 



the vector

of continuous states,  the vector of time-discrete

states, 



the vector of input variables, 



the parame-

ter vector and 

the vector of output variables. Both,

the continuous and the time-discrete states can jump

when the block is activated by an event input. In ad-

dition, then the time of each output event impulse

(



) is calculated (Nikoukhah, 1996):









,



,



,



,



,





(4)









,



,



,



,



,





(5)









,



,



,



,



,





(6)

Here, 



is the event input vector. It contains both the

external event inputs of the block and internal events

(e.g. due to zero crossing). 



and 



are the values

of the states right after event activation. 



,



 and





are the states / inputs at the arrival of an event.

4.2.3 Model Transformation Compiler

The Model transformation compiler is the core of the

code generator and drives the process. This is done in

three steps:

- Pre-processing of the Xcos model

- Linking of functional and topological description

- Post-processing of the generated code fragments

During pre-processing, the Xcos data structure is first

transformed into the dataflow graph (cf. section 4.1.1).

MODELSWARD 2020 - 8th International Conference on Model-Driven Engineering and Software Development

It is then checked for validity. In particular, this con-

cerns the inclusion of unsupported basic blocks or al-

gebraic loops. To recognize algebraic loops, a modi-

fied depth-first search is performed to find cycles in

the dataflow graph. If the given model is considered

to be valid, the topology can be optimized, e.g. by re-

moving paths that are not used or by merging similar

structures into functional groups. Finally, starting

from each source node, a topological sorting of the

graph is performed to determine the correct calcula-

tion sequence.

To link topology and functional description, the

given algorithms for each node 



∈ are trans-

formed into C code by executing formal transfor-

mation rules. Taking into account the previously de-

termined calculation sequence, the code fragments

are thus joined up. This can be done for the model as

a whole (without considering hierarchy levels) or by

encapsulating functions while retaining the hierar-

chical structure. The post-processing of the generated

code is mainly done by associated modules (e.g. to

generate a SiL- or HiL-simulation).

4.3 Design of the LoRra-RTI

The RTI implements the model from the offline sim-

ulation automatically into a real-time environment for

HiL-simulations as described in section 2. The func-

tionality can be divided into two tasks. Firstly, the

Xcos model must be linked to the interfaces of the

target hardware (e.g. digital out, A/D converter,

PWM generation, etc.). This takes place on model

level in Xcos. In addition, the automated implemen-

tation of the model on the real-time hardware must be

realized.

4.3.1 Model to Hardware Interfaces

For linking the model with the hardware interfaces,

specially implemented Xcos basic blocks are used.

By the deposited functional description, correspond-

ing code for reading, scaling and processing of signals

is generated. Due to the modular concept of the code

generator, the peripheral interfaces or microcontroller

functions can easily be implemented in Xcos as a new

basic block.

Presently just a limited number of interfaces is

supported. Due to the modular, functional descrip-

tion, an extension will be easily possible. The config-

uration of the interfaces (like ports and frequencies)

is read from an XML file at runtime of the RTI and

can therefore be adapted without altering the RTI

blocks.

4.3.2 Implementation of the Model

In order to implement an Xcos model on real-time

hardware, the process illustrated by Figure 5 has to be

executed. The code fragments, generated by the code

generator, are assembled to the application software

by using code-templates and finally linked with a

basic software by the embedded code transformer.

The basic software includes, e.g., the real-time oper-

ating system (RTOS) and the hardware abstraction

layer. It is a component of the RTI and needs to be

adapted in view of the specific target hardware. In ad-

dition to optimize the generated source code, the Em-

bedded Code Transformer configures the RTOS, in-

tegrates memory-protection mechanisms, and effects

linking to driver- and function libraries. Use of a lay-

ered architecture having standardized interfaces ena-

bles flexible adaption to different microcontrollers as

real-time hardware.

Figure 5: Process for translating and programming by the

LoRra-RTI (Jacobitz, 2019).

Compiler and linker calls are completely auto-

mated. Subsequently, the generated executable pro-

gram file can be analysed in view of extracting, e.g.,

memory map information. These will be saved and

transmitted to the iGES interface for measurement

and calibration.

4.3.3 Structure of the Real-time

Environment

The real-time environment is structured in layers with

standardized interfaces. It contains the layers basic

and application software. Figure 6 illustrates the sim-

plified structure of the resulting software system.

The application software consists of hardware-in-

dependent components of the RTI (e.g. the XCP

server for processing commands for measurement

and calibration tasks) and one or more user-generated

Xcos

model

LoRra code generator

Start

End

code

templates and

basic software

LoRra embedded

code transformer

source

code

fragments

compiler and linker

programmer interface

executable

binaries

The Seamless Low-cost Development Platform LoRra for Model based Systems Engineering

applications. Usually, these applications are encapsu-

lated modules that do not share resources. If two ap-

plications use the same resource (e.g. memory or pe-

ripherals), access is controlled by memory protection

functions of the operating system such as mutex.

However, particular care must be taken here to ensure

that no deadlocks occur (e.g. by path coverage tests).

Figure 6: Software structure of the real-time environment.

The access to the basic software as well as the mi-

crocontroller takes place via standardized interfaces.

The CMSIS API standard allows the usage of multi-

tasking, memory protection and so on independently

of the specifically implemented RTOS. In addition to

the operating system, the basic software also includes

functions for simplified hardware access (Hardware

Abstraction Layer, HAL) and more complex drivers

such as a TCP/IP stack.

5 APPLICATION OF LoRra

For the verification, optimization and demonstration

of the LoRra platform, the seamless functional design

of a State of Charge (SoC) estimator as part of a bat-

tery management system (BMS) for LiFePO

cells

will be performed in this section. The design process

is carried out according to the methodology presented

in Section 2. First, the concept of the BMS is intro-

duced. After that, the modelling and synthesis is done

using the LoRra model libraries. Finally, the results

of MiL-, SiL- and HiL-simulations are discussed.

5.1 BMS

The battery system is a typical embedded control sys-

tem containing four LiFePO

batteries connected in

series, sensors, actuators and an information-pro-

cessing unit. The BMS consists of a central battery

management controller, which is used for high-order

algorithms such as SoC estimation, power prediction

or a safety concept, and decentralized cell modules on

each battery cell. The cell modules provide as well

acquisition of the terminal voltage and communica-

tion via CAN to the control unit as the local execution

of the load balancing.

An essential function of the BMS is the SoC esti-

mation. Since many other functions depend on the es-

timated SoC, only minor deviations of maximum

±1% may occur during operation (long-term behav-

iour). A further challenge is the determination of the

unknown SoC during initialization of the BMS (con-

vergence behaviour). The estimated SoC must reach

a stationary value within a short time (maximum 5s).

5.2 Modelling and Synthesis

To design the SoC estimator using the LoRra plat-

form, a sufficiently accurate model of the battery pack

is needed. The LoRra model library offers among oth-

ers the nonlinear battery model, introduced in section

4.1, which has already been identified and verified.

An Extended Kalman Filter (EKF) according to

(Quantmeyer, 2014b) is used for SoC-estimation be-

cause of the nonlinear system behaviour. Therefore

the battery model is transformed into state-space with

the state vector 

, consisting of the SoC and the over

voltages at the RC-elements (



), the current 



as in-

put and the terminal voltage 



as output.

The algorithm of the EKF consists of a correction

and a prediction step. First, the states predicted by the

last time step are corrected, using measurement data

and the error covariance. Then the states and error co-

variance for the next step are predicted by using the in-

puts. The filter is initialized at the correction step with

initial values for the states and the error covariance.

The covariance matrix of the measurement  is

determined using various measurements on the real

system with help of the LoRra-iGES and a subsequent

analysis of the noise. Finally, the covariance matrix

of the system 



was selected as a weighting matrix

with a compromise being made between stability of

the EKF and sufficiently fast convergence according

to (Liu-Henke, 2017).

5.3 MiL- / SiL-simulation

The designed EKF is now tested and optimized by

various offline simulations (MiL) in the LoRra plat-

form. For the tests, the battery starts with 99 % 

and the EKF is initialized with  = 80 % to test as

well the convergence as the stationary behaviour. Af-

ter a brief idling period the battery pack is subjected

to the dynamic stress test as already used for validat-

ing the battery model.

CMSIS-Driver

Microcontroller

Complex

drivers

Hardware

Abstraction

Layer

CMSIS-Wrapper

RTOS …

application software

XCP

Server

Appli-

cation 1

Appli-

cation 2

…

basic software

MODELSWARD 2020 - 8th International Conference on Model-Driven Engineering and Software Development

After a sufficient quality has been achieved, code

is generated from the EKF model using the LoRra-

Code-generator. The generated code is then further

tested and optimized using SiL-simulations in the

LoRra test environment.

5.4 HiL-simulation

After successful SiL-simulations, the EKF is inte-

grated into the real-time environment by the LoRra-

RTI, in order to perform HiL-simulations. For the

online-simulations, a special HiL-test-rig is used. It

measures the terminal voltage as well as the current,

using an AD-converter. The cell modules are con-

nected via CAN. A safety circuit is actuated by digital

outputs. In addition, an electronic load- / source mod-

ule is driven via CAN.

Figure 7 shows the measurement results, recorded

by LoRra-iGES. The diagram shows the SoC, the de-

viation between estimated and calculated SoC, the

terminal voltage, and the current profile. In the left

part you see clearly the rapid convergence of the fil-

ter. After that, the right part shows that the deviation

of the estimation is very small. Also, the measured

and estimated terminal voltages matches with high

accuracy.

Figure 7: Measurement results from the HiL-simulation.

024135

100

110

time in s

SoC in %

024135

-0.1

0.1

-0.05

0.05

time in s

deviation in %

024135

13.5

time in s

voltage in V

024135

-20

-10

time in s

current in A

02040608010 30 50 70 90

100

time in min

SoC in %

02040608010 30 50 70 90

-0.1

0.1

-0.05

0.05

time in min

deviation in %

02040608010 30 50 70 90

12.5

13.5

time in min

voltage in V

02040608010 30 50 70 90

-20

-10

time in min

current in A

measurement

calculation

measurement

estimation

The Seamless Low-cost Development Platform LoRra for Model based Systems Engineering

6 CONCLUSION AND OUTLOOK

In this paper, a seamless and integrated low-cost rapid

control prototyping development platform (LoRra for

short) based on the open source software Scilab was

presented. The model-based, verification-oriented

RCP development process, starting with the LoRra

model libraries, the automated generation of code with

the LoRra-Code-generator and its implementation on a

microcontroller as real-time hardware using the LoRra-

RTI, can thus be performed highly automated in a sin-

gle low-cost software environment. The three process

steps Model-in-the-Loop, Software-in-the-Loop and

Hardware-in-the-Loop are supported for testing and

verification.

Further work deals with the implementation of a

graphical user interface for measurement and calibra-

tion tasks. In addition, the development of a test field

for interconnected autonomous guided vehicles for

further verification is being forced. For this purpose,

it is planned to add functions from the IoT, Industry

4.0 and Smart Home areas to the LoRra-RTI.

ACKNOWLEDGEMENT

The results presented were obtained in the context of

the project LoCoRCP that is funded by the EFRE

Fund of the European Union (grant number ZW 6-

85003460). Responsibility for the con-tent of this pa-

per lies with the authors.

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